diff --git a/bin/generate.py b/bin/generate.py index d1e030a2e9..a79786109a 100644 --- a/bin/generate.py +++ b/bin/generate.py @@ -67,7 +67,7 @@ def generate_transforms_conf(lookups, TEMPLATE_PATH, OUTPUT_PATH): utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('transforms.j2') output_path = path.join(OUTPUT_PATH, 'default/transforms.conf') output = template.render(lookups=sorted_lookups, time=utc_time) @@ -83,7 +83,7 @@ def generate_collections_conf(lookups, TEMPLATE_PATH, OUTPUT_PATH): utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('collections.j2') output_path = path.join(OUTPUT_PATH, 'default/collections.conf') output = template.render(lookups=sorted_lookups, time=utc_time) @@ -102,7 +102,7 @@ def generate_savedsearches_conf(detections, deployments, TEMPLATE_PATH, OUTPUT_P utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) j2_env.filters['custom_jinja2_enrichment_filter'] = custom_jinja2_enrichment_filter template = j2_env.get_template('savedsearches.j2') output_path = path.join(OUTPUT_PATH, 'default/savedsearches.conf') @@ -117,7 +117,7 @@ def generate_analytic_story_conf(stories, detections, TEMPLATE_PATH, OUTPUT_PATH utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('analytic_stories.j2') output_path = path.join(OUTPUT_PATH, 'default/analytic_stories.conf') output = template.render(stories=stories, time=utc_time) @@ -130,7 +130,7 @@ def generate_use_case_library_conf(stories, detections, TEMPLATE_PATH, OUTPUT_PA utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('use_case_library.j2') output_path = path.join(OUTPUT_PATH, 'default/use_case_library.conf') output = template.render(stories=stories, detections=detections, @@ -155,7 +155,7 @@ def generate_macros_conf(macros, detections, TEMPLATE_PATH, OUTPUT_PATH): utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('macros.j2') output_path = path.join(OUTPUT_PATH, 'default/macros.conf') output = template.render(macros=all_macros, time=utc_time) @@ -175,7 +175,7 @@ def generate_workbench_panels(response_tasks, stories, TEMPLATE_PATH, OUTPUT_PAT response_task['lowercase_name'] = response_file_name workbench_panel_objects.append(response_task) j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('panel.j2') file_path = "default/data/ui/panels/workbench_panel_" + response_file_name_xml output_path = path.join(OUTPUT_PATH, file_path) @@ -187,14 +187,14 @@ def generate_workbench_panels(response_tasks, stories, TEMPLATE_PATH, OUTPUT_PAT f.write(output) j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('es_investigations.j2') output_path = path.join(OUTPUT_PATH, 'default/es_investigations.conf') output = template.render(response_tasks=workbench_panel_objects, stories=stories) with open(output_path, 'w', encoding="utf-8") as f: f.write(output) j2_env = Environment(loader=FileSystemLoader(TEMPLATE_PATH), - trim_blocks=True, autoescape=True) + trim_blocks=True) template = j2_env.get_template('workflow_actions.j2') output_path = path.join(OUTPUT_PATH, 'default/workflow_actions.conf') output = template.render(response_tasks=workbench_panel_objects) @@ -603,7 +603,6 @@ def get_objects(REPO_PATH, OUTPUT_PATH, PRODUCT, VERBOSE): def main(REPO_PATH, OUTPUT_PATH, PRODUCT, VERBOSE): global_product = PRODUCT - select_autoescape(default_for_string=True, default=True) TEMPLATE_PATH = path.join(REPO_PATH, 'bin/jinja2_templates') objects = get_objects(REPO_PATH, OUTPUT_PATH, PRODUCT, VERBOSE) diff --git a/dist/escu/default/analytic_stories.conf b/dist/escu/default/analytic_stories.conf index 8aff80e9ad..cec834693d 100644 --- a/dist/escu/default/analytic_stories.conf +++ b/dist/escu/default/analytic_stories.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security_content -# On Date: 2021-08-20T22:00:45 UTC +# On Date: 2021-08-27T14:05:22 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -21,10 +21,10 @@ support_searches = ["ESCU - Previously Seen AWS Cross Account Activity"] data_models = [] providing_technologies = none description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. -narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ +narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ This Analytic Story includes searches that will help you monitor your AWS CloudTrail logs for evidence of suspicious cross-account activity. For example, while accessing multiple AWS accounts and roles may be perfectly valid behavior, it may be suspicious when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [AWS IAM Privilege Escalation] category = Cloud Security @@ -42,7 +42,7 @@ providing_technologies = none description = This analytic story contains detections that query your AWS Cloudtrail for activities related to privilege escalation. narrative = Amazon Web Services provides a neat feature called Identity and Access Management (IAM) that enables organizations to manage various AWS services and resources in a secure way. All IAM users have roles, groups and policies associated with them which governs and sets permissions to allow a user to access specific restrictions.\ However, if these IAM policies are misconfigured and have specific combinations of weak permissions; it can allow attackers to escalate their privileges and further compromise the organization. Rhino Security Labs have published comprehensive blogs detailing various AWS Escalation methods. By using this as an inspiration, Splunk’s research team wants to highlight how these attack vectors look in AWS Cloudtrail logs and provide you with detection queries to uncover these potentially malicious events via this Analytic Story. \ -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [AWS Network ACL Activity] category = Cloud Security @@ -59,7 +59,7 @@ data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [AWS Security Hub Alerts] category = Cloud Security @@ -76,7 +76,7 @@ data_models = [] providing_technologies = none description = This story is focused around detecting Security Hub alerts generated from AWS narrative = AWS Security Hub collects and consolidates findings from AWS security services enabled in your environment, such as intrusion detection findings from Amazon GuardDuty, vulnerability scans from Amazon Inspector, S3 bucket policy findings from Amazon Macie, publicly accessible and cross-account resources from IAM Access Analyzer, and resources lacking WAF coverage from AWS Firewall Manager. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [AWS User Monitoring] category = Cloud Security @@ -91,12 +91,12 @@ investigative_searches = ["ESCU - Get Notable History - Response Task", "ESCU - support_searches = ["ESCU - Baseline of API Calls per User ARN", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Create a list of approved AWS service accounts", "ESCU - Previously seen API call per user roles in CloudTrail"] data_models = [] providing_technologies = none -description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. -narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ +description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. +narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ Fortunately, you can leverage Amazon Web Services (AWS) CloudTrail--a tool that helps you enable governance, compliance, and risk auditing of your AWS account--to give you increased visibility into your user and resource activity by recording AWS Management Console actions and API calls. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred.\ The detection searches in this Analytic Story are designed to help you uncover AWS API activities from users not listed in the identity table, as well as similar activities from disabled accounts. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Active Directory Password Spraying] category = Adversary Tactics @@ -115,7 +115,7 @@ description = Monitor for activities and techniques associated with Password Spr narrative = In a password spraying attack, adversaries leverage one or a small list of commonly used / popular passwords against a large volume of usernames to acquire valid account credentials. Unlike a Brute Force attack that targets a specific user or small group of users with a large number of passwords, password spraying follows the opposite aproach and increases the chances of obtaining valid credentials while avoiding account lockouts. This allows adversaries to remain undetected if the target organization does not have the proper monitoring and detection controls in place.\ Password Spraying can be leveraged by adversaries across different stages in an attack. It can be used to obtain an iniial access to an environment but can also be used to escalate privileges when access has been already achieved. In some scenarios, this technique capitalizes on a security policy most organizations implement, password rotation. As enterprise users change their passwords, it is possible some pick predictable, seasonal passwords such as `$CompanyNameWinter`, `Summer2021`, etc.\ Specifically, this Analytic Story is focused on detecting possible Password Spraying attacks against Active Directory environments leveraging Windows Event Logs in the `Account Logon` and `Logon/Logoff` Advanced Audit Policy categories. It presents 9 detection analytics which can aid defenders in identifyng instances where one source user, source host or source process attempts to authenticate against a target or targets using a high, unsual, number of unique users. A user, host or process attempting to authenticate with multiple users is not common behavior for legitimate systems and should be monitored by security teams. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, multi-user systems and missconfigured systems. These should be easily spotted when first implementing the detection and addded to an allow list or lookup table. The presented detections can also be used in Threat Hunting exercises. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Apache Struts Vulnerability] category = Vulnerability @@ -131,21 +131,21 @@ support_searches = [] data_models = ["Endpoint", "Web"] providing_technologies = none description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. -narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's <a href=https://www.usatoday.com/story/tech/2017/09/07/nations-biggest-hacks-and-data-breaches-millions/644311001/> 5th largest data breach</a>. The target, credit giant Equifax, <a href=https://money.cnn.com/2017/09/16/technology/equifax-breach-security-hole/index.html>told investigators</a> that it had become aware of the vulnerability two months before the attack. \ +narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ This Analytic Story contains two different searches that help to identify activity that may be related to this issue. The first search looks for characteristics of the `Content-Type` header consistent with attempts to exploit the vulnerability. This should be a relatively pertinent indicator, as the `Content-Type` header is generally consistent and does not have a large degree of variation.\ The second search looks for the execution of various commands typically entered on the command shell when an attacker first lands on a system. These commands are not generally executed on web servers during the course of day-to-day operation, but they may be used when the system is undergoing maintenance or troubleshooting.\ First, it is helpful is to understand how often the notable event is generated, as well as the commonalities in some of these events. This may help determine whether this is a common occurrence that is of a lesser concern or a rare event that may require more extensive investigation. It can also help to understand whether the issue is restricted to a single user or system or is broader in scope.\ When looking at the target of the behavior illustrated by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to see what other events involving the target have occurred in the recent past. This can help tie different events together and give further situational awareness regarding the target.\ Various types of information for external systems should be reviewed and (potentially) collected if the incident is, indeed, judged to be malicious. Information like this can be useful in generating your own threat intelligence to create alerts in the future.\ -Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ +Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ Gathering various data elements on the system of interest can sometimes help quickly determine that something suspicious may be happening. Some of these items include determining who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ hen a specific service or application is targeted, it is often helpful to know the associated version to help determine whether or not it is vulnerable to a specific exploit.\ hen it is suspected there is an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ In the event that a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that have the file open, what processes created and/or modified the file, and the number of systems that may have this file can help to determine if the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes quickly help determine whether it is malicious in nature.\ Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if `svchost.exe` is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. Similarly, if the process itself seems legitimate, but the parent process is running from the temporary browser cache, there may be activity initiated via a compromised website the user visited.\ It can also be very helpful to examine various behaviors of the process of interest or the parent of the process that is of interest. For example, if it turns out that the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might also be worth further scrutiny. If a process is suspect, reviewing the network connections made around the time of the event and/or if the process spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Asset Tracking] category = Best Practices @@ -162,7 +162,7 @@ data_models = ["Network_Sessions"] providing_technologies = none description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. narrative = This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [BITS Jobs] category = Adversary Tactics @@ -179,7 +179,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Adversaries may abuse BITS jobs to persistently execute or clean up after malicious payloads. narrative = Windows Background Intelligent Transfer Service (BITS) is a low-bandwidth, asynchronous file transfer mechanism exposed through Component Object Model (COM). BITS is commonly used by updaters, messengers, and other applications preferred to operate in the background (using available idle bandwidth) without interrupting other networked applications. File transfer tasks are implemented as BITS jobs, which contain a queue of one or more file operations. The interface to create and manage BITS jobs is accessible through PowerShell and the BITSAdmin tool. Adversaries may abuse BITS to download, execute, and even clean up after running malicious code. BITS tasks are self-contained in the BITS job database, without new files or registry modifications, and often permitted by host firewalls. BITS enabled execution may also enable persistence by creating long-standing jobs (the default maximum lifetime is 90 days and extendable) or invoking an arbitrary program when a job completes or errors (including after system reboots). -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Baron Samedit CVE-2021-3156] category = Adversary Tactics @@ -195,8 +195,8 @@ support_searches = [] data_models = [] providing_technologies = none description = Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Research Team, this vulnerability has been found to affect sudo across multiple Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and prior). As this vulnerability was committed to code in July 2011, there will be many distributions affected. Successful exploitation of this vulnerability allows any unprivileged user to gain root privileges on the vulnerable host. -narrative = A non-privledged user is able to execute the sudoedit command to trigger a buffer overflow. After the successful buffer overflow, they are then able to gain root privileges on the affected host. The conditions needed to be run are a trailing "\" along with shell and edit flags. Monitoring the /var/log directory on Linux hosts using the Splunk Universal Forwarder will allow you to pick up this behavior when using the provided detection. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = A non-privledged user is able to execute the sudoedit command to trigger a buffer overflow. After the successful buffer overflow, they are then able to gain root privileges on the affected host. The conditions needed to be run are a trailing "\" along with shell and edit flags. Monitoring the /var/log directory on Linux hosts using the Splunk Universal Forwarder will allow you to pick up this behavior when using the provided detection. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Brand Monitoring] category = Abuse @@ -212,10 +212,10 @@ support_searches = ["ESCU - DNSTwist Domain Names"] data_models = ["Email", "Endpoint", "Network_Resolution", "Web"] providing_technologies = none description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. -narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible&#51;phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ +narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ Notable events will include IP addresses, URLs, and user data. Drilling down can provide you with even more actionable intelligence, including likely geographic information, contextual searches to help you scope the problem, and investigative searches. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Clop Ransomware] category = Malware @@ -232,7 +232,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the Clop ransomware, including looking for file writes associated with Clope, encrypting network shares, deleting and resizing shadow volume storage, registry key modification, deleting of security logs, and more. narrative = Clop ransomware campaigns targeting healthcare and other vertical sectors, involve the use of ransomware payloads along with exfiltration of data per HHS bulletin. Malicious actors demand payment for ransome of data and threaten deletion and exposure of exfiltrated data. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Cloud Cryptomining] category = Cloud Security @@ -248,11 +248,11 @@ support_searches = ["ESCU - Baseline Of Cloud Instances Destroyed", "ESCU - Base data_models = ["Change"] providing_technologies = none description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. -narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ -Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ +narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ +Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ When malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ This Analytic Story is focused on detecting suspicious new instances in your cloud environment to help prevent cryptominers from gaining a foothold. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Cloud Federated Credential Abuse] category = Cloud Security @@ -267,9 +267,9 @@ investigative_searches = [] support_searches = [] data_models = ["Endpoint"] providing_technologies = none -description = This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. +description = This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. narrative = This story is composed of detection searches based on endpoint that addresses the use of Mimikatz, Escalation of Privileges and Abnormal processes that may indicate the extraction of Federated directory objects such as passwords, Oauth2 tokens, certificates and keys. Cloud environment (AWS, Azure) related events are also addressed in specific cloud environment detection searches. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Cobalt Strike] category = Adversary Tactics @@ -295,7 +295,7 @@ With that, new detections were generated focused on these spawnto processes spaw - Does the spawnto_ value make network connections?\ - Is it normal for spawnto_ value to load jscript, vbscript, Amsi.dll, and clr.dll?\ While investigating a detection related to this Analytic Story, keep in mind the parent process, process path, and any file modifications that may occur. Tuning may need to occur to remove any false positives. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [ColdRoot MacOS RAT] category = Malware @@ -311,10 +311,10 @@ support_searches = [] data_models = [] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. -narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before&#151;a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ -This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ +narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ +This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ Searches in this Analytic Story leverage the capabilities of OSquery to address ColdRoot detection from several different angles, such as looking for the existence of associated files and processes, and monitoring for signs of an installed keylogger. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Collection and Staging] category = Adversary Tactics @@ -333,7 +333,7 @@ description = Monitor for and investigate activities--such as suspicious writes narrative = A common adversary goal is to identify and exfiltrate data of value from a target organization. This data may include email conversations and addresses, confidential company information, links to network design/infrastructure, important dates, and so on.\ Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ Use the searches to detect and monitor suspicious behavior related to these activities. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Command and Control] category = Adversary Tactics @@ -351,7 +351,7 @@ providing_technologies = none description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. narrative = Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Container Implantation Monitoring and Investigation] category = Cloud Security @@ -366,9 +366,9 @@ investigative_searches = [] support_searches = [] data_models = [] providing_technologies = none -description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. +description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. narrative = Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Credential Dumping] category = Adversary Tactics @@ -377,17 +377,17 @@ modification_date = 2020-02-04 id = 854d78bf-d0e2-4f4e-b05c-640905f86d7a version = 3 reference = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] -detection_searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Creation of lsass Dump with Taskmgr - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Dump LSASS via procdump - Rule", "ESCU - Dump LSASS via procdump Rename - Rule", "ESCU - Extract SAM from Registry - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations - Rule", "ESCU - Investigate Pass the Hash Attempts - Rule", "ESCU - Investigate Pass the Ticket Attempts - Rule", "ESCU - Investigate Previous Unseen User - Rule", "ESCU - Ntdsutil Export NTDS - Rule", "ESCU - SAM Database File Access Attempt - Rule", "ESCU - SecretDumps Offline NTDS Dumping Tool - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule"] -mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation", "Installation"], "mitre_attack": ["T1003.001", "T1003.002", "T1003.003", "T1059.001"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} +detection_searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Creation of lsass Dump with Taskmgr - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Dump LSASS via procdump - Rule", "ESCU - Dump LSASS via procdump Rename - Rule", "ESCU - Esentutl SAM Copy - Rule", "ESCU - Extract SAM from Registry - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations - Rule", "ESCU - Investigate Pass the Hash Attempts - Rule", "ESCU - Investigate Pass the Ticket Attempts - Rule", "ESCU - Investigate Previous Unseen User - Rule", "ESCU - Ntdsutil Export NTDS - Rule", "ESCU - SAM Database File Access Attempt - Rule", "ESCU - SecretDumps Offline NTDS Dumping Tool - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule"] +mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation", "Installation", "Lateral Movement", "Privilege Escalation"], "mitre_attack": ["T1003.001", "T1003.002", "T1003.003", "T1059.001"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} investigative_searches = ["ESCU - Investigate Failed Logins for Multiple Destinations - Response Task", "ESCU - Investigate Pass the Hash Attempts - Response Task", "ESCU - Investigate Pass the Ticket Attempts - Response Task", "ESCU - Investigate Previous Unseen User - Response Task"] support_searches = [] data_models = ["Authentication", "Endpoint"] providing_technologies = none description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. -narrative = Credential dumping&#151;gathering credentials from a target system, often hashed or encrypted&#151;is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ +narrative = Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ The detection searches in this Analytic Story monitor access to the Local Security Authority Subsystem Service (LSASS) process, the usage of shadowcopies for credential dumping and some other techniques for credential dumping. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [DHS Report TA18-074A] category = Malware @@ -404,10 +404,10 @@ data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. narrative = The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ -There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic data&#1151;all of which could confer advantages in various arenas. They may also target critical infrastructure. \ +There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ One joint Technical Alert (TA) issued by the Department of Homeland and the FBI in mid-March of 2018 attributed some cyber activity targeting utility infrastructure to operatives sponsored by the Russian government. The hackers executed spearfishing attacks, installed malware, employed watering-hole domains, and more. While they caused no physical damage, the attacks provoked fears that a nation-state could turn off water, redirect power, or compromise a nuclear power plant.\ Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify the network configuration), suspicious registry modifications, and many more--may all be events you may wish to investigate further. While the use of these technique may be an indication that a nation-state actor is attempting to compromise your environment, it is important to note that these techniques are often employed by other groups, as well. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [DNS Amplification Attacks] category = Abuse @@ -422,10 +422,10 @@ investigative_searches = ["ESCU - Get Notable History - Response Task"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none -description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. -narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ -The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. +narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ +The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [DNS Hijacking] category = Adversary Tactics @@ -443,14 +443,14 @@ providing_technologies = none description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. narrative = Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ -On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity and Infrastructure Security Agency (CISA) raised awareness of some high-profile DNS hijacking attacks against infrastructure, both in the United States and abroad. It issued Emergency Directive 19-01 (see https://cyber.dhs.gov/ed/19-01/), which summarized the activity and required government agencies to take the following four actions, all within 10 days: \ +On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity and Infrastructure Security Agency (CISA) raised awareness of some high-profile DNS hijacking attacks against infrastructure, both in the United States and abroad. It issued Emergency Directive 19-01 (see https://cyber.dhs.gov/ed/19-01/), which summarized the activity and required government agencies to take the following four actions, all within 10 days: \ 1. For all .gov or other agency-managed domains, audit public DNS records on all authoritative and secondary DNS servers, verify that they resolve to the intended location or report them to CISA.\ -1. Update the passwords for all accounts on systems that can make changes to each agency 2019's DNS records.\ -1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency's 2019 DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ +1. Update the passwords for all accounts on systems that can make changes to each agency 2019's DNS records.\ +1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency's 2019 DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ 1. CISA will begin regular delivery of newly added certificates to Certificate Transparency (CT) logs for agency domains via the Cyber Hygiene service. Upon receipt, agencies must immediately begin monitoring CT log data for certificates issued that they did not request. If an agency confirms that a certificate was unauthorized, it must report the certificate to the issuing certificate authority and to CISA. Of course, it makes sense to put equivalent actions in place within your environment, as well. \ In DNS hijacking, the attacker assumes control over an account or makes use of a DNS service exploit to make changes to DNS records. Once they gain access, attackers can substitute their own MX records, name-server records, and addresses, redirecting emails and traffic through their infrastructure, where they can read, copy, or modify information seen. They can also generate valid encryption certificates to help them avoid browser-certificate checks. In one notable attack on the Internet service provider, GoDaddy, the hackers altered Sender Policy Framework (SPF) records a relatively minor change that did not inflict excessive damage but allowed for more effective spam campaigns.\ The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [DarkSide Ransomware] category = Malware @@ -467,7 +467,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the DarkSide Ransomware narrative = This story addresses Darkside ransomware. This ransomware payload has many similarities to common ransomware however there are certain items particular to it. The creation of a .TXT log that shows every item being encrypted as well as the creation of ransomware notes and files adding a machine ID created based on CRC32 checksum algorithm. This ransomware payload leaves machines in minimal operation level,enough to browse the attackers websites. A customized URI with leaked information is presented to each victim.This is the ransomware payload that shut down the Colonial pipeline. The story is composed of several detection searches covering similar items to other ransomware payloads and those particular to Darkside payload. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Data Exfiltration] category = Adversary Tactics @@ -484,7 +484,7 @@ data_models = ["Endpoint"] providing_technologies = none description = The stealing of data by an adversary. narrative = Exfiltration comes in many flavors. Adversaries can collect data over encrypted or non-encrypted channels. They can utilise Command and Control channels that are already in place to exfiltrate data. They can use both standard data transfer protocols such as FTP, SCP, etc to exfiltrate data. Or they can use non-standard protocols such as DNS, ICMP, etc with specially crafted fields to try and circumvent security technologies in place. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Data Protection] category = Abuse @@ -501,7 +501,7 @@ data_models = ["Change_Analysis", "Endpoint", "Network_Resolution", "Network_Tra providing_technologies = none description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. narrative = Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Deobfuscate-Decode Files or Information] category = Adversary Tactics @@ -518,7 +518,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Adversaries may use Obfuscated Files or Information to hide artifacts of an intrusion from analysis. narrative = An example of obfuscated files is `Certutil.exe` usage to encode a portable executable to a certificate file, which is base64 encoded, to hide the originating file. There are many utilities cross-platform to encode using XOR, using compressed .cab files to hide contents and scripting languages that may perform similar native Windows tasks. Triaging an event related will require the capability to review related process events and file modifications. Using a tool such as CyberChef will assist with identifying the encoding that was used, and potentially assist with decoding the contents. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Detect Zerologon Attack] category = Adversary Tactics @@ -535,7 +535,7 @@ data_models = [] providing_technologies = none description = Uncover activity related to the execution of Zerologon CVE-2020-11472, a technique wherein attackers target a Microsoft Windows Domain Controller to reset its computer account password. The result from this attack is attackers can now provide themselves high privileges and take over Domain Controller. The included searches in this Analytic Story are designed to identify attempts to reset Domain Controller Computer Account via exploit code remotely or via the use of tool Mimikatz as payload carrier. narrative = This attack is a privilege escalation technique, where attacker targets a Netlogon secure channel connection to a domain controller, using Netlogon Remote Protocol (MS-NRPC). This vulnerability exposes vulnerable Windows Domain Controllers to be targeted via unaunthenticated RPC calls which eventually reset Domain Contoller computer account ($) providing the attacker the opportunity to exfil domain controller credential secrets and assign themselve high privileges that can lead to domain controller and potentially complete network takeover. The detection searches in this Analytic Story use Windows Event viewer events and Sysmon events to detect attack execution, these searches monitor access to the Local Security Authority Subsystem Service (LSASS) process which is an indicator of the use of Mimikatz tool which has bee updated to carry this attack payload. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Dev Sec Ops] category = Cloud Security @@ -544,8 +544,8 @@ modification_date = 2021-08-18 id = 0ca8c38e-631e-4b81-940c-f9c5450ce41e version = 1 reference = ["https://www.redhat.com/en/topics/devops/what-is-devsecops"] -detection_searches = ["ESCU - AWS ECR Container Scanning Findings High - Rule", "ESCU - AWS ECR Container Scanning Findings Low Informational Unknown - Rule", "ESCU - AWS ECR Container Scanning Findings Medium - Rule", "ESCU - AWS ECR Container Upload Outside Business Hours - Rule", "ESCU - AWS ECR Container Upload Unknown User - Rule"] -mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003"], "nist": ["DE.CM", "PR.AC", "PR.DS"]} +detection_searches = ["ESCU - AWS ECR Container Scanning Findings High - Rule", "ESCU - AWS ECR Container Scanning Findings Low Informational Unknown - Rule", "ESCU - AWS ECR Container Scanning Findings Medium - Rule", "ESCU - AWS ECR Container Upload Outside Business Hours - Rule", "ESCU - AWS ECR Container Upload Unknown User - Rule", "ESCU - Kubernetes Nginx Ingress LFI - Rule", "ESCU - Kubernetes Nginx Ingress RFI - Rule", "ESCU - Kubernetes Scanner Image Pulling - Rule"] +mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.003", "T1212", "T1526"], "nist": ["DE.CM", "PR.AC", "PR.DS"]} investigative_searches = [] support_searches = [] data_models = [] @@ -569,7 +569,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. narrative = Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a block list (which would prevent them from running). -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Domain Trust Discovery] category = Adversary Tactics @@ -586,7 +586,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Adversaries may attempt to gather information on domain trust relationships that may be used to identify lateral movement opportunities in Windows multi-domain/forest environments. narrative = Domain trusts provide a mechanism for a domain to allow access to resources based on the authentication procedures of another domain. Domain trusts allow the users of the trusted domain to access resources in the trusting domain. The information discovered may help the adversary conduct SID-History Injection, Pass the Ticket, and Kerberoasting. Domain trusts can be enumerated using the DSEnumerateDomainTrusts() Win32 API call, .NET methods, and LDAP. The Windows utility Nltest is known to be used by adversaries to enumerate domain trusts. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Dynamic DNS] category = Malware @@ -602,8 +602,8 @@ support_searches = [] data_models = ["Endpoint", "Network_Resolution", "Network_Traffic", "Web"] providing_technologies = none description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and deny lists. -narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and deny lists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and deny lists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Emotet Malware DHS Report TA18-201A ] category = Malware @@ -622,7 +622,7 @@ description = Detect rarely used executables, specific registry paths that may c narrative = The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ The searches in this Analytic Story will help you find executables that are rarely used in your environment, specific registry paths that malware often uses to ensure survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that Emotet or other malware has compromised your environment. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [F5 TMUI RCE CVE-2020-5902] category = Adversary Tactics @@ -639,7 +639,7 @@ data_models = [] providing_technologies = none description = Uncover activity consistent with CVE-2020-5902. Discovered by Positive Technologies researchers, this vulnerability affects F5 BIG-IP, BIG-IQ. and Traffix SDC devices (vulnerable versions in F5 support link below). This vulnerability allows unauthenticated users, along with authenticated users, who have access to the configuration utility to execute system commands, create/delete files, disable services, and/or execute Java code. This vulnerability can result in full system compromise. narrative = A client is able to perform a remote code execution on an exposed and vulnerable system. The detection search in this Analytic Story uses syslog to detect the malicious behavior. Syslog is going to be the best detection method, as any systems using SSL to protect their management console will make detection via wire data difficult. The searches included used Splunk Connect For Syslog (https://splunkbase.splunk.com/app/4740/), and used a custom destination port to help define the data as F5 data (covered in https://splunk-connect-for-syslog.readthedocs.io/en/master/sources/F5/) -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [GCP Cross Account Activity] category = Cloud Security @@ -658,7 +658,7 @@ description = Track when a user assumes an IAM role in another GCP account to ob narrative = Google Cloud Platform (GCP) admins manage access to GCP resources and services across the enterprise using GCP Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage GCP users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as Compute instances, the GCP Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are potentially assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ This Analytic Story includes searches that will help you monitor your GCP Audit logs logs for evidence of suspicious cross-account activity. For example, while accessing multiple GCP accounts and roles may be perfectly valid behavior, it may be suspicious when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [HAFNIUM Group] category = Adversary Tactics @@ -677,7 +677,7 @@ description = HAFNIUM group was identified by Microsoft as exploiting 4 Microsof narrative = On Tuesday, March 2, 2021, Microsoft released a set of security patches for its mail server, Microsoft Exchange. These patches respond to a group of vulnerabilities known to impact Exchange 2013, 2016, and 2019. It is important to note that an Exchange 2010 security update has also been issued, though the CVEs do not reference that version as being vulnerable.\ While the CVEs do not shed much light on the specifics of the vulnerabilities or exploits, the first vulnerability (CVE-2021-26855) has a remote network attack vector that allows the attacker, a group Microsoft named HAFNIUM, to authenticate as the Exchange server. Three additional vulnerabilities (CVE-2021-26857, CVE-2021-26858, and CVE-2021-27065) were also identified as part of this activity. When chained together along with CVE-2021-26855 for initial access, the attacker would have complete control over the Exchange server. This includes the ability to run code as SYSTEM and write to any path on the server.\ The following Splunk detections assist with identifying the HAFNIUM groups tradecraft and methodology. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Hidden Cobra Malware] category = Malware @@ -693,11 +693,11 @@ support_searches = ["ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseli data_models = ["Authentication", "Email", "Endpoint", "Network_Resolution", "Network_Traffic"] providing_technologies = none description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. -narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ -These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ -In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ -Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ +These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ +In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ +Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [IcedID] category = Malware @@ -713,8 +713,8 @@ support_searches = ["ESCU - Previously seen command line arguments"] data_models = ["Endpoint"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the IcedID banking trojan, including looking for file writes associated with its payload, process injection, shellcode execution and data collection. -narrative = IcedId banking trojan campaigns targeting banks and other vertical sectors.This malware is known in Microsoft Windows OS targetting browser such as firefox and chrom to steal banking information. It is also known to its unique payload downloaded in C2 where it can be a .png file that hides the core shellcode bot using steganography technique or gzip dat file that contains "license.dat" which is the actual core icedid bot. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = IcedId banking trojan campaigns targeting banks and other vertical sectors.This malware is known in Microsoft Windows OS targetting browser such as firefox and chrom to steal banking information. It is also known to its unique payload downloaded in C2 where it can be a .png file that hides the core shellcode bot using steganography technique or gzip dat file that contains "license.dat" which is the actual core icedid bot. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Ingress Tool Transfer] category = Adversary Tactics @@ -731,7 +731,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Adversaries may transfer tools or other files from an external system into a compromised environment. Files may be copied from an external adversary controlled system through the command and control channel to bring tools into the victim network or through alternate protocols with another tool such as FTP. narrative = Ingress tool transfer is a Technique under tactic Command and Control. Behaviors will include the use of living off the land binaries to download implants or binaries over alternate communication ports. It is imperative to baseline applications on endpoints to understand what generates network activity, to where, and what is its native behavior. These utilities, when abused, will write files to disk in world writeable paths.\ During triage, review the reputation of the remote public destination IP or domain. Capture any files written to disk and perform analysis. Review other parrallel processes for additional behaviors. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [JBoss Vulnerability] category = Vulnerability @@ -762,7 +762,7 @@ If you suspect an attack targeting a web server, it is helpful to look at some o If a suspicious file is found, we can review more information about it to help determine if it is, in fact, malicious. Identifying the file type, any processes that opened the file, the processes that may have created and/or modified the file, and how many other systems potentially have this file can you determine whether the file is malicious. Also, determining the file hash and checking it against reputation sources, such as VirusTotal, can sometimes help you quickly determine if it is malicious in nature.\ Often, a simple inspection of a suspect process name and path can tell you if the system has been compromised. For example, if svchost.exe is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when simply reviewing process names. \ It can also be helpful to examine various behaviors of and the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see whether the parent process spawned other processes that might also warrant further scrutiny. If a process is suspect, a review of the network connections made around the time of the event and noting whether the process has spawned any child processes could be helpful in determining whether it is malicious or executing a malicious script. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Kubernetes Scanning Activity] category = Cloud Security @@ -779,7 +779,7 @@ data_models = [] providing_technologies = none description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Kubernetes Sensitive Object Access Activity] category = Cloud Security @@ -796,7 +796,7 @@ data_models = [] providing_technologies = none description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Lateral Movement] category = Adversary Tactics @@ -812,12 +812,12 @@ support_searches = [] data_models = ["Authentication", "Email", "Endpoint", "Network_Traffic"] providing_technologies = none description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. -narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ -Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ +narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ +Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ An adversary can use lateral movement for multiple purposes, including remote execution of tools, pivoting to additional systems, obtaining access to specific information or files, access to additional credentials, exfiltrating data, or delivering a secondary effect. Adversaries may use legitimate credentials alongside inherent network and operating-system functionality to remotely connect to other systems and remain under the radar of network defenders.\ If there is evidence of lateral movement, it is imperative for analysts to collect evidence of the associated offending hosts. For example, an attacker might leverage host A to gain access to host B. From there, the attacker may try to move laterally to host C. In this example, the analyst should gather as much information as possible from all three hosts. \ It is also important to collect authentication logs for each host, to ensure that the offending accounts are well-documented. Analysts should account for all processes to ensure that the attackers did not install unauthorized software. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Malicious PowerShell] category = Adversary Tactics @@ -826,26 +826,26 @@ modification_date = 2017-08-23 id = 2c8ff66e-0b57-42af-8ad7-912438a403fc version = 5 reference = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -detection_searches = ["ESCU - Any Powershell DownloadFile - Rule", "ESCU - Any Powershell DownloadString - Rule", "ESCU - Detect Empire with PowerShell Script Block Logging - Rule", "ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - PowerShell Domain Enumeration - Rule", "ESCU - PowerShell Loading DotNET into Memory via System Reflection Assembly - Rule", "ESCU - Powershell Creating Thread Mutex - Rule", "ESCU - Powershell Enable SMB1Protocol Feature - Rule", "ESCU - Powershell Execute COM Object - Rule", "ESCU - Powershell Fileless Process Injection via GetProcAddress - Rule", "ESCU - Powershell Fileless Script Contains Base64 Encoded Content - Rule", "ESCU - Powershell Processing Stream Of Data - Rule", "ESCU - Powershell Using memory As Backing Store - Rule", "ESCU - Recon AVProduct Through Pwh or WMI - Rule", "ESCU - Recon Using WMI Class - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unloading AMSI via Reflection - Rule", "ESCU - WMI Recon Running Process Or Services - Rule"] +detection_searches = ["ESCU - Any Powershell DownloadFile - Rule", "ESCU - Any Powershell DownloadString - Rule", "ESCU - Detect Empire with PowerShell Script Block Logging - Rule", "ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - PowerShell 4104 Hunting - Rule", "ESCU - PowerShell Domain Enumeration - Rule", "ESCU - PowerShell Loading DotNET into Memory via System Reflection Assembly - Rule", "ESCU - Powershell Creating Thread Mutex - Rule", "ESCU - Powershell Enable SMB1Protocol Feature - Rule", "ESCU - Powershell Execute COM Object - Rule", "ESCU - Powershell Fileless Process Injection via GetProcAddress - Rule", "ESCU - Powershell Fileless Script Contains Base64 Encoded Content - Rule", "ESCU - Powershell Processing Stream Of Data - Rule", "ESCU - Powershell Using memory As Backing Store - Rule", "ESCU - Recon AVProduct Through Pwh or WMI - Rule", "ESCU - Recon Using WMI Class - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unloading AMSI via Reflection - Rule", "ESCU - WMI Recon Running Process Or Services - Rule"] mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Exploitation", "Installation", "Privilege Escalation", "Reconnaissance"], "mitre_attack": ["T1003", "T1027", "T1027.005", "T1055", "T1059.001", "T1140", "T1546.015", "T1562", "T1592"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} investigative_searches = ["ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] support_searches = [] data_models = ["Email", "Endpoint"] providing_technologies = none -description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. +description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. narrative = The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope. \ The following factors may assist you in determining whether the event is malicious: \ 1. Country of origin \ 1. Responsible party \ 1. Fully qualified domain names associated with the external IP address \ 1. Registration of fully qualified domain names associated with external IP address \ -Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you answer some questions surrounding the attacker and details related to the external system. In addition, there are various sources--such as VirusTotal&#151; that can provide some reputation information on the IP address or domain name, which can assist in determining whether the event is malicious. Finally, determining whether there are other events associated with the IP address may help connect data points or show other events that should be brought into scope. \ +Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you answer some questions surrounding the attacker and details related to the external system. In addition, there are various sources--such as VirusTotal— that can provide some reputation information on the IP address or domain name, which can assist in determining whether the event is malicious. Finally, determining whether there are other events associated with the IP address may help connect data points or show other events that should be brought into scope. \ Gathering data on the system of interest can sometimes help you quickly determine whether something suspicious is happening. Some of these items include finding out who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted. \ Often, a simple inspection of the process name and path can tell you if the system has been compromised. For example, if `svchost.exe` is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when cursorily reviewing process names. Similarly, if the process itself seems legitimate, but the parent process is running from the temporary browser cache, that could be indicative of activity initiated via a compromised website a user visited. \ It can also be very helpful to examine various behaviors of the process of interest or the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might be worth further scrutiny. If a process is suspect, a review of the network connections made in and around the time of the event and/or whether the process spawned any child processes could be helpful, as well. \ In the event a system is suspected of having been compromised via a malicious website, we suggest reviewing the browsing activity from that system around the time of the event. If categories are given for the URLs visited, that can help you zero in on possible malicious sites. \ Most recently we have added new content related to PowerShell Script Block logging, Windows EventCode 4104. Script block logging presents the deobfuscated and raw script executed on an endpoint. The analytics produced were tested against commonly used attack frameworks - PowerShell-Empire, Cobalt Strike and Covenant. In addition, we sampled publicly available samples that utilize PowerShell and validated coverage. The analytics are here to identify suspicious usage, cmdlets, or script values. 4104 events are enabled via the Windows registry and may generate a large volume of data if enabled globally. Enabling on critical systems or a limited set may be best. During triage of 4104 events, review parallel processes for other processes and command executed. Identify any file modifications and network communication and review accordingly. Fortunately, we get the full script to determine the level of threat identified. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Masquerading - Rename System Utilities] category = Adversary Tactics @@ -864,7 +864,7 @@ description = Adversaries may rename legitimate system utilities to try to evade narrative = Security monitoring and control mechanisms may be in place for system utilities adversaries are capable of abusing. It may be possible to bypass those security mechanisms by renaming the utility prior to utilization (ex: rename rundll32.exe). An alternative case occurs when a legitimate utility is copied or moved to a different directory and renamed to avoid detections based on system utilities executing from non-standard paths.\ The following content is here to assist with binaries within `system32` or `syswow64` being moved to a new location or an adversary bringing a the binary in to execute.\ There will be false positives as some native Windows processes are moved or ran by third party applications from different paths. If file names are mismatched between the file name on disk and that of the binarys PE metadata, this is a likely indicator that a binary was renamed after it was compiled. Collecting and comparing disk and resource filenames for binaries by looking to see if the InternalName, OriginalFilename, and or ProductName match what is expected could provide useful leads, but may not always be indicative of malicious activity. Do not focus on the possible names a file could have, but instead on the command-line arguments that are known to be used and are distinct because it will have a better rate of detection. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Meterpreter] category = Adversary Tactics @@ -883,7 +883,7 @@ description = Meterpreter provides red teams, pen testers and threat actors inte narrative = This Analytic Story supports you to detect Tactics, Techniques and Procedures (TTPs) from Meterpreter. Meterpreter is a Metasploit payload for remote execution that leverages DLL injection to make it extremely difficult to detect. Since the software runs in memory, no new processes are created upon injection. It also leverages encrypted communication channels.\ Meterpreter enables the operator to remotely run commands on the target machine, upload payloads, download files, dump password hashes, and much more. It is difficult to determine from the forensic evidence what actions the operator performed. Splunk Research, however, has observed anomalous behaviors on the compromised hosts that seem to only appear when Meterpreter is executing various commands. With that, we have written new detections targeted to these detections.\ While investigating a detection related to this analytic story, please bear in mind that the detections look for anomalies in system behavior. It will be imperative to look for other signs in the endpoint and network logs for lateral movement, discovery and other actions to confirm that the host was compromised and a remote actor used it to progress on their objectives. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Monitor for Updates] category = Best Practices @@ -902,7 +902,7 @@ description = Monitor your enterprise to ensure that your endpoints are being pa narrative = It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ Microsoft releases updates for Windows systems on a monthly cadence. They should be installed as soon as possible after following internal testing and validation procedures. Patches and updates for other systems or applications are typically released as needed. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [NOBELIUM Group] category = Adversary Tactics @@ -919,7 +919,7 @@ data_models = ["Endpoint", "Network_Traffic", "Web"] providing_technologies = none description = Sunburst is a trojanized updates to SolarWinds Orion IT monitoring and management software. It was discovered by FireEye in December 2020. The actors behind this campaign gained access to numerous public and private organizations around the world. narrative = This Analytic Story supports you to detect Tactics, Techniques and Procedures (TTPs) of the NOBELIUM Group. The threat actor behind sunburst compromised the SolarWinds.Orion.Core.BusinessLayer.dll, is a SolarWinds digitally-signed component of the Orion software framework that contains a backdoor that communicates via HTTP to third party servers. The detections in this Analytic Story are focusing on the dll loading events, file create events and network events to detect This malware. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Netsh Abuse] category = Abuse @@ -937,7 +937,7 @@ providing_technologies = none description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. narrative = It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Office 365 Detections] category = Cloud Security @@ -954,7 +954,7 @@ data_models = [] providing_technologies = none description = This story is focused around detecting Office 365 Attacks. narrative = More and more companies are using Microsofts Office 365 cloud offering. Therefore, we see more and more attacks against Office 365. This story provides various detections for Office 365 attacks. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Orangeworm Attack Group] category = Malware @@ -974,7 +974,7 @@ narrative = In May of 2018, the attack group Orangeworm was implicated for insta Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ Healthcare may be a promising target, because it is notoriously behind in technology, often using older operating systems and neglecting to patch computers. Even so, the group was able to evade detection for a full three years. Sources say that the malware spread quickly within the target networks, infecting computers used to control medical devices, such as MRI and X-ray machines.\ This Analytic Story is designed to help you detect and investigate suspicious activities that may be indicative of an Orangeworm attack. One detection search looks for command-line arguments. Another monitors for uses of sc.exe, a non-essential Windows file that can manipulate Windows services. One of the investigative searches helps you get more information on web hosts that you suspect have been compromised. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns] category = Adversary Tactics @@ -991,8 +991,8 @@ data_models = ["Email", "Endpoint"] providing_technologies = none description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. narrative = This story was created as a joint effort between iDefense and Splunk.\ -iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ -The MUDCARP techniques include the use of the compressed-folders module from Microsoft, zipfldr.dll, with RouteTheCall export to run the malicious process or command. After a successful reboot, the malware is made persistent by a manipulating `[HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Run]'help'='c:\\windows\\system32\\rundll32.exe c:\\windows\\system32\\zipfldr.dll,RouteTheCall c:\\programdata\\winapp.exe'`. Though this technique is not exclusive to MUDCARP, it has been spotted in the group's arsenal of advanced techniques seen in the wild.\ +iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ +The MUDCARP techniques include the use of the compressed-folders module from Microsoft, zipfldr.dll, with RouteTheCall export to run the malicious process or command. After a successful reboot, the malware is made persistent by a manipulating `[HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Run]'help'='c:\\windows\\system32\\rundll32.exe c:\\windows\\system32\\zipfldr.dll,RouteTheCall c:\\programdata\\winapp.exe'`. Though this technique is not exclusive to MUDCARP, it has been spotted in the group's arsenal of advanced techniques seen in the wild.\ This Analytic Story searches for evidence of tactics, techniques, and procedures (TTPs) that allow for the use of a endpoint detection-and-response (EDR) bypass technique to mask the true parent of a malicious process. It can also be set as a registry key for further sandbox evasion and to allow the malware to launch only after reboot.\ If behavioral searches included in this story yield positive hits, iDefense recommends conducting IOC searches for the following:\ \ @@ -1018,7 +1018,7 @@ If behavioral searches included in this story yield positive hits, iDefense reco 1. 889a9b52566448231f112a5ce9b5dfaf\ 1. b8ec65dab97cdef3cd256cc4753f0c54\ 1. 04d83cd3813698de28cfbba326d7647c -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [PrintNightmare CVE-2021-34527] category = Lateral Movement @@ -1040,7 +1040,7 @@ The prerequisites for successful exploitation consist of: \ 1. Network connectivity to the target system (initial access has been obtained) \ 1. Hash or password for a low privileged user ( or computer ) account. \ In the most impactful scenario, an attacker would be able to leverage this vulnerability to obtain a SYSTEM shell on a domain controller and so escalate their privileges from a low privileged domain account to full domain access in the target environment as shown below. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Prohibited Traffic Allowed or Protocol Mismatch] category = Best Practices @@ -1057,7 +1057,7 @@ data_models = ["Endpoint", "Network_Resolution", "Network_Traffic"] providing_technologies = none description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. narrative = A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Ransomware] category = Malware @@ -1074,7 +1074,7 @@ data_models = ["Email", "Endpoint", "Network_Traffic"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. narrative = Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Ransomware Cloud] category = Malware @@ -1091,7 +1091,7 @@ data_models = [] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware. These searches include cloud related objects that may be targeted by malicious actors via cloud providers own encryption features. narrative = Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise.Cloud ransomware can be deployed by obtaining high privilege credentials from targeted users or resources. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Revil Ransomware] category = Malware @@ -1108,7 +1108,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the Revil ransomware, including looking for file writes associated with Revil, encrypting network shares, deleting shadow volume storage, registry key modification, deleting of security logs, and more. narrative = Revil ransomware is a RaaS,that a single group may operates and manges the development of this ransomware. It involve the use of ransomware payloads along with exfiltration of data. Malicious actors demand payment for ransome of data and threaten deletion and exposure of exfiltrated data. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Router and Infrastructure Security] category = Best Practices @@ -1125,8 +1125,8 @@ data_models = ["Authentication", "Network_Traffic"] providing_technologies = none description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. narrative = Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ -This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure&#151; effectively increasing the attack surface and accessing private services/data. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Ryuk Ransomware] category = Malware @@ -1143,7 +1143,7 @@ data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the Ryuk ransomware, including looking for file writes associated with Ryuk, Stopping Security Access Manager, DisableAntiSpyware registry key modification, suspicious psexec use, and more. narrative = Cybersecurity Infrastructure Security Agency (CISA) released Alert (AA20-302A) on October 28th called “Ransomware Activity Targeting the Healthcare and Public Health Sector.” This alert details TTPs associated with ongoing and possible imminent attacks against the Healthcare sector, and is a joint advisory in coordination with other U.S. Government agencies. The objective of these malicious campaigns is to infiltrate targets in named sectors and to drop ransomware payloads, which will likely cause disruption of service and increase risk of actual harm to the health and safety of patients at hospitals, even with the aggravant of an ongoing COVID-19 pandemic. This document specifically refers to several crimeware exploitation frameworks, emphasizing the use of Ryuk ransomware as payload. The Ryuk ransomware payload is not new. It has been well documented and identified in multiple variants. Payloads need a carrier, and for Ryuk it has often been exploitation frameworks such as Cobalt Strike, or popular crimeware frameworks such as Emotet or Trickbot. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [SQL Injection] category = Adversary Tactics @@ -1161,7 +1161,7 @@ providing_technologies = none description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. narrative = It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [SamSam Ransomware] category = Malware @@ -1178,12 +1178,12 @@ data_models = ["Authentication", "Email", "Endpoint", "Network_Traffic", "Web"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. narrative = The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ -Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ -SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ -In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ +Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ +SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ +In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ According to Sophos, SamSam previously leveraged RDP to gain access to targeted networks via brute force. SamSam is not spread automatically, like other malware. It requires skill because it forces the attacker to adapt their tactics to the individual environment. Next, the actors escalate their privileges to admin level. They scan the networks for worthy targets, using conventional tools, such as PsExec or PaExec, to deploy/execute, quickly encrypting files.\ This Analytic Story includes searches designed to help detect and investigate signs of the SamSam ransomware, such as the creation of fileswrites to system32, writes with tell-tale extensions, batch files written to system32, and evidence of brute-force attacks via RDP. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Silver Sparrow] category = Adversary Tactics @@ -1200,7 +1200,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Silver Sparrow, identified by Red Canary Intelligence, is a new forward looking MacOS (Intel and M1) malicious software downloader utilizing JavaScript for execution and a launchAgent to establish persistence. narrative = Silver Sparrow works is a dropper and uses typical persistence mechanisms on a Mac. It is cross platform, covering both Intel and Apple M1 architecture. To this date, no implant has been downloaded for malicious purposes. During installation of the update.pkg or updater.pkg file, the malicious software utilizes JavaScript to generate files and scripts on disk for persistence.These files later download a implant from an S3 bucket every hour. This analytic assists with identifying different types of macOS malware families establishing LaunchAgent persistence. Per SentinelOne source, it is predicted that Silver Sparrow is likely selling itself as a mechanism to 3rd party “affiliates” or pay-per-install (PPI) partners, typically seen as commodity adware/malware. Additional indicators and behaviors may be found within the references. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Spearphishing Attachments] category = Adversary Tactics @@ -1217,14 +1217,14 @@ data_models = ["Endpoint"] providing_technologies = none description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. narrative = Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ -As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ -While any kind of file may contain a malicious payload, some are more likely to be perceived as benign (and thus more often escape notice) by the average victim&#151;especially when the attacker sends an email that seems to be from one of their contacts. An example is Microsoft Office files. Most corporate users are familiar with documents with the following suffixes: .doc/.docx (MS Word), .xls/.xlsx (MS Excel), and .ppt/.pptx (MS PowerPoint), so they may click without a second thought, slashing a hole in their organizations' security. \ +As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ +While any kind of file may contain a malicious payload, some are more likely to be perceived as benign (and thus more often escape notice) by the average victim—especially when the attacker sends an email that seems to be from one of their contacts. An example is Microsoft Office files. Most corporate users are familiar with documents with the following suffixes: .doc/.docx (MS Word), .xls/.xlsx (MS Excel), and .ppt/.pptx (MS PowerPoint), so they may click without a second thought, slashing a hole in their organizations' security. \ Following is a typical series of events, according to an [article by Trend Micro](https://blog.trendmicro.com/trendlabs-security-intelligence/rising-trend-attackers-using-lnk-files-download-malware/):\ 1. Attacker sends a phishing email. Recipient downloads the attached file, which is typically a .docx or .zip file with an embedded .lnk file\ 1. The .lnk file executes a PowerShell script\ -1. Powershell executes a reverse shell, rendering the exploit successful </ol>As a side note, adversaries are likely to use a tool like Empire to craft and obfuscate payloads and their post-injection activities, such as [exfiltration, lateral movement, and persistence](https://github.com/EmpireProject/Empire).\ +1. Powershell executes a reverse shell, rendering the exploit successful As a side note, adversaries are likely to use a tool like Empire to craft and obfuscate payloads and their post-injection activities, such as [exfiltration, lateral movement, and persistence](https://github.com/EmpireProject/Empire).\ This Analytic Story focuses on detecting signs that a malicious payload has been injected into your environment. For example, one search detects outlook.exe writing a .zip file. Another looks for suspicious .lnk files launching processes. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious AWS Login Activities] category = Cloud Security @@ -1241,7 +1241,7 @@ data_models = ["Authentication"] providing_technologies = none description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. narrative = It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious AWS S3 Activities] category = Cloud Security @@ -1258,9 +1258,9 @@ data_models = [] providing_technologies = none description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ -Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ +Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ Among things to look out for are S3 access from unfamiliar locations and by unfamiliar users. Some of the searches in this Analytic Story help you detect suspicious behavior and others help you investigate more deeply, when the situation warrants. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious AWS Traffic] category = Cloud Security @@ -1277,10 +1277,10 @@ data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). narrative = A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ -Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ +Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ Attackers may abuse the AWS infrastructure with insecure VPCs so they can co-opt AWS resources for command-and-control nodes, data exfiltration, and more. Once an EC2 instance is compromised, an attacker may initiate outbound network connections for malicious reasons. Monitoring these network traffic behaviors is crucial for understanding the type of traffic flowing in and out of your network and to alert you to suspicious activities.\ The searches in this Analytic Story will monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Cloud Authentication Activities] category = Cloud Security @@ -1298,7 +1298,7 @@ providing_technologies = none description = Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity. narrative = It is important to monitor and control who has access to your cloud infrastructure. Detecting suspicious logins will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any compute activity whether legitimate or otherwise.\ This Analytic Story has data model versions of cloud searches leveraging Authentication data, including those looking for suspicious login activity, and cross-account activity for AWS. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Cloud Instance Activities] category = Cloud Security @@ -1315,7 +1315,7 @@ data_models = ["Change"] providing_technologies = none description = Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. narrative = Monitoring your cloud infrastructure logs allows you enable governance, compliance, and risk auditing. It is crucial for a company to monitor events and actions taken in the their cloud environments to ensure that your instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your cloud compute instances and helps you respond and investigate those activities. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Cloud Provisioning Activities] category = Cloud Security @@ -1332,8 +1332,8 @@ data_models = ["Change"] providing_technologies = none description = Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. narrative = Because most enterprise cloud infrastructure activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to add specific IPs to an allow list because they vary.\ -This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Cloud User Activities] category = Cloud Security @@ -1349,9 +1349,9 @@ support_searches = ["ESCU - Baseline Of Cloud Infrastructure API Calls Per User" data_models = ["Change"] providing_technologies = none description = Detect and investigate suspicious activities by users and roles in your cloud environments. -narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ +narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new instances and increased bandwidth usage. -product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Security Analytics for AWS', 'Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Command-Line Executions] category = Adversary Tactics @@ -1366,9 +1366,9 @@ investigative_searches = ["ESCU - Get Notable History - Response Task", "ESCU - support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] data_models = ["Endpoint"] providing_technologies = none -description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. +description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. narrative = The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Compiled HTML Activity] category = Adversary Tactics @@ -1388,7 +1388,7 @@ narrative = Adversaries may abuse Compiled HTML files (.chm) to conceal maliciou HH.exe relies upon hhctrl.ocx to load CHM topics.This will load upon execution of a chm file. \ During investigation, review all parallel processes and child processes. It is possible for file modification events to occur and it is best to capture the CHM file and decompile it for further analysis. \ Upon usage of InfoTech Storage Handlers, ms-its, its, mk, itss.dll will load. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious DNS Traffic] category = Adversary Tactics @@ -1404,8 +1404,8 @@ support_searches = ["ESCU - Baseline of DNS Query Length - MLTK"] data_models = ["Endpoint", "Network_Resolution", "Network_Traffic"] providing_technologies = none description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. -narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Emails] category = Adversary Tactics @@ -1426,7 +1426,7 @@ Once a phishing message has been detected, the next steps are to answer the foll 1. Which users have received this or a similar message in the past?\ 1. When did the targeted campaign begin?\ 1. Have any users interacted with the content of the messages (by downloading an attachment or clicking on a malicious URL)?This Analytic Story provides detection searches to identify suspicious emails, as well as contextual and investigative searches to help answer some of these questions. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious GCP Storage Activities] category = Cloud Security @@ -1443,7 +1443,7 @@ data_models = [] providing_technologies = none description = Use the searches in this Analytic Story to monitor your GCP Storage buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open storage buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = Similar to other cloud providers, GCP operates on a shared responsibility model. This means the end user, you, are responsible for setting appropriate access control lists and permissions on your GCP resources.\ This Analytics Story concentrates on detecting things like open storage buckets (both read and write) along with storage bucket access from unfamiliar users and IP addresses. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious MSHTA Activity] category = Adversary Tactics @@ -1472,7 +1472,7 @@ The objective of this step is meant to identify suspicious behavioral indicators 1. Network connections. Any network connections? Review the reputation of the remote IP or domain.\ Retrieval of script code\ The objective of this step is to confirm the executed script code is benign or malicious. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Okta Activity] category = Adversary Tactics @@ -1491,7 +1491,7 @@ description = Monitor your Okta environment for suspicious activities. Due to th narrative = Okta is the leading single sign on (SSO) provider, allowing users to authenticate once to Okta, and from there access a variety of web-based applications. These applications are assigned to users and allow administrators to centrally manage which users are allowed to access which applications. It also provides centralized logging to help understand how the applications are used and by whom. \ While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ With people moving quickly to adopt web-based applications and ways to manage them, many are still struggling to understand how best to monitor these environments. This analytic story provides searches to help monitor this environment, and identify events and activity that warrant further investigation such as credential stuffing or password spraying attacks, and users logging in from multiple locations when travel is disallowed. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Regsvcs Regasm Activity] category = Adversary Tactics @@ -1508,7 +1508,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. narrative = Adversaries may abuse Regsvcs and Regasm to proxy execution of code through a trusted Windows utility. Regsvcs and Regasm are Windows command-line utilities that are used to register .NET Component Object Model (COM) assemblies. Both are digitally signed by Microsoft. The following queries assist with detecting suspicious and malicious usage of Regasm.exe and Regsvcs.exe. Upon reviewing usage of Regasm.exe Regsvcs.exe, review file modification events for possible script code written. Review parallel process events for csc.exe being utilized to compile script code. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Regsvr32 Activity] category = Adversary Tactics @@ -1524,8 +1524,8 @@ support_searches = [] data_models = ["Endpoint"] providing_technologies = none description = Monitor and detect techniques used by attackers who leverage the regsvr32.exe process to execute malicious code. -narrative = One common adversary tactic is to bypass application control solutions via the regsvr32.exe process. This particular bypass was popularized with "SquiblyDoo" using the "scrobj.dll" dll to load .sct scriptlets. This technique is still widely used by adversaries to bypass detection and prevention controls. The file extension of the DLL is irrelevant (it may load a .txt file extension for example). The searches in this story help you detect and investigate suspicious activity that may indicate that an adversary is leveraging regsvr32.exe to execute malicious code. Validate execution Determine if regsvr32.exe executed. Validate the OriginalFileName of regsvr32.exe and further PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64, it should be highly suspect. Determine if script code was executed with regsvr32. Situational Awareness - The objective of this step is meant to identify suspicious behavioral indicators related to executed of Script code by regsvr32.exe. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application? Module loads. Is regsvr32 loading any suspicious .DLLs? Unsigned or signed from non-standard paths. Network connections. Any network connections? Review the reputation of the remote IP or domain. Retrieval of Script Code - confirm the executed script code is benign or malicious. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = One common adversary tactic is to bypass application control solutions via the regsvr32.exe process. This particular bypass was popularized with "SquiblyDoo" using the "scrobj.dll" dll to load .sct scriptlets. This technique is still widely used by adversaries to bypass detection and prevention controls. The file extension of the DLL is irrelevant (it may load a .txt file extension for example). The searches in this story help you detect and investigate suspicious activity that may indicate that an adversary is leveraging regsvr32.exe to execute malicious code. Validate execution Determine if regsvr32.exe executed. Validate the OriginalFileName of regsvr32.exe and further PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64, it should be highly suspect. Determine if script code was executed with regsvr32. Situational Awareness - The objective of this step is meant to identify suspicious behavioral indicators related to executed of Script code by regsvr32.exe. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application? Module loads. Is regsvr32 loading any suspicious .DLLs? Unsigned or signed from non-standard paths. Network connections. Any network connections? Review the reputation of the remote IP or domain. Retrieval of Script Code - confirm the executed script code is benign or malicious. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Rundll32 Activity] category = Adversary Tactics @@ -1542,7 +1542,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Monitor and detect techniques used by attackers who leverage rundll32.exe to execute arbitrary malicious code. narrative = One common adversary tactic is to bypass application control solutions via the rundll32.exe process. Natively, rundll32.exe will load DLLs and is a great example of a Living off the Land Binary. Rundll32.exe may load malicious DLLs by ordinals, function names or directly. The queries in this story focus on loading default DLLs, syssetup.dll, ieadvpack.dll, advpack.dll and setupapi.dll from disk that may be abused by adversaries. Additionally, two analytics developed to assist with identifying DLLRegisterServer, Start and StartW functions being called. The searches in this story help you detect and investigate suspicious activity that may indicate that an adversary is leveraging rundll32.exe to execute malicious code. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious WMI Use] category = Adversary Tactics @@ -1559,7 +1559,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. narrative = WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems. In the event that unauthorized WMI execution occurs, it will be important for analysts and investigators to determine the context of the event. These details may provide insights related to how WMI was used and to what end. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Windows Registry Activities] category = Adversary Tactics @@ -1576,9 +1576,9 @@ data_models = ["Endpoint"] providing_technologies = none description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. narrative = Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ - The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ + The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Suspicious Zoom Child Processes] category = Adversary Tactics @@ -1596,7 +1596,7 @@ providing_technologies = none description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. narrative = Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Trickbot] category = Malware @@ -1613,7 +1613,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the trickbot banking trojan, including looking for file writes associated with its payload, process injection, shellcode execution and data collection even in LDAP environment. narrative = trickbot banking trojan campaigns targeting banks and other vertical sectors.This malware is known in Microsoft Windows OS where target security Microsoft Defender to prevent its detection and removal. steal Verizon credentials and targeting banks using its multi component modules that collect and exfiltrate data. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Trusted Developer Utilities Proxy Execution] category = Adversary Tactics @@ -1631,7 +1631,7 @@ providing_technologies = none description = Monitor and detect behaviors used by attackers who leverage trusted developer utilities to execute malicious code. narrative = Adversaries may take advantage of trusted developer utilities to proxy execution of malicious payloads. There are many utilities used for software development related tasks that can be used to execute code in various forms to assist in development, debugging, and reverse engineering. These utilities may often be signed with legitimate certificates that allow them to execute on a system and proxy execution of malicious code through a trusted process that effectively bypasses application control solutions.\ The searches in this story help you detect and investigate suspicious activity that may indicate that an adversary is leveraging microsoft.workflow.compiler.exe to execute malicious code. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Trusted Developer Utilities Proxy Execution MSBuild] category = Adversary Tactics @@ -1661,7 +1661,7 @@ The objective of this step is meant to identify suspicious behavioral indicators 1. Network connections. Any network connections? Review the reputation of the remote IP or domain.\ Retrieval of script code\ The objective of this step is to confirm the executed script code is benign or malicious. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Unusual Processes] category = Malware @@ -1677,10 +1677,10 @@ support_searches = ["ESCU - Baseline of Command Line Length - MLTK"] data_models = ["Endpoint"] providing_technologies = none description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. -narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ +narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ In the event an unusual process is identified, it is imperative to better understand how that process was able to execute on the host, when it first executed, and whether other hosts are affected. This extra information may provide clues that can help the analyst further investigate any suspicious activity. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Use of Cleartext Protocols] category = Best Practices @@ -1697,7 +1697,7 @@ data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. narrative = Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Windows DNS SIGRed CVE-2020-1350] category = Adversary Tactics @@ -1713,8 +1713,8 @@ support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none description = Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and is triggered by a malicious DNS response (only affects DNS over TCP). An attacker can use the malicious payload to cause a buffer overflow on the vulnerable system, leading to compromise. The included searches in this Analytic Story are designed to identify the large response payload for SIG and KEY DNS records which can be used for the exploit. -narrative = When a client requests a DNS record for a particular domain, that request gets routed first through the client's locally configured DNS server, then to any DNS server(s) configured as forwarders, and then onto the target domain's own DNS server(s). If a attacker wanted to, they could host a malicious DNS server that responds to the initial request with a specially crafted large response (~65KB). This response would flow through to the client's local DNS server, which if not patched for CVE-2020-1350, would cause the buffer overflow. The detection searches in this Analytic Story use wire data to detect the malicious behavior. Searches for Splunk Stream and Zeek are included. The Splunk Stream search correlates across stream:dns and stream:tcp, while the Zeek search correlates across bro:dns:json and bro:conn:json. These correlations are required to pick up both the DNS record types (SIG and KEY) along with the payload size (>65KB). -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = When a client requests a DNS record for a particular domain, that request gets routed first through the client's locally configured DNS server, then to any DNS server(s) configured as forwarders, and then onto the target domain's own DNS server(s). If a attacker wanted to, they could host a malicious DNS server that responds to the initial request with a specially crafted large response (~65KB). This response would flow through to the client's local DNS server, which if not patched for CVE-2020-1350, would cause the buffer overflow. The detection searches in this Analytic Story use wire data to detect the malicious behavior. Searches for Splunk Stream and Zeek are included. The Splunk Stream search correlates across stream:dns and stream:tcp, while the Zeek search correlates across bro:dns:json and bro:conn:json. These correlations are required to pick up both the DNS record types (SIG and KEY) along with the payload size (>65KB). +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Windows Defense Evasion Tactics] category = Adversary Tactics @@ -1730,8 +1730,8 @@ support_searches = [] data_models = ["Endpoint"] providing_technologies = none description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others -narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Windows File Extension and Association Abuse] category = Malware @@ -1749,10 +1749,10 @@ providing_technologies = none description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. narrative = Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ - Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ -Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ + Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ +Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ Run the searches in this story to detect and investigate suspicious behavior that may indicate abuse or manipulation of Windows file extensions and/or associations. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Windows Log Manipulation] category = Adversary Tactics @@ -1770,7 +1770,7 @@ providing_technologies = none description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. narrative = Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Windows Persistence Techniques] category = Adversary Tactics @@ -1787,7 +1787,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. narrative = Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Windows Privilege Escalation] category = Adversary Tactics @@ -1803,8 +1803,8 @@ support_searches = [] data_models = ["Endpoint"] providing_technologies = none description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. -narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [Windows Service Abuse] category = Malware @@ -1821,7 +1821,7 @@ data_models = ["Endpoint"] providing_technologies = none description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. narrative = The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] [XMRig] category = Malware @@ -1838,6 +1838,6 @@ data_models = ["Endpoint"] providing_technologies = none description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the xmrig monero, including looking for file writes associated with its payload, process command-line, defense evasion (killing services, deleting users, modifying files or folder permission, killing other malware or other coin miner) and hacking tools including Telegram as mean of command and control (C2) to download other files. Adversaries may leverage the resources of co-opted systems in order to solve resource intensive problems which may impact system and/or hosted service availability. One common purpose for Resource Hijacking is to validate transactions of cryptocurrency networks and earn virtual currency. Adversaries may consume enough system resources to negatively impact and/or cause affected machines to become unresponsive. (1) Servers and cloud-based (2) systems are common targets because of the high potential for available resources, but user endpoint systems may also be compromised and used for Resource Hijacking and cryptocurrency mining. narrative = XMRig is a high performance, open source, cross platform RandomX, KawPow, CryptoNight and AstroBWT unified CPU/GPU miner. This monero is seen in the wild on May 2017. -product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] +product = ['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud'] #### END STORIES #### \ No newline at end of file diff --git a/dist/escu/default/collections.conf b/dist/escu/default/collections.conf index 4ac3b787e8..559b7f5d66 100644 --- a/dist/escu/default/collections.conf +++ b/dist/escu/default/collections.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security_content -# On Date: 2021-08-20T22:00:44 UTC +# On Date: 2021-08-27T14:05:22 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# diff --git a/dist/escu/default/es_investigations.conf b/dist/escu/default/es_investigations.conf index f36dbd7908..302ae68c36 100644 --- a/dist/escu/default/es_investigations.conf +++ b/dist/escu/default/es_investigations.conf @@ -29,7 +29,7 @@ panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn___resp [panel_group://workbench_panel_group_aws_user_monitoring] label = AWS User Monitoring -description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. +description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. disabled = 0 panels = ["panel://workbench_panel_get_notable_history___response_task", "panel://workbench_panel_investigate_aws_user_activities_by_user_field___response_task"] @@ -92,7 +92,7 @@ panels = ["panel://workbench_panel_aws_investigate_security_hub_alerts_by_dest__ [panel_group://workbench_panel_group_cloud_federated_credential_abuse] label = Cloud Federated Credential Abuse -description = This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. +description = This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. disabled = 0 panels = ["panel://workbench_panel_get_notable_history___response_task"] @@ -127,7 +127,7 @@ panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn___resp [panel_group://workbench_panel_group_container_implantation_monitoring_and_investigation] label = Container Implantation Monitoring and Investigation -description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. +description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. disabled = 0 panels = ["panel://workbench_panel_get_notable_history___response_task"] @@ -148,7 +148,7 @@ panels = ["panel://workbench_panel_get_notable_history___response_task", "panel: [panel_group://workbench_panel_group_dns_amplification_attacks] label = DNS Amplification Attacks -description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. +description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. disabled = 0 panels = ["panel://workbench_panel_get_notable_history___response_task"] @@ -302,7 +302,7 @@ panels = ["panel://workbench_panel_get_history_of_email_sources___response_task" [panel_group://workbench_panel_group_malicious_powershell] label = Malicious PowerShell -description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. +description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. disabled = 0 panels = ["panel://workbench_panel_get_history_of_email_sources___response_task", "panel://workbench_panel_get_notable_history___response_task", "panel://workbench_panel_get_parent_process_info___response_task", "panel://workbench_panel_get_process_info___response_task"] @@ -491,7 +491,7 @@ panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn___resp [panel_group://workbench_panel_group_suspicious_command_line_executions] label = Suspicious Command-Line Executions -description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. +description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. disabled = 0 panels = ["panel://workbench_panel_get_notable_history___response_task", "panel://workbench_panel_get_parent_process_info___response_task", "panel://workbench_panel_get_process_info___response_task"] diff --git a/dist/escu/default/macros.conf b/dist/escu/default/macros.conf index 76318e1d17..68de37d96d 100644 --- a/dist/escu/default/macros.conf +++ b/dist/escu/default/macros.conf @@ -1,13 +1,13 @@ ############# # Automatically generated by generator.py in splunk/security_content -# On Date: 2021-08-20T22:00:45 UTC +# On Date: 2021-08-27T14:05:22 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# [aws_cloudwatchlogs_eks] -definition = sourcetype="aws:cloudwatchlogs:eks" +definition = sourcetype="aws:cloudwatchlogs:eks" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [aws_config] @@ -15,7 +15,7 @@ definition = sourcetype=aws:config description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [aws_description] -definition = sourcetype="aws:description" +definition = sourcetype="aws:description" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [aws_ecr_users] @@ -27,11 +27,11 @@ definition = sourcetype=aws:s3:accesslogs description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [aws_securityhub_finding] -definition = sourcetype="aws:securityhub:finding" +definition = sourcetype="aws:securityhub:finding" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [aws_securityhub_firehose] -definition = sourcetype="aws:securityhub:firehose" +definition = sourcetype="aws:securityhub:firehose" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [brand_abuse_dns] @@ -51,7 +51,7 @@ definition = eventtype=cisco_ios description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [cloud_api_calls_from_previously_unseen_user_roles_activity_window] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far back you should be checking for new commands from user roles [cloudtrail] @@ -59,7 +59,7 @@ definition = sourcetype=aws:cloudtrail description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [cloudwatch_eks] -definition = sourcetype="aws:cloudwatchlogs:eks" +definition = sourcetype="aws:cloudwatchlogs:eks" description = customer specific splunk configurations(eg- index, source, sourcetype) for AWS cloudwatch eks logs. Replace the macro definition with configurations for your Splunk Environmnent. [cloudwatch_vpc] @@ -111,19 +111,23 @@ definition = (query=outlook* AND query=login* AND query=account*) description = This limits the query fields to domains that are associated with evilginx masquerading as Outlook [f5_bigip_rogue] -definition = index=netops sourcetype="f5:bigip:rogue" +definition = index=netops sourcetype="f5:bigip:rogue" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [filter_rare_process_allow_list] -definition = lookup update=true lookup_rare_process_allow_list_default process as process OUTPUTNEW allow_list | where allow_list="false" | lookup update=true lookup_rare_process_allow_list_local process as process OUTPUT allow_list | where allow_list="false" +definition = lookup update=true lookup_rare_process_allow_list_default process as process OUTPUTNEW allow_list | where allow_list="false" | lookup update=true lookup_rare_process_allow_list_local process as process OUTPUT allow_list | where allow_list="false" description = This macro is intended to allow_list processes that have been definied as rare +[github] +definition = sourcetype=aws:firehose:json +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. + [google_gcp_pubnet_message] -definition = sourcetype="google:gcp:pubsub:message" +definition = sourcetype="google:gcp:pubsub:message" description = customer specific splunk configurations(eg- index, source, sourcetype) for Google GCP. Replace the macro definition with configurations for your Splunk Environmnent. [google_gcp_pubsub_message] -definition = sourcetype="google:gcp:pubsub:message" +definition = sourcetype="google:gcp:pubsub:message" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [gsuite_drive] @@ -138,16 +142,24 @@ description = customer specific splunk configurations(eg- index, source, sourcet definition = lookup update=true is_windows_system_file filename as process_name OUTPUT systemFile | search systemFile=true description = This macro limits the output to process names that are in the Windows System directory +[kube_objects_events] +definition = sourcetype=kube:objects:events +description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. + [kubernetes_azure] definition = sourcetype=mscs:storage:blob:json description = customer specific splunk configurations(eg- index, source, sourcetype) for Kubernetes data from Azure. Replace the macro definition with configurations for your Splunk Environmnent. +[kubernetes_container_controller] +definition = sourcetype=kube:container:controller +description = customer specific splunk configurations(eg- index, source, sourcetype) for Kubernetes data. Replace the macro definition with configurations for your Splunk Environmnent. + [linux_hosts] definition = index=* description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [netbackup] -definition = sourcetype="netbackup_logs" +definition = sourcetype="netbackup_logs" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [network_acl_events] @@ -167,79 +179,83 @@ definition = eventtype=okta_log description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [osquery_process] -definition = eventtype="osquery-process" +definition = eventtype="osquery-process" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [powershell] -definition = (source=WinEventLog:Microsoft-Windows-PowerShell/Operational OR source="XmlWinEventLog:Microsoft-Windows-PowerShell/Operational") +definition = (source=WinEventLog:Microsoft-Windows-PowerShell/Operational OR source="XmlWinEventLog:Microsoft-Windows-PowerShell/Operational") description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [previously_seen_cloud_api_calls_per_user_role_forget_window] -definition = "-90d@d" +definition = "-90d@d" description = Use this macro to determine how long to keep track of cloud api calls per user role [previously_seen_cloud_compute_creations_by_user_search_window_begin_offset] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far into the past the window should be to determine if the user is new or not [previously_seen_cloud_compute_image_search_window_begin_offset] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far into the past the window should be to determine if the image is new or not [previously_seen_cloud_compute_images_forget_window] -definition = "-90d@d" +definition = "-90d@d" description = Use this macro to determine how long to keep track of cloud instance images [previously_seen_cloud_compute_instance_type_forget_window] -definition = "-90d@d" +definition = "-90d@d" description = Use this macro to determine how long to keep track of cloud instance types [previously_seen_cloud_compute_instance_types_search_window_begin_offset] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far into the past the window should be to determine if the instance type is new or not [previously_seen_cloud_instance_modifications_by_user_search_window_begin_offset] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far into the past the window should be to determine if the user is new or not [previously_seen_cloud_provisioning_activity_forget_window] -definition = "-90d@d" +definition = "-90d@d" description = Use this macro to determine how long to keep track of cloud provisioning locations [previously_seen_cloud_region_forget_window] -definition = "-90d@d" +definition = "-90d@d" description = Use this macro to determine how long to keep track of cloud regions [previously_seen_cloud_regions_search_window_begin_offset] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far into the past the window should be to determine if the region is new or not [previously_seen_windows_services_forget_window] -definition = "-90d@d" +definition = "-90d@d" description = Use this macro to determine how long to keep track of Windows services [previously_seen_windows_services_window] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far back you should be checking for new Windows services [previously_seen_zoom_child_processes_forget_window] -definition = "-90d@d" +definition = "-90d@d" description = Use this macro to determine how long to keep track of zoom child processes [previously_seen_zoom_child_processes_window] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far back you should be checking for new zoom child processes [previously_unseen_cloud_provisioning_activity_window] -definition = "-70m@m" +definition = "-70m@m" description = Use this macro to determine how far back you should be checking for new provisioning activities [printservice] -definition = source="wineventlog:microsoft-windows-printservice/operational" OR sourcetype="WinEventLog:Microsoft-Windows-PrintService/Admin" +definition = source="wineventlog:microsoft-windows-printservice/operational" OR sourcetype="WinEventLog:Microsoft-Windows-PrintService/Admin" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. +[process_esentutl] +definition = (Processes.process_name=esentutl.exe OR Processes.original_file_name=esentutl.exe) +description = Matches the process with its original file name, data for this macro came from https://strontic.github.io/ + [prohibited_apps_launching_cmd] -definition = | inputlookup prohibited_apps_launching_cmd | rename prohibited_applications as parent_process_name | eval parent_process_name="*" . parent_process_name | table parent_process_name +definition = | inputlookup prohibited_apps_launching_cmd | rename prohibited_applications as parent_process_name | eval parent_process_name="*" . parent_process_name | table parent_process_name description = This macro outputs a list of process that should not be the parent process of cmd.exe [prohibited_softwares] @@ -251,11 +267,11 @@ definition = lookup update=true ransomware_extensions_lookup Extensions AS file_ description = This macro limits the output to files that have extensions associated with ransomware [ransomware_notes] -definition = lookup ransomware_notes_lookup ransomware_notes as file_name OUTPUT status as "Known Ransomware Notes" | search "Known Ransomware Notes"=True +definition = lookup ransomware_notes_lookup ransomware_notes as file_name OUTPUT status as "Known Ransomware Notes" | search "Known Ransomware Notes"=True description = This macro limits the output to files that have been identified as a ransomware note [remove_valid_domains] -definition = eval domain=trim(domain,"*") | search NOT[| inputlookup domains] NOT[ |inputlookup cim_corporate_email_domain_lookup] NOT[inputlookup cim_corporate_web_domain_lookup] | eval domain="*"+domain+"*" +definition = eval domain=trim(domain,"*") | search NOT[| inputlookup domains] NOT[ |inputlookup cim_corporate_email_domain_lookup] NOT[inputlookup cim_corporate_web_domain_lookup] | eval domain="*"+domain+"*" description = This macro removes valid domains from the output [s3_accesslogs] @@ -264,12 +280,12 @@ description = customer specific splunk configurations(eg- index, source, sourcet [security_content_ctime(1)] args = field -definition = convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) +definition = convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) description = convert epoch time to string [security_content_summariesonly] definition = summariesonly=false allow_old_summaries=true -description = search data model's summaries only +description = search data model's summaries only [security_group_api_calls] definition = (eventName=AuthorizeSecurityGroupIngress OR eventName=CreateSecurityGroup OR eventName=DeleteSecurityGroup OR eventName=DescribeClusterSecurityGroups OR eventName=DescribeDBSecurityGroups OR eventName=DescribeSecurityGroupReferences OR eventName=DescribeSecurityGroups OR eventName=DescribeStaleSecurityGroups OR eventName=RevokeSecurityGroupIngress OR eventName=UpdateSecurityGroupRuleDescriptionsIngress) @@ -292,7 +308,7 @@ definition = lookup update=true is_suspicious_file_extension_lookup file_name OU description = This macro limits the output to email attachments that have suspicious extensions [suspicious_writes] -definition = lookup suspicious_writes_lookup file as file_name OUTPUT note as "Reference" | search "Reference" != False +definition = lookup suspicious_writes_lookup file as file_name OUTPUT note as "Reference" | search "Reference" != False description = This macro limites the output to file names that have been marked as suspicious [sysmon] @@ -300,7 +316,7 @@ definition = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR s description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [system_network_configuration_discovery_tools] -definition = (process_name= "arp.exe" OR process_name= "at.exe" OR process_name= "attrib.exe" OR process_name= "cscript.exe" OR process_name= "dsquery.exe" OR process_name= "hostname.exe" OR process_name= "ipconfig.exe" OR process_name= "mimikatz.exe" OR process_name= "nbstat.exe" OR process_name= "net.exe" OR process_name= "netsh.exe" OR process_name= "nslookup.exe" OR process_name= "ping.exe" OR process_name= "quser.exe" OR process_name= "qwinsta.exe" OR process_name= "reg.exe" OR process_name= "runas.exe" OR process_name= "sc.exe" OR process_name= "schtasks.exe" OR process_name= "ssh.exe" OR process_name= "systeminfo.exe" OR process_name= "taskkill.exe" OR process_name= "telnet.exe" OR process_name= "tracert.exe" OR process_name="wscript.exe" OR process_name= "xcopy.exe") +definition = (process_name= "arp.exe" OR process_name= "at.exe" OR process_name= "attrib.exe" OR process_name= "cscript.exe" OR process_name= "dsquery.exe" OR process_name= "hostname.exe" OR process_name= "ipconfig.exe" OR process_name= "mimikatz.exe" OR process_name= "nbstat.exe" OR process_name= "net.exe" OR process_name= "netsh.exe" OR process_name= "nslookup.exe" OR process_name= "ping.exe" OR process_name= "quser.exe" OR process_name= "qwinsta.exe" OR process_name= "reg.exe" OR process_name= "runas.exe" OR process_name= "sc.exe" OR process_name= "schtasks.exe" OR process_name= "ssh.exe" OR process_name= "systeminfo.exe" OR process_name= "taskkill.exe" OR process_name= "telnet.exe" OR process_name= "tracert.exe" OR process_name="wscript.exe" OR process_name= "xcopy.exe") description = This macro is a list of process that can be used to discover the network configuration [uncommon_processes] @@ -316,15 +332,15 @@ definition = eventtype=wineventlog_system description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [wmi] -definition = sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" +definition = sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [zeek_rpc] -definition = index=zeek sourcetype="zeek:rpc:json" +definition = index=zeek sourcetype="zeek:rpc:json" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [zeek_ssl] -definition = index=zeek sourcetype="zeek:ssl:json" +definition = index=zeek sourcetype="zeek:ssl:json" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. [7zip_commandline_to_smb_share_path_filter] @@ -1367,6 +1383,10 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[esentutl_sam_copy_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [eventvwr_uac_bypass_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. @@ -1607,14 +1627,30 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[github_commit_changes_in_master_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [gsuite_drive_share_in_external_email_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. +[gsuite_email_suspicious_subject_with_attachment_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[gsuite_email_with_known_abuse_web_service_link_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [gsuite_outbound_email_with_attachment_to_external_domain_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. +[gsuite_suspicious_shared_file_name_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [hide_user_account_from_sign_in_screen_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. @@ -1803,6 +1839,18 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[kubernetes_nginx_ingress_lfi_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_nginx_ingress_rfi_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_scanner_image_pulling_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [large_volume_of_dns_any_queries_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. @@ -2063,6 +2111,10 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[powershell_4104_hunting_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [powershell_domain_enumeration_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. diff --git a/dist/escu/default/savedsearches.conf b/dist/escu/default/savedsearches.conf index 1f0366c27a..f83d0a45e2 100644 --- a/dist/escu/default/savedsearches.conf +++ b/dist/escu/default/savedsearches.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security_content -# On Date: 2021-08-20T22:00:44 UTC +# On Date: 2021-08-27T14:05:22 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -55,12 +55,12 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule] action.escu = 0 action.escu.enabled = 1 -description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} action.escu.data_models = [] -action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 @@ -82,8 +82,8 @@ action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] -action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen City action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -96,17 +96,17 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter` +search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search City=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by City | eval newCity=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCity=1 | table City] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, City, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_city_filter` [ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule] action.escu = 0 action.escu.enabled = 1 -description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} action.escu.data_models = [] -action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 @@ -124,8 +124,8 @@ action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] -action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen Country action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -138,17 +138,17 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter` +search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_country_filter` [ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] action.escu = 0 action.escu.enabled = 1 -description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} action.escu.data_models = [] -action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 @@ -166,8 +166,8 @@ action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 1"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] -action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen IP Address action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -180,17 +180,17 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` +search = `cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_ip_address_filter` [ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] action.escu = 0 action.escu.enabled = 1 -description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} action.escu.data_models = [] -action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2018-03-16 action.escu.modification_date = 2018-03-16 @@ -208,8 +208,8 @@ action.correlationsearch.label = ESCU - Deprecated - AWS Cloud Provisioning From action.correlationsearch.annotations = {"analytic_story": ["AWS Suspicious Provisioning Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] -action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = AWS Cloud Provisioning From Previously Unseen Region action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -222,7 +222,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter` +search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Region=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Region | eval newRegion=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newRegion=1 | table Region] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Region, eventName, errorCode | `aws_cloud_provisioning_from_previously_unseen_region_filter` [ESCU - AWS Create Policy Version to allow all resources - Rule] action.escu = 0 @@ -261,7 +261,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success | spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | search key_policy_action_1 = "*" | stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added by eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_create_policy_version_to_allow_all_resources_filter` +search = `cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success | spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | search key_policy_action_1 = "*" | stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added by eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_create_policy_version_to_allow_all_resources_filter` [ESCU - AWS CreateAccessKey - Rule] action.escu = 0 @@ -349,7 +349,7 @@ action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on O action.escu.data_models = ["Authentication"] action.escu.eli5 = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. -action.escu.known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. +action.escu.known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. action.escu.creation_date = 2020-05-28 action.escu.modification_date = 2020-05-28 action.escu.confidence = high @@ -378,7 +378,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") | where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter` +search = | tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") | where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter` [ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule] action.escu = 0 @@ -417,7 +417,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` +search = `cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter` [ESCU - AWS Detect Users with KMS keys performing encryption S3 - Rule] action.escu = 0 @@ -456,7 +456,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` +search = `cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter` [ESCU - AWS ECR Container Scanning Findings High - Rule] action.escu = 0 @@ -755,7 +755,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get* | stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName values(src) as src values(userAgent) as userAgent by user userIdentity.arn | where dc_events > 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_excessive_security_scanning_filter` +search = `cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get* | stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName values(src) as src values(userAgent) as userAgent by user userIdentity.arn | where dc_events > 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_excessive_security_scanning_filter` [ESCU - AWS IAM AccessDenied Discovery Events - Rule] action.escu = 0 @@ -794,7 +794,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) | bucket _time span=1h | stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources values(userIdentity.arn) by src_ip, userIdentity.arn, _time | where failures >= 5 and methods >= 1 and sources >= 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_accessdenied_discovery_events_filter` +search = `cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) | bucket _time span=1h | stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources values(userIdentity.arn) by src_ip, userIdentity.arn, _time | where failures >= 5 and methods >= 1 and sources >= 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_accessdenied_discovery_events_filter` [ESCU - AWS IAM Assume Role Policy Brute Force - Rule] action.escu = 0 @@ -833,7 +833,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn | where count >= 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_assume_role_policy_brute_force_filter` +search = `cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn | where count >= 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_assume_role_policy_brute_force_filter` [ESCU - AWS IAM Delete Policy - Rule] action.escu = 0 @@ -960,7 +960,7 @@ action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on O action.escu.data_models = [] action.escu.eli5 = The search looks for AWS CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your AWS CloudTrail inputs. -action.escu.known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. +action.escu.known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. action.escu.creation_date = 2021-01-11 action.escu.modification_date = 2021-01-11 action.escu.confidence = high @@ -989,7 +989,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter` +search = `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter` [ESCU - AWS Network Access Control List Deleted - Rule] action.escu = 0 @@ -999,7 +999,7 @@ action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on O action.escu.data_models = [] action.escu.eli5 = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the AWS CloudTrail logs to detect users deleting network ACLs. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. -action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. +action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. action.escu.creation_date = 2021-01-12 action.escu.modification_date = 2021-01-12 action.escu.confidence = high @@ -1228,7 +1228,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter` +search = `cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev | `abnormally_high_aws_instances_launched_by_user_filter` [ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule] action.escu = 0 @@ -1259,7 +1259,7 @@ action.correlationsearch.label = ESCU - Deprecated - Abnormally High AWS Instanc action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining", "Suspicious AWS EC2 Activities"], "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user successfully launches an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = Abnormally High AWS Instances Launched by User - MLTK action.notable.param.security_domain = network @@ -1273,7 +1273,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 +search = `cloudtrail` eventName=RunInstances errorCode=success `abnormally_high_aws_instances_launched_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | apply ec2_excessive_runinstances_v1 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 [ESCU - Abnormally High AWS Instances Terminated by User - Rule] action.escu = 0 @@ -1313,7 +1313,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter` +search = `cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev | `abnormally_high_aws_instances_terminated_by_user_filter` [ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule] action.escu = 0 @@ -1340,7 +1340,7 @@ action.correlationsearch.label = ESCU - Deprecated - Abnormally High AWS Instanc action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS EC2 Activities"], "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user successfully terminates an abnormally high number of instances. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = Abnormally High AWS Instances Terminated by User - MLTK action.notable.param.security_domain = network @@ -1354,7 +1354,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 +search = `cloudtrail` eventName=TerminateInstances errorCode=success `abnormally_high_aws_instances_terminated_by_user___mltk_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | apply ec2_excessive_terminateinstances_v1 | rename "IsOutlier(instances_terminated)" as isOutlier | where isOutlier=1 [ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule] action.escu = 0 @@ -1393,7 +1393,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` +search = | tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter` [ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule] action.escu = 0 @@ -1432,7 +1432,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter` +search = | tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter` [ESCU - Abnormally High Number Of Cloud Instances Launched - Rule] action.escu = 0 @@ -1471,7 +1471,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter` +search = | tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter` [ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule] action.escu = 0 @@ -1510,7 +1510,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter` +search = | tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter` [ESCU - Access LSASS Memory for Dump Creation - Rule] action.escu = 0 @@ -1541,7 +1541,7 @@ action.correlationsearch.label = ESCU - Access LSASS Memory for Dump Creation - action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 6", "CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "TargetImage", "role": ["Target"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = Detect memory dumping of the LSASS process. action.notable.param.rule_title = Access LSASS Memory for Dump Creation action.notable.param.security_domain = endpoint @@ -1586,7 +1586,7 @@ action.correlationsearch.label = ESCU - Account Discovery With Net App - Rule action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "IcedID"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 10, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_name", "role": ["Process"], "type": "Process Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = this search is to detect a potential account discovery series of command used by several malware or attack to recon the target machine. This technique is also seen in some note worthy malware like trickbot where it runs a cmd process, or even drop its module that will execute the said series of net command. This series of command are good correlation search and indicator of attacker recon if seen in the machines within a none technical user or department (HR, finance, ceo and etc) network. action.notable.param.rule_title = Account Discovery With Net App action.notable.param.security_domain = endpoint @@ -1600,7 +1600,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND (Processes.process="*user*" OR Processes.process="*config*" OR Processes.process="*view /all*") by Processes.process_name Processes.dest Processes.user Processes.parent_process_name | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `account_discovery_with_net_app_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND (Processes.process="*user*" OR Processes.process="*config*" OR Processes.process="*view /all*") by Processes.process_name Processes.dest Processes.user Processes.parent_process_name | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `account_discovery_with_net_app_filter` [ESCU - Allow File And Printing Sharing In Firewall - Rule] action.escu = 0 @@ -1627,7 +1627,7 @@ action.correlationsearch.label = ESCU - Allow File And Printing Sharing In Firew action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.007"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a suspicious modification of firewall to allow file and printer sharing. This technique was seen in ransomware to be able to discover more machine connected to the compromised host to encrypt more files action.notable.param.rule_title = Allow File And Printing Sharing In Firewall action.notable.param.security_domain = endpoint @@ -1641,7 +1641,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" Processes.process= "*group=\"File and Printer Sharing\"*" Processes.process="*enable=Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_file_and_printing_sharing_in_firewall_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" Processes.process= "*group=\"File and Printer Sharing\"*" Processes.process="*enable=Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_file_and_printing_sharing_in_firewall_filter` [ESCU - Allow Inbound Traffic By Firewall Rule Registry - Rule] action.escu = 0 @@ -1672,7 +1672,7 @@ action.correlationsearch.label = ESCU - Allow Inbound Traffic By Firewall Rule R action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 10, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic detects a potential suspicious modification of firewall rule registry allowing inbound traffic in specific port with public profile. This technique was identified when an adversary wants to grant remote access to a machine by allowing the traffic in a firewall rule. action.notable.param.rule_title = Allow Inbound Traffic By Firewall Rule Registry action.notable.param.security_domain = endpoint @@ -1686,7 +1686,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.registry_value_name = "*|Action=Allow|*" Registry.registry_value_name = "*|Dir=In|*" Registry.registry_value_name = "*|Profile=Public|*" Registry.registry_value_name = "*|LPort=*" by Registry.registry_path Registry.registry_key_name Registry.user Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `allow_inbound_traffic_by_firewall_rule_registry_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.registry_value_name = "*|Action=Allow|*" Registry.registry_value_name = "*|Dir=In|*" Registry.registry_value_name = "*|Profile=Public|*" Registry.registry_value_name = "*|LPort=*" by Registry.registry_path Registry.registry_key_name Registry.user Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `allow_inbound_traffic_by_firewall_rule_registry_filter` [ESCU - Allow Inbound Traffic In Firewall Rule - Rule] action.escu = 0 @@ -1757,7 +1757,7 @@ action.correlationsearch.label = ESCU - Allow Network Discovery In Firewall - Ru action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.007"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a suspicious modification to the firewall to allow network discovery on a machine. This technique was seen in couple of ransomware (revil, reddot) to discover other machine connected to the compromised host to encrypt more files. action.notable.param.rule_title = Allow Network Discovery In Firewall action.notable.param.security_domain = endpoint @@ -1771,7 +1771,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" Processes.process= "*group=\"Network Discovery\"*" Processes.process="*enable*" Processes.process="*Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_network_discovery_in_firewall_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" Processes.process= "*group=\"Network Discovery\"*" Processes.process="*enable*" Processes.process="*Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_network_discovery_in_firewall_filter` [ESCU - Allow Operation with Consent Admin - Rule] action.escu = 0 @@ -1802,7 +1802,7 @@ action.correlationsearch.label = ESCU - Allow Operation with Consent Admin - Rul action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This analytic identifies a potential privilege escalation attempt to perform malicious task. This registry modification is designed to allow the `Consent Admin` to perform an operation that requires elevation without consent or credentials. We also found this in some attacker to gain privilege escalation to the compromise machine. action.notable.param.rule_title = Allow Operation with Consent Admin action.notable.param.security_domain = endpoint @@ -1816,15 +1816,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*" Registry.registry_key_name = ConsentPromptBehaviorAdmin Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `allow_operation_with_consent_admin_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*" Registry.registry_key_name = ConsentPromptBehaviorAdmin Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `allow_operation_with_consent_admin_filter` [ESCU - Amazon EKS Kubernetes Pod scan detection - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API +description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]} action.escu.data_models = [] -action.escu.eli5 = This search provides detection information on unauthenticated requests against Kubernetes' Pods API +action.escu.eli5 = This search provides detection information on unauthenticated requests against Kubernetes' Pods API action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. action.escu.creation_date = 2020-04-15 @@ -1843,7 +1843,7 @@ action.correlationsearch.label = ESCU - Amazon EKS Kubernetes Pod scan detection action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API +action.notable.param.rule_description = This search provides detection information on unauthenticated requests against Kubernetes' Pods API action.notable.param.rule_title = Amazon EKS Kubernetes Pod scan detection action.notable.param.security_domain = threat action.notable.param.severity = high @@ -1856,7 +1856,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` +search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter` [ESCU - Amazon EKS Kubernetes cluster scan detection - Rule] action.escu = 0 @@ -1896,7 +1896,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` +search = `aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter` [ESCU - Anomalous usage of 7zip - Rule] action.escu = 0 @@ -1927,7 +1927,7 @@ action.correlationsearch.label = ESCU - Anomalous usage of 7zip - Rule action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike", "NOBELIUM Group"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 80, "kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1560.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies a 7z.exe spawned from `Rundll32.exe` or `Dllhost.exe`. It is assumed that the adversary has brought in `7z.exe` and `7z.dll`. It has been observed where an adversary will rename `7z.exe`. Additional coverage may be required to identify the behavior of renamed instances of `7z.exe`. During triage, identify the source of injection into `Rundll32.exe` or `Dllhost.exe`. Capture any files written to disk and analyze as needed. Review parallel processes for additional behaviors. Typically, archiving files will result in exfiltration. action.notable.param.rule_title = Anomalous usage of 7zip action.notable.param.security_domain = endpoint @@ -1941,7 +1941,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `anomalous_usage_of_7zip_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `anomalous_usage_of_7zip_filter` [ESCU - Any Powershell DownloadFile - Rule] action.escu = 0 @@ -1972,7 +1972,7 @@ action.correlationsearch.label = ESCU - Any Powershell DownloadFile - Rule action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Ingress Tool Transfer"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Exploitation"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of PowerShell downloading a file using `DownloadFile` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant. action.notable.param.rule_title = Any Powershell DownloadFile action.notable.param.security_domain = endpoint @@ -2017,7 +2017,7 @@ action.correlationsearch.label = ESCU - Any Powershell DownloadString - Rule action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "HAFNIUM Group", "Ingress Tool Transfer"], "confidence": 70, "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of PowerShell downloading a file using `DownloadString` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant. action.notable.param.rule_title = Any Powershell DownloadString action.notable.param.security_domain = endpoint @@ -2062,7 +2062,7 @@ action.correlationsearch.label = ESCU - Attacker Tools On Endpoint - Rule action.correlationsearch.annotations = {"analytic_story": ["Monitor for Unauthorized Software", "XMRig", "SamSam Ransomware", "Unusual Processes"], "cis20": ["CIS 2"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Exploitation", "Stage:Recon", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1036.005", "T1595", "T1003"], "nist": ["ID.AM", "PR.DS"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process", "Attacker"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for execution of commonly used attacker tools on an endpoint. action.notable.param.rule_title = Attacker Tools On Endpoint action.notable.param.security_domain = endpoint @@ -2085,7 +2085,7 @@ description = Attempt To Add Certificate To Untrusted Store action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1553.004"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Attempt To Add Certificate To Untrusted Store -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. action.escu.creation_date = 2020-11-03 action.escu.modification_date = 2020-11-03 @@ -2107,7 +2107,7 @@ action.correlationsearch.label = ESCU - Attempt To Add Certificate To Untrusted action.correlationsearch.annotations = {"analytic_story": ["Disabling Security Tools"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1553.004"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Attempt To Add Certificate To Untrusted Store action.notable.param.rule_title = Attempt To Add Certificate To Untrusted Store action.notable.param.security_domain = endpoint @@ -2121,7 +2121,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter` [ESCU - Attempt To Stop Security Service - Rule] action.escu = 0 @@ -2152,7 +2152,7 @@ action.correlationsearch.label = ESCU - Attempt To Stop Security Service - Rule action.correlationsearch.annotations = {"analytic_story": ["Disabling Security Tools", "Trickbot"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for attempts to stop security-related services on the endpoint. action.notable.param.rule_title = Attempt To Stop Security Service action.notable.param.security_domain = endpoint @@ -2166,7 +2166,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter` [ESCU - Attempted Credential Dump From Registry via Reg exe - Rule] action.escu = 0 @@ -2175,7 +2175,7 @@ description = Monitor for execution of reg.exe with parameters specifying an exp action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.002"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified. action.escu.creation_date = 2019-12-02 action.escu.modification_date = 2019-12-02 @@ -2197,7 +2197,7 @@ action.correlationsearch.label = ESCU - Attempted Credential Dump From Registry action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "DarkSide Ransomware"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.002"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. action.notable.param.rule_title = Attempted Credential Dump From Registry via Reg exe action.notable.param.security_domain = endpoint @@ -2242,7 +2242,7 @@ action.correlationsearch.label = ESCU - BCDEdit Failure Recovery Modification - action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware", "Ransomware"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Impact"], "impact": 100, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery. action.notable.param.rule_title = BCDEdit Failure Recovery Modification action.notable.param.security_domain = endpoint @@ -2256,7 +2256,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter` [ESCU - BITS Job Persistence - Rule] action.escu = 0 @@ -2287,7 +2287,7 @@ action.correlationsearch.label = ESCU - BITS Job Persistence - Rule action.correlationsearch.annotations = {"analytic_story": ["BITS Jobs"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` scheduling a BITS job to persist on an endpoint. The query identifies the parameters used to create, resume or add a file to a BITS job. Typically seen combined in a oneliner or ran in sequence. If identified, review the BITS job created and capture any files written to disk. It is possible for BITS to be used to upload files and this may require further network data analysis to identify. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. action.notable.param.rule_title = BITS Job Persistence action.notable.param.security_domain = endpoint @@ -2306,10 +2306,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - BITSAdmin Download File - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. +description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197", "T1105"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. +action.escu.eli5 = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Limited false positives, however it may be required to filter based on parent process name or network connection. action.escu.creation_date = 2021-03-26 @@ -2332,8 +2332,8 @@ action.correlationsearch.label = ESCU - BITSAdmin Download File - Rule action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "BITS Jobs", "DarkSide Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197", "T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. action.notable.param.rule_title = BITSAdmin Download File action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -2356,7 +2356,7 @@ action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], " action.escu.data_models = ["Endpoint"] action.escu.eli5 = The search looks for a batch file (.bat) written to the Windows system directory tree. action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -action.escu.known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. +action.escu.known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. action.escu.creation_date = 2018-12-14 action.escu.modification_date = 2018-12-14 action.escu.confidence = high @@ -2377,7 +2377,7 @@ action.correlationsearch.label = ESCU - Batch File Write to System32 - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1204.002"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The search looks for a batch file (.bat) written to the Windows system directory tree. action.notable.param.rule_title = Batch File Write to System32 action.notable.param.security_domain = endpoint @@ -2391,7 +2391,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?<file_extension>\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat | `batch_file_write_to_system32_filter` [ESCU - CHCP Command Execution - Rule] action.escu = 0 @@ -2422,7 +2422,7 @@ action.correlationsearch.label = ESCU - CHCP Command Execution - Rule action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Recon"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect execution of chcp.exe application. this utility is used to change the active code page of the console. This technique was seen in icedid malware to know the locale region/language/country of the compromise host. action.notable.param.rule_title = CHCP Command Execution action.notable.param.security_domain = endpoint @@ -2441,10 +2441,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - CMD Echo Pipe - Escalation - Rule] action.escu = 0 action.escu.enabled = 1 -description = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. +description = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. action.escu.mappings = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1059.003", "T1543.003"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. +action.escu.eli5 = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Unknown. It is possible filtering may be required to ensure fidelity. action.escu.creation_date = 2021-05-20 @@ -2467,8 +2467,8 @@ action.correlationsearch.label = ESCU - CMD Echo Pipe - Escalation - Rule action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1059.003", "T1543.003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. action.notable.param.rule_title = CMD Echo Pipe - Escalation action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -2525,7 +2525,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=7 ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", "*\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_filter` +search = `sysmon` EventCode=7 ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", "*\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_filter` [ESCU - CertUtil Download With URLCache and Split Arguments - Rule] action.escu = 0 @@ -2556,7 +2556,7 @@ action.correlationsearch.label = ESCU - CertUtil Download With URLCache and Spli action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "DarkSide Ransomware"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Command and Control"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Certutil.exe may download a file from a remote destination using `-urlcache`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. However, it is uncommon for `certutil.exe` to write files to world writeable paths.\ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. action.notable.param.rule_title = CertUtil Download With URLCache and Split Arguments action.notable.param.security_domain = endpoint @@ -2575,10 +2575,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - CertUtil Download With VerifyCtl and Split Arguments - Rule] action.escu = 0 action.escu.enabled = 1 -description = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. +description = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\`. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. +action.escu.eli5 = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\`. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection. action.escu.creation_date = 2021-03-23 @@ -2601,8 +2601,8 @@ action.correlationsearch.label = ESCU - CertUtil Download With VerifyCtl and Spl action.correlationsearch.annotations = {"analytic_story": ["Ingress Tool Transfer", "DarkSide Ransomware"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Command and Control"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\`. action.notable.param.rule_title = CertUtil Download With VerifyCtl and Split Arguments action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -2646,7 +2646,7 @@ action.correlationsearch.label = ESCU - CertUtil With Decode Argument - Rule action.correlationsearch.annotations = {"analytic_story": ["Deobfuscate-Decode Files or Information"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1140"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = CertUtil.exe may be used to `encode` and `decode` a file, including PE and script code. Encoding will convert a file to base64 with `-----BEGIN CERTIFICATE-----` and `-----END CERTIFICATE-----` tags. Malicious usage will include decoding a encoded file that was downloaded. Once decoded, it will be loaded by a parallel process. Note that there are two additional command switches that may be used - `encodehex` and `decodehex`. Similarly, the file will be encoded in HEX and later decoded for further execution. During triage, identify the source of the file being decoded. Review its contents or execution behavior for further analysis. action.notable.param.rule_title = CertUtil With Decode Argument action.notable.param.security_domain = endpoint @@ -2691,7 +2691,7 @@ action.correlationsearch.label = ESCU - Certutil exe certificate extraction - Ru action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques", "Cloud Federated Credential Abuse"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 90, "kill_chain_phases": ["Installation"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS. action.notable.param.rule_title = Certutil exe certificate extraction action.notable.param.security_domain = endpoint @@ -2705,7 +2705,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id| `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "*-exportPFX*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `certutil_exe_certificate_extraction_filter` [ESCU - Child Processes of Spoolsv exe - Rule] action.escu = 0 @@ -2714,7 +2714,7 @@ description = This search looks for child processes of spoolsv.exe. This activit action.escu.mappings = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. action.escu.known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 @@ -2732,7 +2732,7 @@ action.correlationsearch.label = ESCU - Child Processes of Spoolsv exe - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Privilege Escalation"], "cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. action.notable.param.rule_title = Child Processes of Spoolsv exe action.notable.param.security_domain = endpoint @@ -2777,7 +2777,7 @@ action.correlationsearch.label = ESCU - Clear Unallocated Sector Using Cipher Ap action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Impact"], "impact": 100, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070.004"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = this search is to detect execution of `cipher.exe` to clear the unallocated sectors of a specific disk. This technique was seen in some ransomware to make it impossible to forensically recover deleted files. action.notable.param.rule_title = Clear Unallocated Sector Using Cipher App action.notable.param.security_domain = endpoint @@ -2791,7 +2791,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cipher.exe" Processes.process = "*/w:*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clear_unallocated_sector_using_cipher_app_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cipher.exe" Processes.process = "*/w:*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clear_unallocated_sector_using_cipher_app_filter` [ESCU - Clients Connecting to Multiple DNS Servers - Rule] action.escu = 0 @@ -2801,9 +2801,9 @@ action.escu.mappings = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phas action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. action.escu.how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ -This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ +This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -action.escu.known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. +action.escu.known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -2820,7 +2820,7 @@ action.correlationsearch.label = ESCU - Deprecated - Clients Connecting to Multi action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"], "cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. action.notable.param.rule_title = Clients Connecting to Multiple DNS Servers action.notable.param.security_domain = network @@ -2834,15 +2834,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` +search = | tstats `security_content_summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 | `clients_connecting_to_multiple_dns_servers_filter` [ESCU - Clop Common Exec Parameter - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. +description = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. action.escu.mappings = {"kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1204"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. +action.escu.eli5 = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Operators can execute third party tools using these parameters. action.escu.creation_date = 2021-03-17 @@ -2865,8 +2865,8 @@ action.correlationsearch.label = ESCU - Clop Common Exec Parameter - Rule action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 100, "kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1204"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. action.notable.param.rule_title = Clop Common Exec Parameter action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -2879,7 +2879,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*" Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*" Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter` [ESCU - Clop Ransomware Known Service Name - Rule] action.escu = 0 @@ -2923,7 +2923,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter` +search = `wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter` [ESCU - Cloud API Calls From Previously Unseen User Roles - Rule] action.escu = 0 @@ -2962,7 +2962,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter` +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter` [ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule] action.escu = 0 @@ -2971,8 +2971,8 @@ description = This search looks for cloud compute instances created by users who action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} action.escu.data_models = ["Change"] action.escu.eli5 = This search looks for cloud compute instances created by users who have not created them before. -action.escu.how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. -action.escu.known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. +action.escu.how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. +action.escu.known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. action.escu.creation_date = 2021-07-13 action.escu.modification_date = 2021-07-13 action.escu.confidence = high @@ -3001,7 +3001,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter` +search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter` [ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule] action.escu = 0 @@ -3011,7 +3011,7 @@ action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on O action.escu.data_models = ["Change"] action.escu.eli5 = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. -action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. +action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. action.escu.creation_date = 2020-09-02 action.escu.modification_date = 2020-09-02 action.escu.confidence = high @@ -3040,7 +3040,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter` +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter` [ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule] action.escu = 0 @@ -3079,7 +3079,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter` +search = | tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter` [ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule] action.escu = 0 @@ -3118,7 +3118,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` [ESCU - Cloud Instance Modified By Previously Unseen User - Rule] action.escu = 0 @@ -3127,8 +3127,8 @@ description = This search looks for cloud instances being modified by users who action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} action.escu.data_models = ["Change"] action.escu.eli5 = This search looks for cloud instances being modified by users who have not previously modified them. -action.escu.how_to_implement = This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. -action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. +action.escu.how_to_implement = This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. +action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. action.escu.creation_date = 2020-07-29 action.escu.modification_date = 2020-07-29 action.escu.confidence = high @@ -3157,17 +3157,17 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter` +search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter` [ESCU - Cloud Network Access Control List Deleted - Rule] action.escu = 0 action.escu.enabled = 1 -description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate +description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} action.escu.data_models = [] -action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate +action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. -action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. +action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. action.escu.creation_date = 2020-09-08 action.escu.modification_date = 2020-09-08 action.escu.confidence = high @@ -3184,8 +3184,8 @@ action.correlationsearch.label = ESCU - Deprecated - Cloud Network Access Contro action.correlationsearch.annotations = {"analytic_story": ["Cloud Network ACL Activity"], "cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['src'] -action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate +action.notable.param.nes_fields = ['src'] +action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate action.notable.param.rule_title = Cloud Network Access Control List Deleted action.notable.param.security_domain = network action.notable.param.severity = high @@ -3208,7 +3208,7 @@ action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": [ action.escu.data_models = ["Change"] action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2020-10-09 action.escu.modification_date = 2020-10-09 @@ -3238,7 +3238,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)` +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter` | `security_content_ctime(firstTime)` [ESCU - Cloud Provisioning Activity From Previously Unseen Country - Rule] action.escu = 0 @@ -3248,7 +3248,7 @@ action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": [ action.escu.data_models = ["Change"] action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2020-10-09 action.escu.modification_date = 2020-10-09 @@ -3278,7 +3278,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)` +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter` | `security_content_ctime(firstTime)` [ESCU - Cloud Provisioning Activity From Previously Unseen IP Address - Rule] action.escu = 0 @@ -3288,7 +3288,7 @@ action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": [ action.escu.data_models = ["Change"] action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2020-08-16 action.escu.modification_date = 2020-08-16 @@ -3318,7 +3318,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)` +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` | `security_content_ctime(firstTime)` [ESCU - Cloud Provisioning Activity From Previously Unseen Region - Rule] action.escu = 0 @@ -3328,7 +3328,7 @@ action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": [ action.escu.data_models = ["Change"] action.escu.eli5 = This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. action.escu.how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. action.escu.creation_date = 2020-08-16 action.escu.modification_date = 2020-08-16 @@ -3358,7 +3358,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)` +search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter` | `security_content_ctime(firstTime)` [ESCU - Cobalt Strike Named Pipes - Rule] action.escu = 0 @@ -3391,7 +3391,7 @@ action.correlationsearch.label = ESCU - Cobalt Strike Named Pipes - Rule action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike", "Trickbot", "DarkSide Ransomware"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1055"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Parent Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies the use of default or publicly known named pipes used with Cobalt Strike. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. Cobalt Strike uses named pipes in many ways and has default values used with the Artifact Kit and Malleable C2 Profiles. The following query assists with identifying these default named pipes. Each EDR product presents named pipes a little different. Consider taking the values and generating a query based on the product of choice. \ Upon triage, review the process performing the named pipe. If it is explorer.exe, It is possible it was injected into by another process. Review recent parallel processes to identify suspicious patterns or behaviors. A parallel process may have a network connection, review and follow the connection back to identify any file modifications. action.notable.param.rule_title = Cobalt Strike Named Pipes @@ -3416,7 +3416,7 @@ action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Ob action.escu.data_models = ["Endpoint"] action.escu.eli5 = The search looks for file modifications with extensions commonly used by Ransomware action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ -This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ 1. \ 1. **Label:** File Extension, **Field:** file_extension\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` @@ -3441,7 +3441,7 @@ action.correlationsearch.label = ESCU - Common Ransomware Extensions - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware", "Clop Ransomware"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The search looks for file modifications with extensions commonly used by Ransomware action.notable.param.rule_title = Common Ransomware Extensions action.notable.param.security_domain = endpoint @@ -3455,7 +3455,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?<file_extension>\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` | `common_ransomware_extensions_filter` [ESCU - Common Ransomware Notes - Rule] action.escu = 0 @@ -3465,7 +3465,7 @@ action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Ob action.escu.data_models = ["Endpoint"] action.escu.eli5 = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. action.escu.how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -action.escu.known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. +action.escu.known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. action.escu.creation_date = 2020-11-09 action.escu.modification_date = 2020-11-09 action.escu.confidence = high @@ -3486,7 +3486,7 @@ action.correlationsearch.label = ESCU - Common Ransomware Notes - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ransomware", "Ryuk Ransomware", "Clop Ransomware"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. action.notable.param.rule_title = Common Ransomware Notes action.notable.param.security_domain = endpoint @@ -3531,7 +3531,7 @@ action.correlationsearch.label = ESCU - Conti Common Exec parameter - Rule action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search detects the suspicious commandline argument of revil ransomware to encrypt specific or all local drive and network shares of the compromised machine or host. action.notable.param.rule_title = Conti Common Exec parameter action.notable.param.security_domain = endpoint @@ -3545,7 +3545,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*" OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process = "*-nomutex*" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `conti_common_exec_parameter_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*" OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process = "*-nomutex*" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `conti_common_exec_parameter_filter` [ESCU - Create Remote Thread In Shell Application - Rule] action.escu = 0 @@ -3589,7 +3589,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*") | stats count min(_time) as firstTime max(_time) as lastTime by TargetImage TargetProcessId SourceProcessId EventCode StartAddress SourceImage Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_remote_thread_in_shell_application_filter` +search = `sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*") | stats count min(_time) as firstTime max(_time) as lastTime by TargetImage TargetProcessId SourceProcessId EventCode StartAddress SourceImage Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_remote_thread_in_shell_application_filter` [ESCU - Create Remote Thread into LSASS - Rule] action.escu = 0 @@ -3620,7 +3620,7 @@ action.correlationsearch.label = ESCU - Create Remote Thread into LSASS - Rule action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"], "observable": [{"name": "TargetImage", "role": ["Other"], "type": "Other"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = Detect remote thread creation into LSASS consistent with credential dumping. action.notable.param.rule_title = Create Remote Thread into LSASS action.notable.param.security_domain = endpoint @@ -3639,10 +3639,10 @@ search = `sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as f [ESCU - Create Service In Suspicious File Path - Rule] action.escu = 0 action.escu.enabled = 1 -description = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. +description = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. action.escu.mappings = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1569.002"]} action.escu.data_models = [] -action.escu.eli5 = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. +action.escu.eli5 = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-03-12 @@ -3665,8 +3665,8 @@ action.correlationsearch.label = ESCU - Create Service In Suspicious File Path - action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1569.002"], "observable": [{"name": "Service_File_Name", "role": ["Other"], "type": "Other"}, {"name": "Service_Name", "role": ["Other"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] -action.notable.param.rule_description = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. action.notable.param.rule_title = Create Service In Suspicious File Path action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -3679,7 +3679,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_system` EventCode=7045 Service_File_Name = "*\.exe" NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) Service_Type = "user mode service" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_service_in_suspicious_file_path_filter` +search = `wineventlog_system` EventCode=7045 Service_File_Name = "*\.exe" NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) Service_Type = "user mode service" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_service_in_suspicious_file_path_filter` [ESCU - Create local admin accounts using net exe - Rule] action.escu = 0 @@ -3688,7 +3688,7 @@ description = This search looks for the creation of local administrator accounts action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.001"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for the creation of local administrator accounts using net.exe. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators often leverage net.exe to create admin accounts. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -3710,7 +3710,7 @@ action.correlationsearch.label = ESCU - Create local admin accounts using net ex action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for the creation of local administrator accounts using net.exe. action.notable.param.rule_title = Create local admin accounts using net exe action.notable.param.security_domain = endpoint @@ -3733,7 +3733,7 @@ description = This search looks for the creation or deletion of hidden shares us action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.005"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for the creation or deletion of hidden shares using net.exe. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -3755,7 +3755,7 @@ action.correlationsearch.label = ESCU - Create or delete windows shares using ne action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for the creation or deletion of hidden shares using net.exe. action.notable.param.rule_title = Create or delete windows shares using net exe action.notable.param.security_domain = endpoint @@ -3778,7 +3778,7 @@ description = Monitor for signs that Vssadmin or Wmic has been used to create a action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Legitimate administrator usage of Vssadmin or Wmic will create false positives. action.escu.creation_date = 2019-12-10 action.escu.modification_date = 2019-12-10 @@ -3800,7 +3800,7 @@ action.correlationsearch.label = ESCU - Creation of Shadow Copy - Rule action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. action.notable.param.rule_title = Creation of Shadow Copy action.notable.param.security_domain = endpoint @@ -3845,7 +3845,7 @@ action.correlationsearch.label = ESCU - Creation of Shadow Copy with wmic and po action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search detects the use of wmic and Powershell to create a shadow copy. action.notable.param.rule_title = Creation of Shadow Copy with wmic and powershell action.notable.param.security_domain = endpoint @@ -3890,7 +3890,7 @@ action.correlationsearch.label = ESCU - Creation of lsass Dump with Taskmgr - Ru action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 6", "CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "TargetFilename", "role": ["Victim"], "type": "File Name"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = Detect the hands on keyboard behavior of Windows Task Manager creating a process dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp. action.notable.param.rule_title = Creation of lsass Dump with Taskmgr action.notable.param.security_domain = endpoint @@ -3913,7 +3913,7 @@ description = This search detects credential dumping using copy command from a s action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search detects credential dumping using copy command from a shadow copy. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = unknown action.escu.creation_date = 2019-12-10 action.escu.modification_date = 2019-12-10 @@ -3935,7 +3935,7 @@ action.correlationsearch.label = ESCU - Credential Dumping via Copy Command from action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search detects credential dumping using copy command from a shadow copy. action.notable.param.rule_title = Credential Dumping via Copy Command from Shadow Copy action.notable.param.security_domain = endpoint @@ -3958,7 +3958,7 @@ description = This search detects the creation of a symlink to a shadow copy. action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search detects the creation of a symlink to a shadow copy. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = unknown action.escu.creation_date = 2019-12-10 action.escu.modification_date = 2019-12-10 @@ -3980,7 +3980,7 @@ action.correlationsearch.label = ESCU - Credential Dumping via Symlink to Shadow action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 8", "CIS 16"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search detects the creation of a symlink to a shadow copy. action.notable.param.rule_title = Credential Dumping via Symlink to Shadow Copy action.notable.param.security_domain = endpoint @@ -4025,7 +4025,7 @@ action.correlationsearch.label = ESCU - DLLHost with no Command Line Arguments w action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_image", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies DLLHost.exe with no command line arguments with a network connection. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = DLLHost with no Command Line Arguments with Network action.notable.param.security_domain = endpoint @@ -4039,7 +4039,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(dllhost\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `dllhost_with_no_command_line_arguments_with_network_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(dllhost\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `dllhost_with_no_command_line_arguments_with_network_filter` [ESCU - DNS Exfiltration Using Nslookup App - Rule] action.escu = 0 @@ -4070,7 +4070,7 @@ action.correlationsearch.label = ESCU - DNS Exfiltration Using Nslookup App - Ru action.correlationsearch.annotations = {"analytic_story": ["Suspicious DNS Traffic", "Dynamic DNS", "Command and Control", "Data Exfiltration"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = this search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type, TXT, A, AAAA, that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries. action.notable.param.rule_title = DNS Exfiltration Using Nslookup App action.notable.param.security_domain = endpoint @@ -4084,7 +4084,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="-type=*" OR Processes.process="*-retry=*" by Processes.dest Processes.user Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dns_exfiltration_using_nslookup_app_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="-type=*" OR Processes.process="*-retry=*" by Processes.dest Processes.user Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dns_exfiltration_using_nslookup_app_filter` [ESCU - DNS Query Length Outliers - MLTK - Rule] action.escu = 0 @@ -4093,8 +4093,8 @@ description = This search allows you to identify DNS requests that are unusually action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. -action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ 1. **Label:** DNS Query Length, **Field:** query_length\ 1. \ @@ -4117,7 +4117,7 @@ action.correlationsearch.label = ESCU - DNS Query Length Outliers - MLTK - Rule action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"], "cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. action.notable.param.rule_title = DNS Query Length Outliers - MLTK action.notable.param.security_domain = network @@ -4131,7 +4131,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` +search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter` [ESCU - DNS Query Length With High Standard Deviation - Rule] action.escu = 0 @@ -4141,7 +4141,7 @@ action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Com action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. -action.escu.known_false_positives = It's possible there can be long domain names that are legitimate. +action.escu.known_false_positives = It's possible there can be long domain names that are legitimate. action.escu.creation_date = 2021-07-21 action.escu.modification_date = 2021-07-21 action.escu.confidence = high @@ -4175,7 +4175,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where NOT DNS.message_type IN("Pointer","PTR") by DNS.query | `drop_dm_object_name("DNS")` | eval query_length = len(query) | table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where NOT DNS.message_type IN("Pointer","PTR") by DNS.query | `drop_dm_object_name("DNS")` | eval query_length = len(query) | table query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter` [ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] action.escu = 0 @@ -4202,7 +4202,7 @@ action.correlationsearch.label = ESCU - Deprecated - DNS Query Requests Resolved action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. action.notable.param.rule_title = DNS Query Requests Resolved by Unauthorized DNS Servers action.notable.param.security_domain = network @@ -4216,7 +4216,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` | `dns_query_requests_resolved_by_unauthorized_dns_servers_filter` [ESCU - DNS record changed - Rule] action.escu = 0 @@ -4225,9 +4225,9 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. -action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ action.escu.known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. @@ -4247,7 +4247,7 @@ action.correlationsearch.label = ESCU - Deprecated - DNS record changed - Rule action.correlationsearch.annotations = {"analytic_story": ["DNS Hijacking"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['src'] +action.notable.param.nes_fields = ['src'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. action.notable.param.rule_title = DNS record changed action.notable.param.security_domain = network @@ -4261,22 +4261,22 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | inputlookup discovered_dns_records | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?<domain>\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter` +search = | inputlookup discovered_dns_records | rename answer as discovered_answer | join domain[|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter` [ESCU - DSQuery Domain Discovery - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ +description = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\ -The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ +The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\ The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\ In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ +action.escu.eli5 = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\ -The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ +The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\ The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\ In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used. @@ -4302,10 +4302,10 @@ action.correlationsearch.label = ESCU - DSQuery Domain Discovery - Rule action.correlationsearch.annotations = {"analytic_story": ["Domain Trust Discovery"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\ -The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ +The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\ The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\ In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used. @@ -4365,7 +4365,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message= "*ShadowCopy*" (Message = "*Delete*" OR Message = "*Remove*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `delete_shadowcopy_with_powershell_filter` +search = `powershell` EventCode=4104 Message= "*ShadowCopy*" (Message = "*Delete*" OR Message = "*Remove*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `delete_shadowcopy_with_powershell_filter` [ESCU - Deleting Of Net Users - Rule] action.escu = 0 @@ -4396,7 +4396,7 @@ action.correlationsearch.label = ESCU - Deleting Of Net Users - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic will detect a suspicious net.exe/net1.exe command-line to delete a user on a system. This technique may be use by an administrator for legitimate purposes, however this behavior has been used in the wild to impair some user or deleting adversaries tracks created during its lateral movement additional systems. During triage, review parallel processes for additional behavior. Identify any other user accounts created before or after. action.notable.param.rule_title = Deleting Of Net Users action.notable.param.security_domain = endpoint @@ -4410,7 +4410,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND Processes.process="*/delete*" by Processes.process_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `deleting_of_net_users_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND Processes.process="*/delete*" by Processes.process_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `deleting_of_net_users_filter` [ESCU - Deleting Shadow Copies - Rule] action.escu = 0 @@ -4419,7 +4419,7 @@ description = The vssadmin.exe utility is used to interact with the Volume Shado action.escu.mappings = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. action.escu.creation_date = 2020-11-09 action.escu.modification_date = 2020-11-09 @@ -4441,7 +4441,7 @@ action.correlationsearch.label = ESCU - Deleting Shadow Copies - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "SamSam Ransomware", "Ransomware", "Clop Ransomware"], "cis20": ["CIS 8", "CIS 10"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. action.notable.param.rule_title = Deleting Shadow Copies action.notable.param.security_domain = endpoint @@ -4465,7 +4465,7 @@ action.escu.mappings = {"cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ -This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ 1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ 1. \ @@ -4492,7 +4492,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect API activity from us action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. action.notable.param.rule_title = Detect API activity from users without MFA action.notable.param.security_domain = network @@ -4511,11 +4511,11 @@ search = `cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=fa [ESCU - Detect ARP Poisoning - Rule] action.escu = 0 action.escu.enabled = 1 -description = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. +description = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. action.escu.mappings = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = [] -action.escu.eli5 = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. -action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +action.escu.eli5 = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. +action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. action.escu.known_false_positives = This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). action.escu.creation_date = 2020-08-11 action.escu.modification_date = 2020-08-11 @@ -4533,7 +4533,7 @@ action.correlationsearch.label = ESCU - Detect ARP Poisoning - Rule action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['src_interface', 'firstTime', 'lastTime', 'count'] +action.notable.param.nes_fields = ['src_interface', 'firstTime', 'lastTime', 'count'] action.notable.param.rule_description = ARP Poisoning has been detected on interface $src_interface$ on host $orig_host$. This may be an indication of a MITM attack. action.notable.param.rule_title = ARP Poisoning Detected on $orig_host$ action.notable.param.security_domain = network @@ -4547,7 +4547,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter` +search = `cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter` [ESCU - Detect AWS API Activities From Unapproved Accounts - Rule] action.escu = 0 @@ -4556,14 +4556,14 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for successful AWS CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ -This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ +This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ 1. **Label:** First Time, **Field:** firstTime\ 1. \ 1. **Label:** Last Time, **Field:** lastTime\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -action.escu.known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. +action.escu.known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -4584,7 +4584,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect AWS API Activities F action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for successful AWS CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. action.notable.param.rule_title = Detect AWS API Activities From Unapproved Accounts action.notable.param.security_domain = access @@ -4637,7 +4637,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") |where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_aws_console_login_by_new_user_filter` +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | inputlookup append=t previously_seen_users_console_logins | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") |where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_aws_console_login_by_new_user_filter` [ESCU - Detect AWS Console Login by User from New City - Rule] action.escu = 0 @@ -4676,7 +4676,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user City | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user City | fields earliestseen user City] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user City userStatus userCity | `detect_aws_console_login_by_user_from_new_city_filter` +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user City | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user City | fields earliestseen user City] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCity = "New City" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user City userStatus userCity | `detect_aws_console_login_by_user_from_new_city_filter` [ESCU - Detect AWS Console Login by User from New Country - Rule] action.escu = 0 @@ -4715,7 +4715,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Country | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Country | fields earliestseen user Country] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Country userStatus userCountry | `detect_aws_console_login_by_user_from_new_country_filter` +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Country | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Country | fields earliestseen user Country] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") | where userCountry = "New Country" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Country userStatus userCountry | `detect_aws_console_login_by_user_from_new_country_filter` [ESCU - Detect AWS Console Login by User from New Region - Rule] action.escu = 0 @@ -4754,7 +4754,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Region | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Region | fields earliestseen user Region] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Region userStatus userRegion | `detect_aws_console_login_by_user_from_new_region_filter` +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | table firstTime lastTime user Region | join user type=outer [| inputlookup previously_seen_users_console_logins | stats earliest(firstTime) AS earliestseen by user Region | fields earliestseen user Region] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") | where userRegion = "New Region" AND userStatus != "Old User" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user Region userStatus userRegion | `detect_aws_console_login_by_user_from_new_region_filter` [ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] action.escu = 0 @@ -4785,7 +4785,7 @@ action.correlationsearch.label = ESCU - Detect Activity Related to Pass the Hash action.correlationsearch.annotations = {"analytic_story": ["Lateral Movement"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1550.002"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "EventCode", "role": ["Other"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. action.notable.param.rule_title = Detect Activity Related to Pass the Hash Attacks action.notable.param.security_domain = access @@ -4799,7 +4799,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` +search = `wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_activity_related_to_pass_the_hash_attacks_filter` [ESCU - Detect AzureHound Command-Line Arguments - Rule] action.escu = 0 @@ -4830,7 +4830,7 @@ action.correlationsearch.label = ESCU - Detect AzureHound Command-Line Arguments action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 80, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the common command-line argument used by AzureHound `Invoke-AzureHound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives. action.notable.param.rule_title = Detect AzureHound Command-Line Arguments action.notable.param.security_domain = endpoint @@ -4844,15 +4844,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_command_line_arguments_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_command_line_arguments_filter` [ESCU - Detect AzureHound File Modifications - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. +description = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. +action.escu.eli5 = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. action.escu.known_false_positives = False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed. action.escu.creation_date = 2021-06-01 @@ -4875,8 +4875,8 @@ action.correlationsearch.label = ESCU - Detect AzureHound File Modifications - R action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 70, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] -action.notable.param.rule_description = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. action.notable.param.rule_title = Detect AzureHound File Modifications action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -4889,7 +4889,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_file_modifications_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_file_modifications_filter` [ESCU - Detect Baron Samedit CVE-2021-3156 - Rule] action.escu = 0 @@ -4929,7 +4929,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter` +search = `linux_hosts` | search "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter` [ESCU - Detect Baron Samedit CVE-2021-3156 Segfault - Rule] action.escu = 0 @@ -4969,7 +4969,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter` +search = `linux_hosts` | search sudoedit segfault | stats count min(_time) as firstTime max(_time) as lastTime by host | search count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter` [ESCU - Detect Baron Samedit CVE-2021-3156 via OSQuery - Rule] action.escu = 0 @@ -5009,7 +5009,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter` +search = `osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter` [ESCU - Detect Computer Changed with Anonymous Account - Rule] action.escu = 0 @@ -5040,7 +5040,7 @@ action.correlationsearch.label = ESCU - Detect Computer Changed with Anonymous A action.correlationsearch.annotations = {"analytic_story": ["Detect Zerologon Attack"], "cis20": ["CIS 6", "CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1210"], "nist": ["DE.AE", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "EventCode", "role": ["Other"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account. action.notable.param.rule_title = Detect Computer Changed with Anonymous Account action.notable.param.security_domain = endpoint @@ -5054,7 +5054,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter` +search = `wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter` [ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule] action.escu = 0 @@ -5104,7 +5104,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message IN ("*copy*","*[System.IO.File]::Copy*") AND Message IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*") | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_copy_of_shadowcopy_with_script_block_logging_filter` +search = `powershell` EventCode=4104 Message IN ("*copy*","*[System.IO.File]::Copy*") AND Message IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*") | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_copy_of_shadowcopy_with_script_block_logging_filter` [ESCU - Detect Credential Dumping through LSASS access - Rule] action.escu = 0 @@ -5114,7 +5114,7 @@ action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phase action.escu.data_models = [] action.escu.eli5 = This search looks for reading lsass memory consistent with credential dumping. action.escu.how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -action.escu.known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. +action.escu.known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. action.escu.creation_date = 2019-12-03 action.escu.modification_date = 2019-12-03 action.escu.confidence = high @@ -5135,7 +5135,7 @@ action.correlationsearch.label = ESCU - Detect Credential Dumping through LSASS action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Detect Zerologon Attack"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"], "observable": [{"name": "source_image", "role": ["Victim"], "type": "Other"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "TargetImage", "role": ["Victim"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for reading lsass memory consistent with credential dumping. action.notable.param.rule_title = Detect Credential Dumping through LSASS access action.notable.param.security_domain = endpoint @@ -5159,8 +5159,8 @@ action.escu.mappings = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Deli action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. action.escu.how_to_implement = You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ action.escu.known_false_positives = If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. @@ -5180,7 +5180,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect DNS requests to Phis action.correlationsearch.annotations = {"analytic_story": ["Common Phishing Frameworks"], "cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1566.003"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. action.notable.param.rule_title = Detect DNS requests to Phishing Sites leveraging EvilGinx2 action.notable.param.security_domain = network @@ -5194,7 +5194,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution.DNS by DNS.dest DNS.src DNS.query host | `drop_dm_object_name(DNS)`| rex field=query ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | stats count values(query) as query by domain dest src answer| search `evilginx_phishlets_amazon` OR `evilginx_phishlets_facebook` OR `evilginx_phishlets_github` OR `evilginx_phishlets_0365` OR `evilginx_phishlets_outlook` OR `evilginx_phishlets_aws` OR `evilginx_phishlets_google` | search NOT [ inputlookup legit_domains.csv | fields domain]| join domain type=outer [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$" | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter` [ESCU - Detect Empire with PowerShell Script Block Logging - Rule] action.escu = 0 @@ -5254,11 +5254,11 @@ action.escu.mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.002"], "nis action.escu.data_models = ["Change"] action.escu.eli5 = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. action.escu.how_to_implement = You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ -action.escu.known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. +action.escu.known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. action.escu.creation_date = 2020-11-09 action.escu.modification_date = 2020-11-09 action.escu.confidence = high @@ -5279,7 +5279,7 @@ action.correlationsearch.label = ESCU - Detect Excessive Account Lockouts From E action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 16"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 60, "mitre_attack": ["T1078.002"], "nist": ["PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. action.notable.param.rule_title = Detect Excessive Account Lockouts From Endpoint action.notable.param.security_domain = access @@ -5293,7 +5293,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter` [ESCU - Detect Excessive User Account Lockouts - Rule] action.escu = 0 @@ -5324,7 +5324,7 @@ action.correlationsearch.label = ESCU - Detect Excessive User Account Lockouts - action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 16"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 60, "mitre_attack": ["T1078.003"], "nist": ["PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "result", "role": ["Victim"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search detects user accounts that have been locked out a relatively high number of times in a short period. action.notable.param.rule_title = Detect Excessive User Account Lockouts action.notable.param.security_domain = access @@ -5338,7 +5338,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter` [ESCU - Detect Exchange Web Shell - Rule] action.escu = 0 @@ -5369,7 +5369,7 @@ action.correlationsearch.label = ESCU - Detect Exchange Web Shell - Rule action.correlationsearch.annotations = {"analytic_story": ["HAFNIUM Group"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Exploitation"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505.003"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_name", "role": ["Victim"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation. action.notable.param.rule_title = Detect Exchange Web Shell action.notable.param.security_domain = endpoint @@ -5383,7 +5383,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System by _time span=1h Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `detect_exchange_web_shell_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System by _time span=1h Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `detect_exchange_web_shell_filter` [ESCU - Detect F5 TMUI RCE CVE-2020-5902 - Rule] action.escu = 0 @@ -5423,7 +5423,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter` +search = `f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter` [ESCU - Detect GCP Storage access from a new IP - Rule] action.escu = 0 @@ -5463,15 +5463,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter` +search = `google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter` [ESCU - Detect HTML Help Renamed - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +description = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = [] -action.escu.eli5 = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.escu.eli5 = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. action.escu.known_false_positives = Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. action.escu.creation_date = 2021-02-11 @@ -5494,8 +5494,8 @@ action.correlationsearch.label = ESCU - Detect HTML Help Renamed - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] -action.notable.param.rule_description = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Detect HTML Help Renamed action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -5513,10 +5513,10 @@ search = `sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe | st [ESCU - Detect HTML Help Spawn Child Process - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. action.escu.creation_date = 2021-02-11 @@ -5539,8 +5539,8 @@ action.correlationsearch.label = ESCU - Detect HTML Help Spawn Child Process - R action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Detect HTML Help Spawn Child Process action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -5558,10 +5558,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Detect HTML Help URL in Command Line - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. action.escu.creation_date = 2021-02-11 @@ -5584,8 +5584,8 @@ action.correlationsearch.label = ESCU - Detect HTML Help URL in Command Line - R action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Detect HTML Help URL in Command Line action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -5603,10 +5603,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Detect HTML Help Using InfoTech Storage Handlers - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.escu.eli5 = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. action.escu.creation_date = 2021-02-11 @@ -5629,8 +5629,8 @@ action.correlationsearch.label = ESCU - Detect HTML Help Using InfoTech Storage action.correlationsearch.annotations = {"analytic_story": ["Suspicious Compiled HTML Activity"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Detect HTML Help Using InfoTech Storage Handlers action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -5643,15 +5643,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter` [ESCU - Detect IPv6 Network Infrastructure Threats - Rule] action.escu = 0 action.escu.enabled = 1 -description = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. +description = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. action.escu.mappings = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = [] -action.escu.eli5 = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. +action.escu.eli5 = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. action.escu.known_false_positives = None currently known action.escu.creation_date = 2020-10-28 @@ -5670,7 +5670,7 @@ action.correlationsearch.label = ESCU - Detect IPv6 Network Infrastructure Threa action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. +action.notable.param.rule_description = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. action.notable.param.rule_title = Detect IPv6 Network Infrastructure Threats action.notable.param.security_domain = network action.notable.param.severity = high @@ -5683,7 +5683,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter` +search = `cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter` [ESCU - Detect Large Outbound ICMP Packets - Rule] action.escu = 0 @@ -5692,7 +5692,7 @@ description = This search looks for outbound ICMP packets with a packet size lar action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]} action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. -action.escu.how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model +action.escu.how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model action.escu.known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. action.escu.creation_date = 2018-06-01 action.escu.modification_date = 2018-06-01 @@ -5723,7 +5723,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` +search = | tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter` [ESCU - Detect Long DNS TXT Record Response - Rule] action.escu = 0 @@ -5733,7 +5733,7 @@ action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phas action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. action.escu.how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. -action.escu.known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. +action.escu.known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -5750,7 +5750,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect Long DNS TXT Record action.correlationsearch.annotations = {"analytic_story": ["Suspicious DNS Traffic", "Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. action.notable.param.rule_title = Detect Long DNS TXT Record Response action.notable.param.security_domain = network @@ -5764,15 +5764,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" | `detect_long_dns_txt_record_response_filter` [ESCU - Detect MSHTA Url in Command Line - Rule] action.escu = 0 action.escu.enabled = 1 -description = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +description = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +action.escu.eli5 = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = It is possible legitimate applications may perform this behavior and will need to be filtered. action.escu.creation_date = 2021-01-20 @@ -5795,8 +5795,8 @@ action.correlationsearch.label = ESCU - Detect MSHTA Url in Command Line - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. action.notable.param.rule_title = Detect MSHTA Url in Command Line action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -5809,7 +5809,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter` [ESCU - Detect Mimikatz Using Loaded Images - Rule] action.escu = 0 @@ -5840,7 +5840,7 @@ action.correlationsearch.label = ESCU - Detect Mimikatz Using Loaded Images - Ru action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Detect Zerologon Attack", "Cloud Federated Credential Abuse", "DarkSide Ransomware"], "cis20": ["CIS 6", "CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.AE", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "ImageLoaded", "role": ["Other"], "type": "Parent Process"}, {"name": "Image", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code. action.notable.param.rule_title = Detect Mimikatz Using Loaded Images action.notable.param.security_domain = endpoint @@ -5863,8 +5863,8 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. -action.escu.how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. -action.escu.known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. +action.escu.how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. +action.escu.known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 action.escu.confidence = high @@ -5894,7 +5894,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?<privs>\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter` +search = `wineventlog_security` signature_id=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mimikatz_via_powershell_and_eventcode_4703_filter` [ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule] action.escu = 0 @@ -5954,7 +5954,7 @@ action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on O action.escu.data_models = [] action.escu.eli5 = This search looks for newly created accounts that have been elevated to local administrators. action.escu.how_to_implement = You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 -action.escu.known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives +action.escu.known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives action.escu.creation_date = 2020-07-08 action.escu.modification_date = 2020-07-08 action.escu.confidence = high @@ -5975,7 +5975,7 @@ action.correlationsearch.label = ESCU - Detect New Local Admin account - Rule action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A", "HAFNIUM Group"], "cis20": ["CIS 16"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 60, "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1136.001"], "nist": ["PR.AC", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for newly created accounts that have been elevated to local administrators. action.notable.param.rule_title = Detect New Local Admin account action.notable.param.security_domain = access @@ -5998,7 +5998,7 @@ description = The search queries the authentication logs for assets that are cat action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Authentication"] action.escu.eli5 = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. -action.escu.how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. +action.escu.how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. action.escu.known_false_positives = Legitimate router connections may appear as new connections action.escu.creation_date = 2017-09-12 action.escu.modification_date = 2017-09-12 @@ -6016,7 +6016,7 @@ action.correlationsearch.label = ESCU - Detect New Login Attempts to Routers - R action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. action.notable.param.rule_title = Detect New Login Attempts to Routers action.notable.param.security_domain = network @@ -6030,7 +6030,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter` +search = | tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter` [ESCU - Detect New Open GCP Storage Buckets - Rule] action.escu = 0 @@ -6040,7 +6040,7 @@ action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on O action.escu.data_models = [] action.escu.eli5 = This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. action.escu.how_to_implement = This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). -action.escu.known_false_positives = While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. +action.escu.known_false_positives = While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. action.escu.creation_date = 2020-08-05 action.escu.modification_date = 2020-08-05 action.escu.confidence = high @@ -6057,7 +6057,7 @@ action.correlationsearch.label = ESCU - Detect New Open GCP Storage Buckets - Ru action.correlationsearch.annotations = {"analytic_story": ["Suspicious GCP Storage Activities"], "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'src'] +action.notable.param.nes_fields = ['user', 'src'] action.notable.param.rule_description = This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. action.notable.param.rule_title = Detect New Open GCP Storage Buckets action.notable.param.security_domain = network @@ -6081,7 +6081,7 @@ action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on O action.escu.data_models = [] action.escu.eli5 = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket over the aws cli. action.escu.how_to_implement = -action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. +action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. action.escu.creation_date = 2021-07-19 action.escu.modification_date = 2021-07-19 action.escu.confidence = high @@ -6110,7 +6110,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli* ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userIdentity.userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` +search = `cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli* ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userIdentity.userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter` [ESCU - Detect New Open S3 buckets - Rule] action.escu = 0 @@ -6120,7 +6120,7 @@ action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on O action.escu.data_models = [] action.escu.eli5 = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket. action.escu.how_to_implement = You must install the AWS App for Splunk. -action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. +action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. action.escu.creation_date = 2021-07-19 action.escu.modification_date = 2021-07-19 action.escu.confidence = high @@ -6149,7 +6149,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?<json_field>{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by user_arn userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` +search = `cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by user_arn userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter` [ESCU - Detect Outbound SMB Traffic - Rule] action.escu = 0 @@ -6159,7 +6159,7 @@ action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on O action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. action.escu.how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -action.escu.known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. +action.escu.known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -6189,7 +6189,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)` | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter` +search = | tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(start_time)` | `security_content_ctime(end_time)` | `detect_outbound_smb_traffic_filter` [ESCU - Detect Outlook exe writing a zip file - Rule] action.escu = 0 @@ -6216,7 +6216,7 @@ action.correlationsearch.label = ESCU - Detect Outlook exe writing a zip file - action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.001"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. action.notable.param.rule_title = Detect Outlook exe writing a zip file action.notable.param.security_domain = network @@ -6230,7 +6230,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `detect_outlook_exe_writing_a_zip_file_filter` +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path | where file_name != "" | `detect_outlook_exe_writing_a_zip_file_filter` [ESCU - Detect Path Interception By Creation Of program exe - Rule] action.escu = 0 @@ -6239,7 +6239,7 @@ description = The detection Detect Path Interception By Creation Of program exe action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1574.009"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = unknown action.escu.creation_date = 2020-07-03 action.escu.modification_date = 2020-07-03 @@ -6261,7 +6261,7 @@ action.correlationsearch.label = ESCU - Detect Path Interception By Creation Of action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1574.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. action.notable.param.rule_title = Detect Path Interception By Creation Of program exe action.notable.param.security_domain = endpoint @@ -6275,7 +6275,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?<service_process>[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter` [ESCU - Detect Port Security Violation - Rule] action.escu = 0 @@ -6284,7 +6284,7 @@ description = By enabling Port Security on a Cisco switch you can restrict input action.escu.mappings = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Exploitation", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = [] action.escu.eli5 = By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. -action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. action.escu.known_false_positives = This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. action.escu.creation_date = 2020-10-28 action.escu.modification_date = 2020-10-28 @@ -6315,7 +6315,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter` +search = `cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter` [ESCU - Detect Prohibited Applications Spawning cmd exe - Rule] action.escu = 0 @@ -6346,7 +6346,7 @@ action.correlationsearch.label = ESCU - Detect Prohibited Applications Spawning action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Zoom Child Processes", "NOBELIUM Group"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. action.notable.param.rule_title = Detect Prohibited Applications Spawning cmd exe action.notable.param.security_domain = endpoint @@ -6369,8 +6369,8 @@ description = This search looks for events where `PsExec.exe` is run with the `a action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine action.escu.creation_date = 2020-11-10 action.escu.modification_date = 2020-11-10 action.escu.confidence = high @@ -6391,7 +6391,7 @@ action.correlationsearch.label = ESCU - Detect PsExec With accepteula Flag - Rul action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Lateral Movement"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. action.notable.param.rule_title = Detect PsExec With accepteula Flag action.notable.param.security_domain = endpoint @@ -6436,7 +6436,7 @@ action.correlationsearch.label = ESCU - Detect RClone Command-Line Usage - Rule action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Exfiltration"], "impact": 50, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1020"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies commonly used command-line arguments used by `rclone.exe` to initiate a file transfer. Some arguments were negated as they are specific to the configuration used by adversaries. In particular, an adversary may list the files or directories of the remote file share using `ls` or `lsd`, which is not indicative of malicious behavior. During triage, at this stage of a ransomware event, exfiltration is about to occur or has already. Isolate the endpoint and continue investigating by review file modifications and parallel processes. action.notable.param.rule_title = Detect RClone Command-Line Usage action.notable.param.security_domain = endpoint @@ -6450,7 +6450,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*copy*", "*mega*", "*pcloud*", "*ftp*", "*--config*", "*--progress*", "*--no-check-certificate*", "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", "*--multi-thread-streams*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rclone_command_line_usage_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*copy*", "*mega*", "*pcloud*", "*ftp*", "*--config*", "*--progress*", "*--no-check-certificate*", "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", "*--multi-thread-streams*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rclone_command_line_usage_filter` [ESCU - Detect Rare Executables - Rule] action.escu = 0 @@ -6477,7 +6477,7 @@ action.correlationsearch.label = ESCU - Detect Rare Executables - Rule action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware DHS Report TA18-201A ", "Unusual Processes", "Cloud Federated Credential Abuse"], "cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. action.notable.param.rule_title = Detect Rare Executables action.notable.param.security_domain = endpoint @@ -6491,7 +6491,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` +search = | tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_allow_list`| table process ] | `detect_rare_executables_filter` [ESCU - Detect Regasm Spawning a Process - Rule] action.escu = 0 @@ -6522,7 +6522,7 @@ action.correlationsearch.label = ESCU - Detect Regasm Spawning a Process - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. action.notable.param.rule_title = Detect Regasm Spawning a Process action.notable.param.security_domain = endpoint @@ -6567,7 +6567,7 @@ action.correlationsearch.label = ESCU - Detect Regasm with Network Connection - action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. action.notable.param.rule_title = Detect Regasm with Network Connection action.notable.param.security_domain = endpoint @@ -6612,7 +6612,7 @@ action.correlationsearch.label = ESCU - Detect Regasm with no Command Line Argum action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_image", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. action.notable.param.rule_title = Detect Regasm with no Command Line Arguments action.notable.param.security_domain = endpoint @@ -6626,7 +6626,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) | regex CommandLine="(regasm\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_no_command_line_arguments_filter` +search = `sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) | regex CommandLine="(regasm\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_no_command_line_arguments_filter` [ESCU - Detect Regsvcs Spawning a Process - Rule] action.escu = 0 @@ -6657,7 +6657,7 @@ action.correlationsearch.label = ESCU - Detect Regsvcs Spawning a Process - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. action.notable.param.rule_title = Detect Regsvcs Spawning a Process action.notable.param.security_domain = endpoint @@ -6702,7 +6702,7 @@ action.correlationsearch.label = ESCU - Detect Regsvcs with Network Connection - action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. action.notable.param.rule_title = Detect Regsvcs with Network Connection action.notable.param.security_domain = Endpoint @@ -6747,7 +6747,7 @@ action.correlationsearch.label = ESCU - Detect Regsvcs with No Command Line Argu action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvcs Regasm Activity"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_image", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe. action.notable.param.rule_title = Detect Regsvcs with No Command Line Arguments action.notable.param.security_domain = endpoint @@ -6761,19 +6761,19 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) | regex CommandLine="(regsvcs\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_no_command_line_arguments_filter` +search = `sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) | regex CommandLine="(regsvcs\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_no_command_line_arguments_filter` [ESCU - Detect Regsvr32 Application Control Bypass - Rule] action.escu = 0 action.escu.enabled = 1 -description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ -Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. +description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ -Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. -action.escu.known_false_positives = Limited false positives related to third party software registering .DLL's. +action.escu.eli5 = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. +action.escu.known_false_positives = Limited false positives related to third party software registering .DLL's. action.escu.creation_date = 2021-01-28 action.escu.modification_date = 2021-01-28 action.escu.confidence = high @@ -6794,9 +6794,9 @@ action.correlationsearch.label = ESCU - Detect Regsvr32 Application Control Bypa action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvr32 Activity", "Cobalt Strike"], "cis20": ["CIS 8", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ -Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. action.notable.param.rule_title = Detect Regsvr32 Application Control Bypass action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -6840,7 +6840,7 @@ action.correlationsearch.label = ESCU - Detect Renamed 7-Zip - Rule action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1560.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies renamed 7-Zip usage using Sysmon. At this stage of an attack, review parallel processes and file modifications for data that is staged or potentially have been exfiltrated. This analytic utilizes the OriginalFileName to capture the renamed process. action.notable.param.rule_title = Detect Renamed 7-Zip action.notable.param.security_domain = endpoint @@ -6885,7 +6885,7 @@ action.correlationsearch.label = ESCU - Detect Renamed PSExec - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "DHS Report TA18-074A", "HAFNIUM Group", "DarkSide Ransomware", "Lateral Movement"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exploitation", "Lateral Movement", "Execution"], "mitre_attack": ["T1569.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies renamed instances of `PsExec.exe` being utilized on an endpoint. Most instances, it is highly probable to capture `Psexec.exe` or other SysInternal utility usage with the command-line argument of `-accepteula`. In this instance, we are using `OriginalFileName` from Sysmon to identify `PsExec` usage. During triage, validate this is the legitimate version of `PsExec` by review the PE metadata. In addition, review parallel processes for further suspicious behavior. action.notable.param.rule_title = Detect Renamed PSExec action.notable.param.security_domain = endpoint @@ -6930,7 +6930,7 @@ action.correlationsearch.label = ESCU - Detect Renamed RClone - Rule action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Ransomware"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1020"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies the usage of `rclone.exe`, renamed, being used to exfiltrate data to a remote destination. RClone has been used by multiple ransomware groups to exfiltrate data. In many instances, it will be downloaded from the legitimate site and executed accordingly. During triage, isolate the endpoint and begin to review parallel processes for additional behavior. At this stage, the adversary may have staged data to be exfiltrated. action.notable.param.rule_title = Detect Renamed RClone action.notable.param.security_domain = endpoint @@ -6975,7 +6975,7 @@ action.correlationsearch.label = ESCU - Detect Renamed WinRAR - Rule action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Collection"], "impact": 30, "kill_chain_phases": ["Exploitation", "Exfiltration"], "mitre_attack": ["T1560.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analtyic identifies renamed instances of `WinRAR.exe`. In most cases, it is not common for WinRAR to be used renamed, however it is common to be installed by a third party application and executed from a non-standard path. In this instance, we are using `OriginalFileName` from Sysmon to determine if the process is WinRAR. During triage, validate additional metadata from the binary that this is `WinRAR`. Review parallel processes and file modifications. action.notable.param.rule_title = Detect Renamed WinRAR action.notable.param.security_domain = endpoint @@ -6994,11 +6994,11 @@ search = `sysmon` EventID=1 (Product=WinRAR OR OriginalFileName=WinRAR.exe) proc [ESCU - Detect Rogue DHCP Server - Rule] action.escu = 0 action.escu.enabled = 1 -description = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). +description = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). action.escu.mappings = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = [] -action.escu.eli5 = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). -action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +action.escu.eli5 = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). +action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. action.escu.known_false_positives = This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. action.escu.creation_date = 2020-08-11 action.escu.modification_date = 2020-08-11 @@ -7016,7 +7016,7 @@ action.correlationsearch.label = ESCU - Detect Rogue DHCP Server - Rule action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['src_mac', 'firstTime', 'lastTime', 'count', 'message_type'] +action.notable.param.nes_fields = ['src_mac', 'firstTime', 'lastTime', 'count', 'message_type'] action.notable.param.rule_description = DHCP Snooping has detected a Rogue DHCP Server on $orig_host$ from $src_mac$. This may be an indication of a MITM attack. action.notable.param.rule_title = Rogue DHCP Server Detected on $orig_host$ action.notable.param.security_domain = network @@ -7030,15 +7030,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter` +search = `cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter` [ESCU - Detect Rundll32 Application Control Bypass - advpack - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +description = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +action.escu.eli5 = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. action.escu.creation_date = 2021-02-04 @@ -7061,8 +7061,8 @@ action.correlationsearch.label = ESCU - Detect Rundll32 Application Control Bypa action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.notable.param.rule_title = Detect Rundll32 Application Control Bypass - advpack action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -7080,10 +7080,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Detect Rundll32 Application Control Bypass - setupapi - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +description = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +action.escu.eli5 = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Although unlikely, some legitimate applications may use setupapi triggering a false positive. action.escu.creation_date = 2021-02-04 @@ -7106,8 +7106,8 @@ action.correlationsearch.label = ESCU - Detect Rundll32 Application Control Bypa action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.notable.param.rule_title = Detect Rundll32 Application Control Bypass - setupapi action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -7125,10 +7125,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Detect Rundll32 Application Control Bypass - syssetup - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +description = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +action.escu.eli5 = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. action.escu.creation_date = 2021-02-04 @@ -7151,8 +7151,8 @@ action.correlationsearch.label = ESCU - Detect Rundll32 Application Control Bypa action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. action.notable.param.rule_title = Detect Rundll32 Application Control Bypass - syssetup action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -7170,10 +7170,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Detect Rundll32 Inline HTA Execution - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +description = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +action.escu.eli5 = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. action.escu.creation_date = 2021-01-20 @@ -7196,8 +7196,8 @@ action.correlationsearch.label = ESCU - Detect Rundll32 Inline HTA Execution - R action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity", "NOBELIUM Group"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. action.notable.param.rule_title = Detect Rundll32 Inline HTA Execution action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -7219,7 +7219,7 @@ description = This search looks at S3 bucket-access logs and detects new or prev action.escu.mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} action.escu.data_models = [] action.escu.eli5 = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. action.escu.known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour action.escu.creation_date = 2018-06-28 action.escu.modification_date = 2018-06-28 @@ -7254,7 +7254,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` +search = `aws_s3_accesslogs` http_status=200 [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter` [ESCU - Detect SNICat SNI Exfiltration - Rule] action.escu = 0 @@ -7294,7 +7294,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `zeek_ssl` | rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter` +search = `zeek_ssl` | rex field=server_name "(?(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter` [ESCU - Detect SharpHound Command-Line Arguments - Rule] action.escu = 0 @@ -7325,7 +7325,7 @@ action.correlationsearch.label = ESCU - Detect SharpHound Command-Line Arguments action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques", "Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies common command-line arguments used by SharpHound `-collectionMethod` and `invoke-bloodhound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives. action.notable.param.rule_title = Detect SharpHound Command-Line Arguments action.notable.param.security_domain = endpoint @@ -7339,15 +7339,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_command_line_arguments_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_command_line_arguments_filter` [ESCU - Detect SharpHound File Modifications - Rule] action.escu = 0 action.escu.enabled = 1 -description = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. +description = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. +action.escu.eli5 = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. action.escu.known_false_positives = False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed. action.escu.creation_date = 2021-05-27 @@ -7370,8 +7370,8 @@ action.correlationsearch.label = ESCU - Detect SharpHound File Modifications - R action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques", "Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] -action.notable.param.rule_description = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. action.notable.param.rule_title = Detect SharpHound File Modifications action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -7384,7 +7384,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json") by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_file_modifications_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json") by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_file_modifications_filter` [ESCU - Detect SharpHound Usage - Rule] action.escu = 0 @@ -7415,7 +7415,7 @@ action.correlationsearch.label = ESCU - Detect SharpHound Usage - Rule action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques", "Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies SharpHound binary usage by using the `OriginalFileName` from Sysmon. In addition to renaming the PE, other coverage is available to detect command-line arguments. This particular analytic only looks for the OriginalFileName of `SharpHound.exe`. It is possible older instances of SharpHound.exe have different original filenames. Dependent upon the operator, the code may be re-compiled and the attributes removed or changed to anything else. During triage, review the metadata of the binary in question. Review parallel processes for suspicious behavior. Identify the source of this binary. action.notable.param.rule_title = Detect SharpHound Usage action.notable.param.security_domain = endpoint @@ -7438,7 +7438,7 @@ description = Adversaries may abuse netbooting to load an unauthorized network d action.escu.mappings = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1542.005"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. -action.escu.how_to_implement = This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. +action.escu.how_to_implement = This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. action.escu.known_false_positives = This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. action.escu.creation_date = 2020-10-28 action.escu.modification_date = 2020-10-28 @@ -7456,7 +7456,7 @@ action.correlationsearch.label = ESCU - Detect Software Download To Network Devi action.correlationsearch.annotations = {"analytic_story": ["Router and Infrastructure Security"], "cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1542.005"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. action.notable.param.rule_title = Detect Software Download To Network Device action.notable.param.security_domain = network @@ -7470,7 +7470,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter` [ESCU - Detect Spike in AWS API Activity - Rule] action.escu = 0 @@ -7480,7 +7480,7 @@ action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on O action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ -This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ 1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ 1. \ @@ -7507,7 +7507,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect Spike in AWS API Act action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = Detect Spike in AWS API Activity action.notable.param.security_domain = network @@ -7521,7 +7521,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter` +search = `cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_aws_api_activity_filter` [ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule] action.escu = 0 @@ -7560,7 +7560,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` +search = `aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter` [ESCU - Detect Spike in AWS Security Hub Alerts for User - Rule] action.escu = 0 @@ -7587,7 +7587,7 @@ action.correlationsearch.label = ESCU - Detect Spike in AWS Security Hub Alerts action.correlationsearch.annotations = {"analytic_story": ["AWS Security Hub Alerts"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals. action.notable.param.rule_title = Detect Spike in AWS Security Hub Alerts for User action.notable.param.security_domain = network @@ -7601,7 +7601,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter` +search = `aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter` [ESCU - Detect Spike in Network ACL Activity - Rule] action.escu = 0 @@ -7610,7 +7610,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. action.escu.creation_date = 2018-05-21 action.escu.modification_date = 2018-05-21 @@ -7632,7 +7632,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect Spike in Network ACL action.correlationsearch.annotations = {"analytic_story": ["AWS Network ACL Activity"], "cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = Detect Spike in Network ACL Activity action.notable.param.security_domain = network @@ -7646,7 +7646,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter` +search = `cloudtrail` `network_acl_events` [search `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup network_acl_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_network_acl_activity_filter` [ESCU - Detect Spike in S3 Bucket deletion - Rule] action.escu = 0 @@ -7655,7 +7655,7 @@ description = This search detects users creating spikes in API activity related action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} action.escu.data_models = [] action.escu.eli5 = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. action.escu.creation_date = 2018-11-27 action.escu.modification_date = 2018-11-27 @@ -7677,7 +7677,7 @@ action.correlationsearch.label = ESCU - Detect Spike in S3 Bucket deletion - Rul action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS S3 Activities"], "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. action.notable.param.rule_title = Detect Spike in S3 Bucket deletion action.notable.param.security_domain = network @@ -7691,7 +7691,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` +search = `cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter` [ESCU - Detect Spike in Security Group Activity - Rule] action.escu = 0 @@ -7700,7 +7700,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. action.escu.creation_date = 2018-04-18 action.escu.modification_date = 2018-04-18 @@ -7722,7 +7722,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect Spike in Security Gr action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = Detect Spike in Security Group Activity action.notable.param.security_domain = network @@ -7736,7 +7736,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter` +search = `cloudtrail` `security_group_api_calls` [search `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup security_group_activity_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_security_group_activity_filter` [ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] action.escu = 0 @@ -7745,7 +7745,7 @@ description = This search will detect spike in blocked outbound network connecti action.escu.mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} action.escu.data_models = [] action.escu.eli5 = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. action.escu.creation_date = 2018-05-07 action.escu.modification_date = 2018-05-07 @@ -7780,7 +7780,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` +search = `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter` [ESCU - Detect Traffic Mirroring - Rule] action.escu = 0 @@ -7789,7 +7789,7 @@ description = Adversaries may leverage traffic mirroring in order to automate da action.escu.mappings = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1020.001"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = [] action.escu.eli5 = Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. -action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. +action.escu.how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. action.escu.known_false_positives = This search will return false positives for any legitimate traffic captures by network administrators. action.escu.creation_date = 2020-10-28 action.escu.modification_date = 2020-10-28 @@ -7820,7 +7820,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter` +search = `cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter` [ESCU - Detect USB device insertion - Rule] action.escu = 0 @@ -7847,7 +7847,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect USB device insertion action.correlationsearch.annotations = {"analytic_story": ["Data Protection"], "cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. action.notable.param.rule_title = Detect USB device insertion action.notable.param.security_domain = endpoint @@ -7861,15 +7861,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter` +search = | tstats `security_content_summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `detect_usb_device_insertion_filter` [ESCU - Detect Unauthorized Assets by MAC address - Rule] action.escu = 0 action.escu.enabled = 1 -description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. +description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. action.escu.mappings = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Network_Sessions"] -action.escu.eli5 = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. +action.escu.eli5 = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. action.escu.how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. action.escu.known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. action.escu.creation_date = 2017-09-13 @@ -7888,7 +7888,7 @@ action.correlationsearch.label = ESCU - Detect Unauthorized Assets by MAC addres action.correlationsearch.annotations = {"analytic_story": ["Asset Tracking"], "cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. +action.notable.param.rule_description = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. action.notable.param.rule_title = Detect Unauthorized Assets by MAC address action.notable.param.security_domain = network action.notable.param.severity = high @@ -7901,7 +7901,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter` +search = | tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter` [ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule] action.escu = 0 @@ -7932,7 +7932,7 @@ action.correlationsearch.label = ESCU - Detect Use of cmd exe to Launch Script I action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware DHS Report TA18-201A ", "Suspicious Command-Line Executions"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine action.notable.param.rule_title = Detect Use of cmd exe to Launch Script Interpreters action.notable.param.security_domain = endpoint @@ -7946,7 +7946,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter` [ESCU - Detect WMI Event Subscription Persistence - Rule] action.escu = 0 @@ -7966,7 +7966,7 @@ All event subscriptions have three components \ 1. Binding - Registers a filter to a consumer. EventID equals 21 \ Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with that provide WMI Event Subscription from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID 19, 20 and 21. Tune and filter known good to limit the volume. -action.escu.known_false_positives = It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage. +action.escu.known_false_positives = It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage. action.escu.creation_date = 2021-06-16 action.escu.modification_date = 2021-06-16 action.escu.confidence = high @@ -8014,7 +8014,7 @@ description = This search detects SIGRed via Splunk Stream. action.escu.mappings = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]} action.escu.data_models = [] action.escu.eli5 = This search detects SIGRed via Splunk Stream. -action.escu.how_to_implement = You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. +action.escu.how_to_implement = You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. action.escu.known_false_positives = unknown action.escu.creation_date = 2020-07-28 action.escu.modification_date = 2020-07-28 @@ -8045,7 +8045,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count +search = `stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count [ESCU - Detect Windows DNS SIGRed via Zeek - Rule] action.escu = 0 @@ -8085,7 +8085,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count [ESCU - Detect Zerologon via Zeek - Rule] action.escu = 0 @@ -8125,7 +8125,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter` +search = `zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter` [ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] action.escu = 0 @@ -8135,7 +8135,7 @@ action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": action.escu.data_models = ["Web"] action.escu.eli5 = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. action.escu.how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. -action.escu.known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. +action.escu.known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. action.escu.creation_date = 2017-09-23 action.escu.modification_date = 2017-09-23 action.escu.confidence = high @@ -8152,7 +8152,7 @@ action.correlationsearch.label = ESCU - Detect attackers scanning for vulnerable action.correlationsearch.annotations = {"analytic_story": ["JBoss Vulnerability", "SamSam Ransomware"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. action.notable.param.rule_title = Detect attackers scanning for vulnerable JBoss servers action.notable.param.security_domain = network @@ -8166,7 +8166,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter` [ESCU - Detect hosts connecting to dynamic domain providers - Rule] action.escu = 0 @@ -8175,8 +8175,8 @@ description = Malicious actors often abuse legitimate Dynamic DNS services to ho action.escu.mappings = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1189"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. -action.escu.how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ -This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +action.escu.how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ +This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ 1. **Label:** DNS Answer, **Field:** answer\ 1. \ @@ -8216,7 +8216,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` +search = | tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter` [ESCU - Detect malicious requests to exploit JBoss servers - Rule] action.escu = 0 @@ -8243,7 +8243,7 @@ action.correlationsearch.label = ESCU - Detect malicious requests to exploit JBo action.correlationsearch.annotations = {"analytic_story": ["JBoss Vulnerability", "SamSam Ransomware"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. action.notable.param.rule_title = Detect malicious requests to exploit JBoss servers action.notable.param.security_domain = network @@ -8257,15 +8257,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter` [ESCU - Detect mshta inline hta execution - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. +description = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. +action.escu.eli5 = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. action.escu.creation_date = 2021-01-20 @@ -8288,8 +8288,8 @@ action.correlationsearch.label = ESCU - Detect mshta inline hta execution - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. action.notable.param.rule_title = Detect mshta inline hta execution action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -8333,7 +8333,7 @@ action.correlationsearch.label = ESCU - Detect mshta renamed - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity"], "cis20": ["CIS 8"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta. action.notable.param.rule_title = Detect mshta renamed action.notable.param.security_domain = endpoint @@ -8356,7 +8356,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in AWS CloudTrail" support search once to create a history of previously seen user roles. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in AWS CloudTrail" support search once to create a history of previously seen user roles. action.escu.known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. action.escu.creation_date = 2018-04-16 action.escu.modification_date = 2018-04-16 @@ -8378,7 +8378,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect new API calls from u action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. action.notable.param.rule_title = Detect new API calls from user roles action.notable.param.security_domain = endpoint @@ -8392,7 +8392,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter` +search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | `detect_new_api_calls_from_user_roles_filter` [ESCU - Detect new user AWS Console Login - Rule] action.escu = 0 @@ -8401,7 +8401,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen users in AWS CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in AWS CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen users in AWS CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in AWS CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -8419,7 +8419,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect new user AWS Console action.correlationsearch.annotations = {"analytic_story": ["Suspicious AWS Login Activities"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. action.notable.param.rule_title = Detect new user AWS Console Login action.notable.param.security_domain = network @@ -8433,7 +8433,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter` +search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | stats earliest(_time) as firstTime latest(_time) as lastTime by user | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user | eval userStatus=if(firstTime >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| where userStatus ="First Time Logging into AWS Console" | `detect_new_user_aws_console_login_filter` [ESCU - Detect processes used for System Network Configuration Discovery - Rule] action.escu = 0 @@ -8464,7 +8464,7 @@ action.correlationsearch.label = ESCU - Detect processes used for System Network action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 2"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 40, "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1016"], "nist": ["ID.AM", "PR.DS"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for fast execution of processes used for system network configuration discovery on the endpoint. action.notable.param.rule_title = Detect processes used for System Network Configuration Discovery action.notable.param.security_domain = endpoint @@ -8478,7 +8478,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT Processes.user IN ("","unknown") by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT Processes.user IN ("","unknown") by Processes.dest Processes.process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter` [ESCU - Detect shared ec2 snapshot - Rule] action.escu = 0 @@ -8517,7 +8517,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=ModifySnapshotAttribute | rename requestParameters.createVolumePermission.add.items{}.userId as requested_account_id | search requested_account_id != NULL | eval match=if(requested_account_id==aws_account_id,"Match","No Match") | table _time user_arn src_ip requestParameters.attributeType requested_account_id aws_account_id match vendor_region user_agent | where match = "No Match" | `detect_shared_ec2_snapshot_filter` +search = `cloudtrail` eventName=ModifySnapshotAttribute | rename requestParameters.createVolumePermission.add.items{}.userId as requested_account_id | search requested_account_id != NULL | eval match=if(requested_account_id==aws_account_id,"Match","No Match") | table _time user_arn src_ip requestParameters.attributeType requested_account_id aws_account_id match vendor_region user_agent | where match = "No Match" | `detect_shared_ec2_snapshot_filter` [ESCU - Detect web traffic to dynamic domain providers - Rule] action.escu = 0 @@ -8527,7 +8527,7 @@ action.escu.mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Comm action.escu.data_models = ["Web"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for web connections to dynamic DNS providers. action.escu.how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ -This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. action.escu.known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. action.escu.creation_date = 2020-07-21 @@ -8546,7 +8546,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detect web traffic to dynam action.correlationsearch.annotations = {"analytic_story": ["Dynamic DNS"], "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1071.001"], "nist": ["PR.IP", "DE.DP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for web connections to dynamic DNS providers. action.notable.param.rule_title = Detect web traffic to dynamic domain providers action.notable.param.security_domain = network @@ -8560,7 +8560,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter` +search = | tstats `security_content_summariesonly` count values(Web.url) as url min(_time) as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic` | `detect_web_traffic_to_dynamic_domain_providers_filter` [ESCU - Detection of DNS Tunnels - Rule] action.escu = 0 @@ -8570,7 +8570,7 @@ action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. action.escu.how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. -action.escu.known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. +action.escu.known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. action.escu.creation_date = 2017-09-18 action.escu.modification_date = 2017-09-18 action.escu.confidence = high @@ -8587,7 +8587,7 @@ action.correlationsearch.label = ESCU - Deprecated - Detection of DNS Tunnels - action.correlationsearch.annotations = {"analytic_story": ["Data Protection", "Suspicious DNS Traffic", "Command and Control"], "cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['src'] +action.notable.param.nes_fields = ['src'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. action.notable.param.rule_title = Detection of DNS Tunnels action.notable.param.security_domain = network @@ -8601,7 +8601,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter` +search = | tstats `security_content_summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `security_content_summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 | `detection_of_dns_tunnels_filter` [ESCU - Detection of tools built by NirSoft - Rule] action.escu = 0 @@ -8610,7 +8610,7 @@ description = This search looks for specific command-line arguments that may ind action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -8628,7 +8628,7 @@ action.correlationsearch.label = ESCU - Detection of tools built by NirSoft - Ru action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware DHS Report TA18-201A "], "cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072"], "nist": ["PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. action.notable.param.rule_title = Detection of tools built by NirSoft action.notable.param.security_domain = endpoint @@ -8642,7 +8642,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter` +search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter` [ESCU - Disable AMSI Through Registry - Rule] action.escu = 0 @@ -8669,7 +8669,7 @@ action.correlationsearch.label = ESCU - Disable AMSI Through Registry - Rule action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = this search is to identify modification in registry to disable AMSI windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible. action.notable.param.rule_title = Disable AMSI Through Registry action.notable.param.security_domain = endpoint @@ -8683,7 +8683,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable" Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_amsi_through_registry_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable" Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_amsi_through_registry_filter` [ESCU - Disable ETW Through Registry - Rule] action.escu = 0 @@ -8710,7 +8710,7 @@ action.correlationsearch.label = ESCU - Disable ETW Through Registry - Rule action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = this search is to identify modification in registry to disable ETW windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible. action.notable.param.rule_title = Disable ETW Through Registry action.notable.param.security_domain = endpoint @@ -8724,7 +8724,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_etw_through_registry_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_etw_through_registry_filter` [ESCU - Disable Logs Using WevtUtil - Rule] action.escu = 0 @@ -8755,7 +8755,7 @@ action.correlationsearch.label = ESCU - Disable Logs Using WevtUtil - Rule action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 80, "context": [{"Source": "Endpoint"}, {"Stage": "Defense Evasion"}], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect execution of wevtutil.exe to disable logs. This technique was seen in several ransomware to disable the event logs to evade alerts and detections. action.notable.param.rule_title = Disable Logs Using WevtUtil action.notable.param.security_domain = endpoint @@ -8769,7 +8769,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wevtutil.exe" Processes.process = "*sl*" Processes.process = "*/e:false*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_logs_using_wevtutil_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wevtutil.exe" Processes.process = "*sl*" Processes.process = "*/e:false*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_logs_using_wevtutil_filter` [ESCU - Disable Registry Tool - Rule] action.escu = 0 @@ -8800,7 +8800,7 @@ action.correlationsearch.label = ESCU - Disable Registry Tool - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 100, "context": [{"Source": "Endpoint"}, {"Stage": "Defense Evasion"}], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search identifies modification of registry to disable the regedit or registry tools of the windows operating system. Since registry tool is a swiss knife in analyzing registry, malware such as RAT or trojan Spy disable this application to prevent the removal of their registry entry such as persistence, file less components and defense evasion. action.notable.param.rule_title = Disable Registry Tool action.notable.param.security_domain = endpoint @@ -8814,7 +8814,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_registry_tool_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_registry_tool_filter` [ESCU - Disable Show Hidden Files - Rule] action.escu = 0 @@ -8834,7 +8834,7 @@ action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splun action.escu.providing_technologies = [] action.escu.analytic_story = ["Windows Defense Evasion Tactics"] action.risk = 1 -action.risk.param._risk_message = Disabled 'Show Hidden Files' +action.risk.param._risk_message = Disabled 'Show Hidden Files' action.risk.param._risk = [] action.risk.param.verbose = 0 cron_schedule = 0 * * * * @@ -8845,7 +8845,7 @@ action.correlationsearch.label = ESCU - Disable Show Hidden Files - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 100, "context": [{"Source": "Endpoint"}, {"Stage": "Defense Evasion"}], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1564.001", "T1562.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic is to identify a modification in the Windows registry to prevent users from seeing all the files with hidden attributes. This event or techniques are known on some worm and trojan spy malware that will drop hidden files on the infected machine. action.notable.param.rule_title = Disable Show Hidden Files action.notable.param.security_domain = endpoint @@ -8859,7 +8859,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt" Registry.registry_value_name = "DWORD (0x00000001)") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden" Registry.registry_value_name = "DWORD (0x00000000)") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_show_hidden_files_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt" Registry.registry_value_name = "DWORD (0x00000001)") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden" Registry.registry_value_name = "DWORD (0x00000000)") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_show_hidden_files_filter` [ESCU - Disable Windows App Hotkeys - Rule] action.escu = 0 @@ -8879,7 +8879,7 @@ action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splun action.escu.providing_technologies = [] action.escu.analytic_story = ["XMRig"] action.risk = 1 -action.risk.param._risk_message = Disabled 'Windows App Hotkeys' +action.risk.param._risk_message = Disabled 'Windows App Hotkeys' action.risk.param._risk = [] action.risk.param.verbose = 0 cron_schedule = 0 * * * * @@ -8890,7 +8890,7 @@ action.correlationsearch.label = ESCU - Disable Windows App Hotkeys - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 100, "context": [{"Source": "Endpoint"}, {"Stage": "Defense Evasion"}], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic detects a suspicious registry modification to disable Windows hotkey (shortcut keys) for native Windows applications. This technique is commonly used to disable certain or several Windows applications like `taskmgr.exe` and `cmd.exe`. This technique is used to impair the analyst in analyzing and removing the attacker implant in compromised systems. action.notable.param.rule_title = Disable Windows App Hotkeys action.notable.param.security_domain = endpoint @@ -8904,7 +8904,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_name = "HotKey Disabled" AND Registry.registry_key_name = "Debugger" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `disable_windows_app_hotkeys_filter` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_name = "HotKey Disabled" AND Registry.registry_key_name = "Debugger" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `disable_windows_app_hotkeys_filter` [ESCU - Disable Windows Behavior Monitoring - Rule] action.escu = 0 @@ -8935,7 +8935,7 @@ action.correlationsearch.label = ESCU - Disable Windows Behavior Monitoring - Ru action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Ransomware", "Revil Ransomware"], "confidence": 100, "context": [{"Source": "Endpoint"}, {"Stage": "Defense Evasion"}], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections. action.notable.param.rule_title = Disable Windows Behavior Monitoring action.notable.param.security_domain = endpoint @@ -8949,7 +8949,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableScanOnRealtimeEnable" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableRealtimeMonitoring" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time Protection\\DisableScriptScanning" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_windows_behavior_monitoring_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableScanOnRealtimeEnable" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableRealtimeMonitoring" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time Protection\\DisableScriptScanning" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_windows_behavior_monitoring_filter` [ESCU - Disable Windows SmartScreen Protection - Rule] action.escu = 0 @@ -8980,7 +8980,7 @@ action.correlationsearch.label = ESCU - Disable Windows SmartScreen Protection - action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following search identifies a modification of registry to disable the smartscreen protection of windows machine. This is windows feature provide an early warning system against website that might engage in phishing attack or malware distribution. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload. action.notable.param.rule_title = Disable Windows SmartScreen Protection action.notable.param.security_domain = endpoint @@ -8994,7 +8994,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled" Registry.registry_value_name = "Off" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_windows_smartscreen_protection_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled" Registry.registry_value_name = "Off" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disable_windows_smartscreen_protection_filter` [ESCU - Disabling CMD Application - Rule] action.escu = 0 @@ -9025,7 +9025,7 @@ action.correlationsearch.label = ESCU - Disabling CMD Application - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = this search is to identify modification in registry to disable cmd prompt application. This technique is commonly seen in RAT, Trojan or WORM to prevent triaging or deleting there samples through cmd application which is one of the tool of analyst to traverse on directory and files. action.notable.param.rule_title = Disabling CMD Application action.notable.param.security_domain = endpoint @@ -9039,7 +9039,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disabling_cmd_application_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disabling_cmd_application_filter` [ESCU - Disabling ControlPanel - Rule] action.escu = 0 @@ -9070,7 +9070,7 @@ action.correlationsearch.label = ESCU - Disabling ControlPanel - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = this search is to identify registry modification to disable control panel window. This technique is commonly seen in malware to prevent their artifacts , persistence removed on the infected machine. action.notable.param.rule_title = Disabling ControlPanel action.notable.param.security_domain = endpoint @@ -9084,7 +9084,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_controlpanel_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_controlpanel_filter` [ESCU - Disabling Firewall with Netsh - Rule] action.escu = 0 @@ -9115,7 +9115,7 @@ action.correlationsearch.label = ESCU - Disabling Firewall with Netsh - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to identifies suspicious firewall disabling using netsh application. this technique is commonly seen in malware that tries to communicate or download its component or other payload to its C2 server. action.notable.param.rule_title = Disabling Firewall with Netsh action.notable.param.security_domain = endpoint @@ -9129,7 +9129,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" (Processes.process= "*off*" OR Processes.process= "*disable*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_firewall_with_netsh_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" (Processes.process= "*off*" OR Processes.process= "*disable*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_firewall_with_netsh_filter` [ESCU - Disabling FolderOptions Windows Feature - Rule] action.escu = 0 @@ -9160,7 +9160,7 @@ action.correlationsearch.label = ESCU - Disabling FolderOptions Windows Feature action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to identify registry modification to disable folder options feature of windows to show hidden files, file extension and etc. This technique used by malware in combination if disabling show hidden files feature to hide their files and also to hide the file extension to lure the user base on file icons or fake file extensions. action.notable.param.rule_title = Disabling FolderOptions Windows Feature action.notable.param.security_domain = endpoint @@ -9174,7 +9174,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_folderoptions_windows_feature_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_folderoptions_windows_feature_filter` [ESCU - Disabling Net User Account - Rule] action.escu = 0 @@ -9205,7 +9205,7 @@ action.correlationsearch.label = ESCU - Disabling Net User Account - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic will identify a suspicious command-line that disables a user account using the `net.exe` utility native to Windows. This technique may used by the adversaries to interrupt availability of such users to do their malicious act. action.notable.param.rule_title = Disabling Net User Account action.notable.param.security_domain = endpoint @@ -9219,7 +9219,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND Processes.process="*/active:no*" by Processes.process_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_net_user_account_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND Processes.process="*/active:no*" by Processes.process_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_net_user_account_filter` [ESCU - Disabling NoRun Windows App - Rule] action.escu = 0 @@ -9250,7 +9250,7 @@ action.correlationsearch.label = ESCU - Disabling NoRun Windows App - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to identify modification of registry to disable run application in window start menu. this application is known to be a helpful shortcut to windows OS user to run known application and also to execute some reg or batch script. This technique is used malware to make cleaning of its infection more harder by preventing known application run easily through run shortcut. action.notable.param.rule_title = Disabling NoRun Windows App action.notable.param.security_domain = endpoint @@ -9264,7 +9264,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_norun_windows_app_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_norun_windows_app_filter` [ESCU - Disabling Remote User Account Control - Rule] action.escu = 0 @@ -9295,7 +9295,7 @@ action.correlationsearch.label = ESCU - Disabling Remote User Account Control - action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1548.002"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). action.notable.param.rule_title = Disabling Remote User Account Control action.notable.param.security_domain = endpoint @@ -9309,7 +9309,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter` [ESCU - Disabling SystemRestore In Registry - Rule] action.escu = 0 @@ -9340,7 +9340,7 @@ action.correlationsearch.label = ESCU - Disabling SystemRestore In Registry - Ru action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following search identifies the modification of registry related in disabling the system restore of a machine. This event or behavior are seen in some RAT malware to make the restore of the infected machine difficult and keep their infection on the box. action.notable.param.rule_title = Disabling SystemRestore In Registry action.notable.param.security_domain = endpoint @@ -9354,7 +9354,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableConfig" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disabling_systemrestore_in_registry_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableConfig" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `disabling_systemrestore_in_registry_filter` [ESCU - Disabling Task Manager - Rule] action.escu = 0 @@ -9385,7 +9385,7 @@ action.correlationsearch.label = ESCU - Disabling Task Manager - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to identifies modification of registry to disable the task manager of windows operating system. this event or technique are commonly seen in malware such as RAT, Trojan, TrojanSpy or worm to prevent the user to terminate their process. action.notable.param.rule_title = Disabling Task Manager action.notable.param.security_domain = endpoint @@ -9399,7 +9399,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_task_manager_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_task_manager_filter` [ESCU - Download Files Using Telegram - Rule] action.escu = 0 @@ -9443,15 +9443,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" |stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode Image process_id TargetFilename Hash | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `download_files_using_telegram_filter` +search = `sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" |stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode Image process_id TargetFilename Hash | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `download_files_using_telegram_filter` [ESCU - Drop IcedID License dat - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. +description = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204.002"]} action.escu.data_models = [] -action.escu.eli5 = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. +action.escu.eli5 = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-07-30 @@ -9474,7 +9474,7 @@ action.correlationsearch.label = ESCU - Drop IcedID License dat - Rule action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204.002"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. +action.notable.param.rule_description = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. action.notable.param.rule_title = Drop IcedID License dat action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -9487,7 +9487,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode= 11 TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*" OR TargetFilename="*\\programdata\\*") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id process_name Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter` +search = `sysmon` EventCode= 11 TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*" OR TargetFilename="*\\programdata\\*") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id process_name Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter` [ESCU - Dump LSASS via comsvcs DLL - Rule] action.escu = 0 @@ -9496,7 +9496,7 @@ description = Detect the usage of comsvcs.dll for dumping the lsass process. action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Detect the usage of comsvcs.dll for dumping the lsass process. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified. action.escu.creation_date = 2020-02-21 action.escu.modification_date = 2020-02-21 @@ -9518,7 +9518,7 @@ action.correlationsearch.label = ESCU - Dump LSASS via comsvcs DLL - Rule action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "Suspicious Rundll32 Activity", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Detect the usage of comsvcs.dll for dumping the lsass process. action.notable.param.rule_title = Dump LSASS via comsvcs DLL action.notable.param.security_domain = endpoint @@ -9565,7 +9565,7 @@ action.correlationsearch.label = ESCU - Dump LSASS via procdump - Rule action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. action.notable.param.rule_title = Dump LSASS via procdump @@ -9613,7 +9613,7 @@ action.correlationsearch.label = ESCU - Dump LSASS via procdump Rename - Rule action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\ During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe. action.notable.param.rule_title = Dump LSASS via procdump Rename @@ -9637,8 +9637,8 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. -action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. +action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -9659,7 +9659,7 @@ action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Modified With action.correlationsearch.annotations = {"analytic_story": ["Unusual AWS EC2 Modifications"], "cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = EC2 Instance Modified With Previously Unseen User action.notable.param.security_domain = endpoint @@ -9673,7 +9673,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter` +search = `cloudtrail` `ec2_modification_api_calls` [search `cloudtrail` `ec2_modification_api_calls` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest | `ec2_instance_modified_with_previously_unseen_user_filter` [ESCU - EC2 Instance Started In Previously Unseen Region - Rule] action.escu = 0 @@ -9682,8 +9682,8 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. +action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. action.escu.creation_date = 2018-02-23 action.escu.modification_date = 2018-02-23 action.escu.confidence = high @@ -9717,7 +9717,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter` +search = `cloudtrail` earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(),"-1d@d"), "Instance Started in a New Region","Previously Seen Region") | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where regionStatus="Instance Started in a New Region" | `ec2_instance_started_in_previously_unseen_region_filter` [ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] action.escu = 0 @@ -9726,7 +9726,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. action.escu.known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 @@ -9757,7 +9757,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter` +search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table firstTime, lastTime, arn, amiID, dest, instanceType | `ec2_instance_started_with_previously_unseen_ami_filter` [ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] action.escu = 0 @@ -9766,7 +9766,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. action.escu.known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. action.escu.creation_date = 2020-02-07 action.escu.modification_date = 2020-02-07 @@ -9784,7 +9784,7 @@ action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Started With P action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining"], "cis20": ["CIS 1"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = EC2 Instance Started With Previously Unseen Instance Type action.notable.param.security_domain = endpoint @@ -9798,7 +9798,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter` +search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | `security_content_ctime(earliest)` | `security_content_ctime(latest)` | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_instance_type_filter` [ESCU - EC2 Instance Started With Previously Unseen User - Rule] action.escu = 0 @@ -9807,8 +9807,8 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. -action.escu.known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. +action.escu.known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -9825,7 +9825,7 @@ action.correlationsearch.label = ESCU - Deprecated - EC2 Instance Started With P action.correlationsearch.annotations = {"analytic_story": ["AWS Cryptomining", "Suspicious AWS EC2 Activities"], "cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. action.notable.param.rule_title = EC2 Instance Started With Previously Unseen User action.notable.param.security_domain = endpoint @@ -9839,18 +9839,18 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter` +search = `cloudtrail` eventName=RunInstances [search `cloudtrail` eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType | `ec2_instance_started_with_previously_unseen_user_filter` [ESCU - Email Attachments With Lots Of Spaces - Rule] action.escu = 0 action.escu.enabled = 1 -description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. +description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} action.escu.data_models = ["Email"] -action.escu.eli5 = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. -action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ +action.escu.eli5 = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. +action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ **Splunk Phantom Playbook Integration**\ -If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. +If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. action.escu.known_false_positives = None at this time action.escu.creation_date = 2017-09-19 action.escu.modification_date = 2017-09-19 @@ -9868,7 +9868,7 @@ action.correlationsearch.label = ESCU - Email Attachments With Lots Of Spaces - action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware DHS Report TA18-201A ", "Suspicious Emails"], "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. +action.notable.param.rule_description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. action.notable.param.rule_title = Email Attachments With Lots Of Spaces action.notable.param.security_domain = network action.notable.param.severity = high @@ -9881,7 +9881,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?<recipient_user>.*)@" | `email_attachments_with_lots_of_spaces_filter` +search = | tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" | `email_attachments_with_lots_of_spaces_filter` [ESCU - Email files written outside of the Outlook directory - Rule] action.escu = 0 @@ -9908,7 +9908,7 @@ action.correlationsearch.label = ESCU - Email files written outside of the Outlo action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. action.notable.param.rule_title = Email files written outside of the Outlook directory action.notable.param.security_domain = endpoint @@ -9922,7 +9922,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` +search = | tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter` [ESCU - Email servers sending high volume traffic to hosts - Rule] action.escu = 0 @@ -9931,7 +9931,7 @@ description = This search looks for an increase of data transfers from your emai action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. -action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. +action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. action.escu.known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -9962,7 +9962,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter` +search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter` [ESCU - Enable RDP In Other Port Number - Rule] action.escu = 0 @@ -9993,7 +9993,7 @@ action.correlationsearch.label = ESCU - Enable RDP In Other Port Number - Rule action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1021"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a modification to registry to enable rdp to a machine with different port number. This technique was seen in some atttacker tries to do lateral movement and remote access to a compromised machine to gain control of it. action.notable.param.rule_title = Enable RDP In Other Port Number action.notable.param.security_domain = endpoint @@ -10007,15 +10007,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_key_name = "PortNumber" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `enable_rdp_in_other_port_number_filter` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_key_name = "PortNumber" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `enable_rdp_in_other_port_number_filter` [ESCU - Enumerate Users Local Group Using Telegram - Rule] action.escu = 0 action.escu.enabled = 1 -description = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. +description = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1087"]} action.escu.data_models = [] -action.escu.eli5 = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. +action.escu.eli5 = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and filter known instances of process like logonUI used in your environment. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-05-06 @@ -10038,7 +10038,7 @@ action.correlationsearch.label = ESCU - Enumerate Users Local Group Using Telegr action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1087"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "ComputerName", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. +action.notable.param.rule_description = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. action.notable.param.rule_title = Enumerate Users Local Group Using Telegram action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -10051,7 +10051,52 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4798 Process_Name = "*\\telegram.exe" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Process_Name Process_ID Account_Name Account_Domain Logon_ID Security_ID Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enumerate_users_local_group_using_telegram_filter` +search = `wineventlog_security` EventCode=4798 Process_Name = "*\\telegram.exe" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Process_Name Process_ID Account_Name Account_Domain Logon_ID Security_ID Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enumerate_users_local_group_using_telegram_filter` + +[ESCU - Esentutl SAM Copy - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = The following analytic identifies the process - `esentutl.exe` - being used to capture credentials stored in ntds.dit or the SAM file on disk. During triage, review parallel processes and determine if legitimate activity. Upon determination of illegitimate activity, take further action to isolate and contain the threat. +action.escu.mappings = {"kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1003.002"]} +action.escu.data_models = ["Endpoint"] +action.escu.eli5 = The following analytic identifies the process - `esentutl.exe` - being used to capture credentials stored in ntds.dit or the SAM file on disk. During triage, review parallel processes and determine if legitimate activity. Upon determination of illegitimate activity, take further action to isolate and contain the threat. +action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +action.escu.known_false_positives = False positives should be limited. Filter as needed. +action.escu.creation_date = 2021-08-18 +action.escu.modification_date = 2021-08-18 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Esentutl SAM Copy - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Credential Dumping"] +action.risk = 1 +action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user user$ attempting to capture credentials for offline cracking or observability. +action.risk.param._risk = [{"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Esentutl SAM Copy - Rule +action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1003.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies the process - `esentutl.exe` - being used to capture credentials stored in ntds.dit or the SAM file on disk. During triage, review parallel processes and determine if legitimate activity. Upon determination of illegitimate activity, take further action to isolate and contain the threat. +action.notable.param.rule_title = Esentutl SAM Copy +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_esentutl` Processes.process IN ("*ntds*", "*SAM*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esentutl_sam_copy_filter` [ESCU - Eventvwr UAC Bypass - Rule] action.escu = 0 @@ -10082,7 +10127,7 @@ action.correlationsearch.label = ESCU - Eventvwr UAC Bypass - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "IcedID"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following search identifies Eventvwr bypass by identifying the registry modification into a specific path that eventvwr.msc looks to (but is not valid) upon execution. A successful attack will include a suspicious command to be executed upon eventvwr.msc loading. Upon triage, review the parallel processes that have executed. Identify any additional registry modifications on the endpoint that may look suspicious. Remediate as necessary. action.notable.param.rule_title = Eventvwr UAC Bypass action.notable.param.security_domain = endpoint @@ -10096,7 +10141,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*mscfile\\shell\\open\\command\\*" by Registry.user, Registry.dest , Registry.registry_value_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `eventvwr_uac_bypass_filter` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*mscfile\\shell\\open\\command\\*" by Registry.user, Registry.dest , Registry.registry_value_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `eventvwr_uac_bypass_filter` [ESCU - Excel Spawning PowerShell - Rule] action.escu = 0 @@ -10127,7 +10172,7 @@ action.correlationsearch.label = ESCU - Excel Spawning PowerShell - Rule action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies Microsoft Excel spawning PowerShell. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written. action.notable.param.rule_title = Excel Spawning PowerShell action.notable.param.security_domain = endpoint @@ -10141,7 +10186,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_powershell_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_powershell_filter` [ESCU - Excel Spawning Windows Script Host - Rule] action.escu = 0 @@ -10172,7 +10217,7 @@ action.correlationsearch.label = ESCU - Excel Spawning Windows Script Host - Rul action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies Microsoft Excel spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64`. `cscript.exe` or `wscript.exe` spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly. action.notable.param.rule_title = Excel Spawning Windows Script Host action.notable.param.security_domain = endpoint @@ -10186,7 +10231,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_windows_script_host_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_windows_script_host_filter` [ESCU - Excessive Attempt To Disable Services - Rule] action.escu = 0 @@ -10217,7 +10262,7 @@ action.correlationsearch.label = ESCU - Excessive Attempt To Disable Services - action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1489"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic will identify suspicious series of command-line to disable several services. This technique is seen where the adversary attempts to disable security app services or other malware services to complete the objective on the compromised system. action.notable.param.rule_title = Excessive Attempt To Disable Services action.notable.param.security_domain = endpoint @@ -10231,7 +10276,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_filter` [ESCU - Excessive DNS Failures - Rule] action.escu = 0 @@ -10271,7 +10316,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter` +search = | tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 | `excessive_dns_failures_filter` [ESCU - Excessive Service Stop Attempt - Rule] action.escu = 0 @@ -10302,7 +10347,7 @@ action.correlationsearch.label = ESCU - Excessive Service Stop Attempt - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Ransomware"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1489"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies suspicious series of attempt to kill multiple services on a system using either `net.exe` or `sc.exe`. This technique is use by adversaries to terminate security services or other related services to continue there objective and evade detections. action.notable.param.rule_title = Excessive Service Stop Attempt action.notable.param.security_domain = endpoint @@ -10316,7 +10361,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR Processes.process_name = "sc.exe" OR Processes.process_name = "net1.exe" AND Processes.process="*stop*" OR Processes.process="*delete*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_service_stop_attempt_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR Processes.process_name = "sc.exe" OR Processes.process_name = "net1.exe" AND Processes.process="*stop*" OR Processes.process="*delete*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_service_stop_attempt_filter` [ESCU - Excessive Usage Of Cacls App - Rule] action.escu = 0 @@ -10347,7 +10392,7 @@ action.correlationsearch.label = ESCU - Excessive Usage Of Cacls App - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe` or `icacls.exe` application to change file or folder permission. This behavior is commonly seen where the adversary attempts to impair some users from deleting or accessing its malware components or artifact from the compromised system. action.notable.param.rule_title = Excessive Usage Of Cacls App action.notable.param.security_domain = endpoint @@ -10361,7 +10406,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "XCACLS.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_cacls_app_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "XCACLS.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_cacls_app_filter` [ESCU - Excessive Usage Of Net App - Rule] action.escu = 0 @@ -10392,7 +10437,7 @@ action.correlationsearch.label = ESCU - Excessive Usage Of Net App - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Scope:Local", "Stage:Execution"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1531"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_name", "role": ["Process", "Attacker"], "type": "Process Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies excessive usage of `net.exe` or `net1.exe` within a bucket of time (1 minute). This behavior was seen in a Monero incident where the adversary attempts to create many users, delete and disable users as part of its malicious behavior. action.notable.param.rule_title = Excessive Usage Of Net App action.notable.param.security_domain = endpoint @@ -10406,7 +10451,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR Processes.process_name = "net1.exe" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_net_app_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR Processes.process_name = "net1.exe" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_net_app_filter` [ESCU - Excessive Usage Of SC Service Utility - Rule] action.escu = 0 @@ -10446,7 +10491,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode = 1 process_name = "sc.exe" | bucket _time span=15m | stats values(process) as process count as numScExe by Computer, _time | eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by Computer | eval upperThreshold=(avgScExe + stdScExe *3) | eval isOutlier=if(avgScExe > 5 and avgScExe >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_sc_service_utility_filter` +search = `sysmon` EventCode = 1 process_name = "sc.exe" | bucket _time span=15m | stats values(process) as process count as numScExe by Computer, _time | eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by Computer | eval upperThreshold=(avgScExe + stdScExe *3) | eval isOutlier=if(avgScExe > 5 and avgScExe >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_sc_service_utility_filter` [ESCU - Excessive Usage Of Taskkill - Rule] action.escu = 0 @@ -10477,7 +10522,7 @@ action.correlationsearch.label = ESCU - Excessive Usage Of Taskkill - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies excessive usage of `taskkill.exe` application. This application is commonly used by adversaries to evade detections by killing security product processes or even other processes to evade detection. action.notable.param.rule_title = Excessive Usage Of Taskkill action.notable.param.security_domain = endpoint @@ -10491,7 +10536,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_taskkill_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_taskkill_filter` [ESCU - Excessive Usage of NSLOOKUP App - Rule] action.escu = 0 @@ -10535,7 +10580,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode = 1 process_name = "nslookup.exe" | bucket _time span=15m | stats count as numNsLookup by Computer, _time | eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by Computer | eval upperThreshold=(avgNsLookup + stdNsLookup *3) | eval isOutlier=if(avgNsLookup > 20 and avgNsLookup >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_nslookup_app_filter` +search = `sysmon` EventCode = 1 process_name = "nslookup.exe" | bucket _time span=15m | stats count as numNsLookup by Computer, _time | eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by Computer | eval upperThreshold=(avgNsLookup + stdNsLookup *3) | eval isOutlier=if(avgNsLookup > 20 and avgNsLookup >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_nslookup_app_filter` [ESCU - Excessive number of distinct processes created in Windows Temp folder - Rule] action.escu = 0 @@ -10544,7 +10589,7 @@ description = This analytic will identify suspicious series of process execution action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This analytic will identify suspicious series of process executions. We have observed that post exploit framework tools like Koadic and Meterpreter will launch an excessive number of processes with distinct file paths from Windows\Temp to execute actions on objective. This behavior is extremely anomalous compared to typical application behaviors that use Windows\Temp. -action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the full process path in the process field of CIM's Process data model. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used. +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the full process path in the process field of CIM's Process data model. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used. action.escu.known_false_positives = Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold. Filter as needed. action.escu.creation_date = 2021-06-03 action.escu.modification_date = 2021-06-03 @@ -10566,7 +10611,7 @@ action.correlationsearch.label = ESCU - Excessive number of distinct processes c action.correlationsearch.annotations = {"analytic_story": ["meterpreter"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic will identify suspicious series of process executions. We have observed that post exploit framework tools like Koadic and Meterpreter will launch an excessive number of processes with distinct file paths from Windows\Temp to execute actions on objective. This behavior is extremely anomalous compared to typical application behaviors that use Windows\Temp. action.notable.param.rule_title = Excessive number of distinct processes created in Windows Temp folder action.notable.param.security_domain = endpoint @@ -10580,7 +10625,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process distinct_count(Processes.process) as distinct_process_count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m | where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_distinct_processes_created_in_windows_temp_folder_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process distinct_count(Processes.process) as distinct_process_count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m | where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_distinct_processes_created_in_windows_temp_folder_filter` [ESCU - Excessive number of service control start as disabled - Rule] action.escu = 0 @@ -10589,7 +10634,7 @@ description = This detection targets behaviors observed when threat actors have action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"]} action.escu.data_models = [] action.escu.eli5 = This detection targets behaviors observed when threat actors have used sc.exe to modify services. We observed malware in a honey pot spawning numerous sc.exe processes in a short period of time, presumably to impair defenses, possibly to block others from compromising the same machine. This detection will alert when we see both an excessive number of sc.exe processes launched with specific commandline arguments to disable the start of certain services. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Legitimate programs and administrators will execute sc.exe with the start disabled flag. It is possible, but unlikely from the telemetry of normal Windows operation we observed, that sc.exe will be called more than seven times in a short period of time. action.escu.creation_date = 2021-06-25 action.escu.modification_date = 2021-06-25 @@ -10611,7 +10656,7 @@ action.correlationsearch.label = ESCU - Excessive number of service control star action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This detection targets behaviors observed when threat actors have used sc.exe to modify services. We observed malware in a honey pot spawning numerous sc.exe processes in a short period of time, presumably to impair defenses, possibly to block others from compromising the same machine. This detection will alert when we see both an excessive number of sc.exe processes launched with specific commandline arguments to disable the start of certain services. action.notable.param.rule_title = Excessive number of service control start as disabled action.notable.param.security_domain = endpoint @@ -10625,7 +10670,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_cmdlines values(Processes.process_id) as process_ids min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "sc.exe" AND Processes.process="*start= disabled*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_id, _time span=30m | where distinct_cmdlines >= 8 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_service_control_start_as_disabled_filter` +search = | tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_cmdlines values(Processes.process_id) as process_ids min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "sc.exe" AND Processes.process="*start= disabled*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_id, _time span=30m | where distinct_cmdlines >= 8 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_service_control_start_as_disabled_filter` [ESCU - Excessive number of taskhost processes - Rule] action.escu = 0 @@ -10656,7 +10701,7 @@ action.correlationsearch.label = ESCU - Excessive number of taskhost processes - action.correlationsearch.annotations = {"analytic_story": ["Meterpreter"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1033"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This detection targets behaviors observed in post exploit kits like Meterpreter and Koadic that are run in memory. We have observed that these tools must invoke an excessive number of taskhost.exe and taskhostex.exe processes to complete various actions (discovery, lateral movement, etc.). It is extremely uncommon in the course of normal operations to see so many distinct taskhost and taskhostex processes running concurrently in a short time frame. action.notable.param.rule_title = Excessive number of taskhost processes action.notable.param.security_domain = endpoint @@ -10670,7 +10715,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_ids min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe" BY Processes.dest Processes.process_name _time span=1h | `drop_dm_object_name(Processes)` | eval pid_count=mvcount(process_ids) | eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0) | eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0) | stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count by _time, dest, firstTime, lastTime | where taskhost_count > 10 and taskhostex_count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_taskhost_processes_filter` +search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_ids min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe" BY Processes.dest Processes.process_name _time span=1h | `drop_dm_object_name(Processes)` | eval pid_count=mvcount(process_ids) | eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0) | eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0) | stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count by _time, dest, firstTime, lastTime | where taskhost_count > 10 and taskhostex_count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_taskhost_processes_filter` [ESCU - Executables Or Script Creation In Suspicious Path - Rule] action.escu = 0 @@ -10701,7 +10746,7 @@ action.correlationsearch.label = ESCU - Executables Or Script Creation In Suspic action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1036"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_id", "role": ["Attacker"], "type": "Process"}, {"name": "file_name", "role": ["Other", "Attacker"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This analytic will identify suspicious executable or scripts (known file extensions) in list of suspicious file path in Windows. This technique is used by adversaries to evade detection. The suspicious file path are known paths used in the wild and are not common to have executable or scripts. action.notable.param.rule_title = Executables Or Script Creation In Suspicious Path action.notable.param.security_domain = endpoint @@ -10746,7 +10791,7 @@ action.correlationsearch.label = ESCU - Execute Javascript With Jscript COM CLSI action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.005"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_id", "role": ["Attacker"], "type": "Process"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic will identify suspicious process of cscript.exe where it tries to execute javascript using jscript.encode CLSID (COM OBJ). This technique was seen in ransomware (reddot ransomware) where it execute javascript with this com object with combination of amsi disabling technique. action.notable.param.rule_title = Execute Javascript With Jscript COM CLSID action.notable.param.security_domain = endpoint @@ -10760,7 +10805,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cscript.exe" Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" by Processes.parent_process_name Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `execute_javascript_with_jscript_com_clsid_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cscript.exe" Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" by Processes.parent_process_name Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `execute_javascript_with_jscript_com_clsid_filter` [ESCU - Execution of File With Spaces Before Extension - Rule] action.escu = 0 @@ -10787,7 +10832,7 @@ action.correlationsearch.label = ESCU - Deprecated - Execution of File With Spac action.correlationsearch.annotations = {"analytic_story": ["Windows File Extension and Association Abuse", "Masquerading - Rename System Utilities"], "cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. action.notable.param.rule_title = Execution of File With Spaces Before Extension action.notable.param.security_domain = endpoint @@ -10801,15 +10846,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process_path) as process_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* .*" by Processes.dest Processes.user Processes.process Processes.process_name | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_spaces_before_extension_filter` [ESCU - Execution of File with Multiple Extensions - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. +description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. +action.escu.eli5 = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. action.escu.known_false_positives = None identified. action.escu.creation_date = 2020-11-18 @@ -10832,8 +10877,8 @@ action.correlationsearch.label = ESCU - Execution of File with Multiple Extensio action.correlationsearch.annotations = {"analytic_story": ["Windows File Extension and Association Abuse", "Masquerading - Rename System Utilities"], "cis20": ["CIS 3", "CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process", "role": ["Parent Process", "Attacker"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. action.notable.param.rule_title = Execution of File with Multiple Extensions action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -10851,10 +10896,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Extended Period Without Successful Netbackup Backups - Rule] action.escu = 0 action.escu.enabled = 1 -description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. +description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} action.escu.data_models = [] -action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. +action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. action.escu.how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. action.escu.known_false_positives = None identified action.escu.creation_date = 2017-09-12 @@ -10873,8 +10918,8 @@ action.correlationsearch.label = ESCU - Deprecated - Extended Period Without Suc action.correlationsearch.annotations = {"analytic_story": ["Monitor Backup Solution"], "cis20": ["CIS 10"], "nist": ["PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] -action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. action.notable.param.rule_title = Extended Period Without Successful Netbackup Backups action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -10887,7 +10932,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter` +search = `netbackup` MESSAGE="Disk/Partition backup completed successfully." | stats latest(_time) as latestTime by COMPUTERNAME | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest | eval isOutlier=if(latestTime <= relative_time(now(), "-7d@d"), 1, 0) | search isOutlier=1 | table latestTime, dest | `extended_period_without_successful_netbackup_backups_filter` [ESCU - Extract SAM from Registry - Rule] action.escu = 0 @@ -10918,7 +10963,7 @@ action.correlationsearch.label = ESCU - Extract SAM from Registry - Rule action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Credential Dumping"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Credential Access", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_id", "role": ["Parent Process", "Attacker"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of `reg.exe` exporting Windows Registry hives containing credentials. Adversaries may use this technique to export registry hives for offline credential access attacks. Typically found executed from a untrusted process or script. Upon execution, a file will be written to disk. action.notable.param.rule_title = Extract SAM from Registry action.notable.param.security_domain = endpoint @@ -10963,7 +11008,7 @@ action.correlationsearch.label = ESCU - File with Samsam Extension - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 100, "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "file_name", "role": ["Other", "Attacker"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The search looks for file writes with extensions consistent with a SamSam ransomware attack. action.notable.param.rule_title = File with Samsam Extension action.notable.param.security_domain = endpoint @@ -10977,7 +11022,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?<file_extension>\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` [ESCU - First Time Seen Child Process of Zoom - Rule] action.escu = 0 @@ -10987,7 +11032,7 @@ action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Acti action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. -action.escu.known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +action.escu.known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. action.escu.creation_date = 2020-05-20 action.escu.modification_date = 2020-05-20 action.escu.confidence = high @@ -11008,7 +11053,7 @@ action.correlationsearch.label = ESCU - First Time Seen Child Process of Zoom - action.correlationsearch.annotations = {"analytic_story": ["Suspicious Zoom Child Processes"], "cis20": ["CIS 3", "CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_name", "role": ["Attacker", "Child Process"], "type": "Process Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. action.notable.param.rule_title = First Time Seen Child Process of Zoom action.notable.param.security_domain = endpoint @@ -11022,7 +11067,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` +search = | tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` [ESCU - First Time Seen Running Windows Service - Rule] action.escu = 0 @@ -11049,7 +11094,7 @@ action.correlationsearch.label = ESCU - First Time Seen Running Windows Service action.correlationsearch.annotations = {"analytic_story": ["Windows Service Abuse", "Orangeworm Attack Group", "NOBELIUM Group"], "cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1569.002"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached. action.notable.param.rule_title = First Time Seen Running Windows Service action.notable.param.security_domain = endpoint @@ -11063,7 +11108,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter` +search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter` [ESCU - First time seen command line argument - Rule] action.escu = 0 @@ -11072,7 +11117,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001", "T1059.003"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"&#151;which creates a lookup file called `previously_seen_cmd_line_arguments.csv`&#151;a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. action.escu.known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -11103,7 +11148,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process Processes.process_name Processes.parent_process_name Processes.dest| `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = cmd.exe Processes.process = "* /c *" by Processes.process | `drop_dm_object_name(Processes)` | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by process | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table process] | `first_time_seen_command_line_argument_filter` [ESCU - FodHelper UAC Bypass - Rule] action.escu = 0 @@ -11142,7 +11187,7 @@ action.correlationsearch.label = ESCU - FodHelper UAC Bypass - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "IcedID"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation"], "impact": 90, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1112", "T1548.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Fodhelper.exe has a known UAC bypass as it attempts to look for specific registry keys upon execution, that do not exist. Therefore, an attacker can write its malicious commands in these registry keys to be executed by fodhelper.exe with the highest privilege. \ 1. `HKCU:\Software\Classes\ms-settings\shell\open\command`\ 1. `HKCU:\Software\Classes\ms-settings\shell\open\command\DelegateExecute`\ @@ -11272,7 +11317,7 @@ action.correlationsearch.label = ESCU - GCP Detect gcploit framework - Rule action.correlationsearch.annotations = {"analytic_story": ["GCP Cross Account Activity"], "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'src'] +action.notable.param.nes_fields = ['user', 'src'] action.notable.param.rule_description = This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts. action.notable.param.rule_title = GCP Detect gcploit framework action.notable.param.security_domain = threat @@ -11353,7 +11398,7 @@ action.correlationsearch.label = ESCU - Deprecated - GCP GCR container uploaded action.correlationsearch.annotations = {"analytic_story": ["Container Implantation Monitoring and Investigation"], "mitre_attack": ["T1525"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. action.notable.param.rule_title = GCP GCR container uploaded action.notable.param.security_domain = threat @@ -11367,15 +11412,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` +search = |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Storage where Storage.event_name=storage.objects.create by Storage.src_user Storage.account Storage.action Storage.bucket_name Storage.event_name Storage.http_user_agent Storage.msg Storage.object_path | `drop_dm_object_name("Storage")` | `gcp_gcr_container_uploaded_filter` [ESCU - GCP Kubernetes cluster pod scan detection - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods +description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]} action.escu.data_models = [] -action.escu.eli5 = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods +action.escu.eli5 = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods action.escu.how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. action.escu.known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. action.escu.creation_date = 2020-07-17 @@ -11394,7 +11439,7 @@ action.correlationsearch.label = ESCU - GCP Kubernetes cluster pod scan detectio action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Scanning Activity"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods +action.notable.param.rule_description = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods action.notable.param.rule_title = GCP Kubernetes cluster pod scan detection action.notable.param.security_domain = threat action.notable.param.severity = high @@ -11447,7 +11492,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `gcp_kubernetes_cluster_scan_detection_filter` +search = `google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerIp!=127.0.0.1 data.protoPayload.requestMetadata.callerIp!=::1 "data.labels.authorization.k8s.io/decision"=forbid "data.protoPayload.status.message"=PERMISSION_DENIED data.protoPayload.authenticationInfo.principalEmail="system:anonymous" | rename data.protoPayload.requestMetadata.callerIp as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_name values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent by src_ip data.resource.labels.cluster_name | rename data.resource.labels.cluster_name as cluster_name| `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `gcp_kubernetes_cluster_scan_detection_filter` [ESCU - GPUpdate with no Command Line Arguments with Network - Rule] action.escu = 0 @@ -11478,7 +11523,7 @@ action.correlationsearch.label = ESCU - GPUpdate with no Command Line Arguments action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Command And Control"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_name", "role": ["Parent Process", "Attacker"], "type": "Process Name"}, {"name": "connection_to_CNC", "role": ["Other"], "type": "IP Address"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies gpupdate.exe with no command line arguments and with a network connection. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = GPUpdate with no Command Line Arguments with Network action.notable.param.security_domain = endpoint @@ -11492,7 +11537,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(gpupdate\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port| `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `gpupdate_with_no_command_line_arguments_with_network_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(gpupdate\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port| `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `gpupdate_with_no_command_line_arguments_with_network_filter` [ESCU - GSuite Email Suspicious Attachment - Rule] action.escu = 0 @@ -11538,6 +11583,50 @@ realtime_schedule = 0 is_visible = false search = `gsuite_gmail` "attachment{}.file_extension_type" IN ("pl", "py", "rb", "sh", "bat", "exe", "dll", "cpl", "com", "js", "vbs", "ps1", "reg","swf", "cmd", "go") | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size by destination{}.service num_message_attachments subject destination{}.address source.address | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_suspicious_attachment_filter` +[ESCU - Github Commit Changes In Master - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search is to detect a pushed or commit to master or main branch. This is to avoid unwanted modification to master without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch +action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1199"]} +action.escu.data_models = [] +action.escu.eli5 = This search is to detect a pushed or commit to master or main branch. This is to avoid unwanted modification to master without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to github logs having the fork, commit, push metadata that can be use to monitor the changes in a github project. +action.escu.known_false_positives = admin can do changes directly to master branch +action.escu.creation_date = 2021-08-20 +action.escu.modification_date = 2021-08-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Github Commit Changes In Master - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["DevSecOps"] +action.risk = 1 +action.risk.param._risk_message = suspicious commit by $commit.commit.author.email$ to main branch +action.risk.param._risk = [{"risk_object_field": "commit.commit.author.email", "risk_object_type": "user", "risk_score": 9}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Github Commit Changes In Master - Rule +action.correlationsearch.annotations = {"analytic_story": ["DevSecOps"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1199"], "observable": [{"name": "commit.commit.author.email", "role": ["attacker"], "type": "User"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search is to detect a pushed or commit to master or main branch. This is to avoid unwanted modification to master without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch +action.notable.param.rule_title = Github Commit Changes In Master +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `github` branches{}.name = main OR branches{}.name = master | stats count min(_time) as firstTime max(_time) as lastTime by commit.author.html_url commit.commit.author.email commit.author.login commit.commit.message repository.pushed_at commit.commit.committer.date | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_commit_changes_in_master_filter` + [ESCU - Gsuite Drive Share In External Email - Rule] action.escu = 0 action.escu.enabled = 1 @@ -11582,6 +11671,94 @@ realtime_schedule = 0 is_visible = false search = `gsuite_drive` NOT (email IN("", "null")) | rex field=parameters.owner "[^@]+@(?[^@]+)" | rex field=email "[^@]+@(?[^@]+)" | where src_domain = "internal_test_email.com" and not dest_domain = "internal_test_email.com" | stats values(parameters.doc_title) as doc_title, values(parameters.doc_type) as doc_types, values(email) as dst_email_list, values(parameters.visibility) as visibility, count min(_time) as firstTime max(_time) as lastTime by parameters.owner | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_drive_share_in_external_email_filter` +[ESCU - Gsuite Email Suspicious Subject With Attachment - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search is to detect a gsuite email contains suspicious subject having known file type used in spear phishing. This technique is a common and effective entry vector of attacker to compromise a network by luring the user to click or execute the suspicious attachment send from external email account because of the effective social engineering of subject related to delivery, bank and so on. On the other hand this detection may catch a normal email traffic related to legitimate transaction so better to check the email sender, spelling and etc. avoid click link or opening the attachment if you are not expecting this type of e-mail. +action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} +action.escu.data_models = [] +action.escu.eli5 = This search is to detect a gsuite email contains suspicious subject having known file type used in spear phishing. This technique is a common and effective entry vector of attacker to compromise a network by luring the user to click or execute the suspicious attachment send from external email account because of the effective social engineering of subject related to delivery, bank and so on. On the other hand this detection may catch a normal email traffic related to legitimate transaction so better to check the email sender, spelling and etc. avoid click link or opening the attachment if you are not expecting this type of e-mail. +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +action.escu.known_false_positives = normal user or normal transaction may contain the subject and file type attachment that this detection try to search. +action.escu.creation_date = 2021-08-19 +action.escu.modification_date = 2021-08-19 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Gsuite Email Suspicious Subject With Attachment - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["DevSecOps"] +action.risk = 1 +action.risk.param._risk_message = suspicious email from $source.address$ to $destination{}.address$ +action.risk.param._risk = [{"risk_object_field": "source.address", "risk_object_type": "user", "risk_score": 25}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Gsuite Email Suspicious Subject With Attachment - Rule +action.correlationsearch.annotations = {"analytic_story": ["DevSecOps"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "source.address", "role": ["attacker"], "type": "User"}, {"name": "destination{}.address", "role": ["Victim"], "type": "User"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search is to detect a gsuite email contains suspicious subject having known file type used in spear phishing. This technique is a common and effective entry vector of attacker to compromise a network by luring the user to click or execute the suspicious attachment send from external email account because of the effective social engineering of subject related to delivery, bank and so on. On the other hand this detection may catch a normal email traffic related to legitimate transaction so better to check the email sender, spelling and etc. avoid click link or opening the attachment if you are not expecting this type of e-mail. +action.notable.param.rule_title = Gsuite Email Suspicious Subject With Attachment +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `gsuite_gmail` num_message_attachments > 0 subject IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "* fedex *", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") attachment{}.file_extension_type IN ("doc", "docx", "xls", "xlsx", "ppt", "pptx", "pdf", "zip", "rar", "html","htm","hta") | rex field=source.from_header_address "[^@]+@(?[^@]+)" | rex field=destination{}.address "[^@]+@(?[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size by destination{}.service num_message_attachments subject destination{}.address source.address | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_suspicious_subject_with_attachment_filter` + +[ESCU - Gsuite Email With Known Abuse Web Service Link - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This analytics is to detect a gmail containing a link that are known to be abused by malware or attacker like pastebin, telegram and discord to deliver malicious payload. This event can encounter some normal email traffic within organization and external email that normally using this application and services. +action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} +action.escu.data_models = [] +action.escu.eli5 = This analytics is to detect a gmail containing a link that are known to be abused by malware or attacker like pastebin, telegram and discord to deliver malicious payload. This event can encounter some normal email traffic within organization and external email that normally using this application and services. +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +action.escu.known_false_positives = normal email contains this link that are known application within the organization or network can be catched by this detection. +action.escu.creation_date = 2021-08-23 +action.escu.modification_date = 2021-08-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Gsuite Email With Known Abuse Web Service Link - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["DevSecOps"] +action.risk = 1 +action.risk.param._risk_message = suspicious email from $source.address$ to $destination{}.address$ +action.risk.param._risk = [{"risk_object_field": "source.address", "risk_object_type": "user", "risk_score": 25}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Gsuite Email With Known Abuse Web Service Link - Rule +action.correlationsearch.annotations = {"analytic_story": ["DevSecOps"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "source.address", "role": ["attacker"], "type": "User"}, {"name": "destination{}.address", "role": ["Victim"], "type": "User"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This analytics is to detect a gmail containing a link that are known to be abused by malware or attacker like pastebin, telegram and discord to deliver malicious payload. This event can encounter some normal email traffic within organization and external email that normally using this application and services. +action.notable.param.rule_title = Gsuite Email With Known Abuse Web Service Link +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me") | rex field=source.from_header_address "[^@]+@(?[^@]+)" | rex field=destination{}.address "[^@]+@(?[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" |stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) as lastTime count by is_spam source.address source.from_header_address subject destination{}.address | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_with_known_abuse_web_service_link_filter` + [ESCU - Gsuite Outbound Email With Attachment To External Domain - Rule] action.escu = 0 action.escu.enabled = 1 @@ -11626,6 +11803,51 @@ realtime_schedule = 0 is_visible = false search = `gsuite_gmail` num_message_attachments > 0 | rex field=source.from_header_address "[^@]+@(?[^@]+)" | rex field=destination{}.address "[^@]+@(?[^@]+)" | where source_domain="internal_test_email.com" and not dest_domain="internal_test_email.com" | stats values(subject) as subject, values(source.from_header_address) as src_domain_list, count as numEvents, dc(source.from_header_address) as numSrcAddresses, min(_time) as firstTime max(_time) as lastTime by dest_domain | where numSrcAddresses < 20 |sort - numSrcAddresses | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_outbound_email_with_attachment_to_external_domain_filter` +[ESCU - Gsuite Suspicious Shared File Name - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search is to detect a shared file in google drive with suspicious file name that are commonly used by spear phishing campaign. This technique is very popular to lure the user by running a malicious document or click a malicious link within the shared file that will redirected to malicious website. This detection can also catch some normal email communication between organization and its external customer. +action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} +action.escu.data_models = [] +action.escu.eli5 = This search is to detect a shared file in google drive with suspicious file name that are commonly used by spear phishing campaign. This technique is very popular to lure the user by running a malicious document or click a malicious link within the shared file that will redirected to malicious website. This detection can also catch some normal email communication between organization and its external customer. +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +action.escu.known_false_positives = normal user or normal transaction may contain the subject and file type attachment that this detection try to search +action.escu.creation_date = 2021-08-23 +action.escu.modification_date = 2021-08-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Gsuite Suspicious Shared File Name - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["DevSecOps"] +action.risk = 1 +action.risk.param._risk_message = suspicious share gdrive from $parameters.owner$ to $email$ namely as $parameters.doc_title$ +action.risk.param._risk = [{"risk_object_field": "parameters.owner", "risk_object_type": "user", "risk_score": 9}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Gsuite Suspicious Shared File Name - Rule +action.correlationsearch.annotations = {"analytic_story": ["DevSecOps"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Reconnaissance"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "parameters.owner", "role": ["attacker"], "type": "User"}, {"name": "email", "role": ["Victim"], "type": "User"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.nes_fields = ['user'] +action.notable.param.rule_description = This search is to detect a shared file in google drive with suspicious file name that are commonly used by spear phishing campaign. This technique is very popular to lure the user by running a malicious document or click a malicious link within the shared file that will redirected to malicious website. This detection can also catch some normal email communication between organization and its external customer. +action.notable.param.rule_title = Gsuite Suspicious Shared File Name +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `gsuite_drive` parameters.owner_is_team_drive=false "parameters.doc_title" IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "*fedex*", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") parameters.doc_type IN ("document","pdf", "msexcel", "msword", "spreadsheet", "presentation") | rex field=parameters.owner "[^@]+@(?[^@]+)" | rex field=parameters.target_user "[^@]+@(?[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | stats count min(_time) as firstTime max(_time) as lastTime by email parameters.owner parameters.target_user parameters.doc_title parameters.doc_type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_suspicious_shared_file_name_filter` + [ESCU - Hide User Account From Sign-In Screen - Rule] action.escu = 0 action.escu.enabled = 1 @@ -11655,7 +11877,7 @@ action.correlationsearch.label = ESCU - Hide User Account From Sign-In Screen - action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "registry_value_name", "role": ["Attacker"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies a suspicious registry modification to hide a user account on the Windows Login screen. This technique was seen in some tradecraft where the adversary will create a hidden user account with Admin privileges in login screen to avoid noticing by the user that they already compromise and to persist on that said machine. action.notable.param.rule_title = Hide User Account From Sign-In Screen action.notable.param.security_domain = endpoint @@ -11669,7 +11891,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_name = "DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `hide_user_account_from_sign_in_screen_filter` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_name = "DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `hide_user_account_from_sign_in_screen_filter` [ESCU - Hiding Files And Directories With Attrib exe - Rule] action.escu = 0 @@ -11678,7 +11900,7 @@ description = Attackers leverage an existing Windows binary, attrib.exe, to mark action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1222.001"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -11700,7 +11922,7 @@ action.correlationsearch.label = ESCU - Hiding Files And Directories With Attrib action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Windows Persistence Techniques"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Persistence"], "impact": 90, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1222.001"], "nist": ["DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process", "role": ["Attacker", "Parent Process"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. action.notable.param.rule_title = Hiding Files And Directories With Attrib exe action.notable.param.security_domain = endpoint @@ -11714,7 +11936,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` +search = | tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `hiding_files_and_directories_with_attrib_exe_filter` [ESCU - High File Deletion Frequency - Rule] action.escu = 0 @@ -11745,7 +11967,7 @@ action.correlationsearch.label = ESCU - High File Deletion Frequency - Rule action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1485"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "Computer", "role": ["Victim"], "type": "Endpoint"}, {"name": "deleted_files", "role": ["Target"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search looks for high frequency of file deletion relative to process name and process id. These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the original files to be deleted as soon it was replace as encrypted data. action.notable.param.rule_title = High File Deletion Frequency action.notable.param.security_domain = endpoint @@ -11759,7 +11981,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=23 TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID |where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `high_file_deletion_frequency_filter` +search = `sysmon` EventCode=23 TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") | stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID |where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `high_file_deletion_frequency_filter` [ESCU - High Number of Login Failures from a single source - Rule] action.escu = 0 @@ -11786,7 +12008,7 @@ action.correlationsearch.label = ESCU - High Number of Login Failures from a sin action.correlationsearch.annotations = {"analytic_story": ["Office 365 Detections"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1110.001"], "nist": ["DE.DP", "DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment. action.notable.param.rule_title = High Number of Login Failures from a single source action.notable.param.security_domain = threat @@ -11800,7 +12022,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter` +search = `o365_management_activity` Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory | stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app | search accounts_locked >= 5| `high_number_of_login_failures_from_a_single_source_filter` [ESCU - High Process Termination Frequency - Rule] action.escu = 0 @@ -11844,7 +12066,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=5 |bin _time span=3s |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode ProcessID | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_filter` +search = `sysmon` EventCode=5 |bin _time span=3s |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime count by Computer EventCode ProcessID | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_filter` [ESCU - Hosts receiving high volume of network traffic from email server - Rule] action.escu = 0 @@ -11853,7 +12075,7 @@ description = This search looks for an increase of data transfers from your emai action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. -action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. +action.escu.how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. action.escu.known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -11884,7 +12106,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` +search = | tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter` [ESCU - ICACLS Grant Command - Rule] action.escu = 0 @@ -11915,7 +12137,7 @@ action.correlationsearch.label = ESCU - ICACLS Grant Command - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig", "Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies potential adversaries that modify the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft and coinminer scripts to evade detections and restrict access to their component files. action.notable.param.rule_title = ICACLS Grant Command action.notable.param.security_domain = endpoint @@ -11929,7 +12151,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/grant*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_grant_command_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/grant*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_grant_command_filter` [ESCU - Icacls Deny Command - Rule] action.escu = 0 @@ -11960,7 +12182,7 @@ action.correlationsearch.label = ESCU - Icacls Deny Command - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies a potential adversary that changes the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft or coinminer scripts. This behavior is meant to evade detection and prevent access to their component files. action.notable.param.rule_title = Icacls Deny Command action.notable.param.security_domain = endpoint @@ -11974,7 +12196,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/deny*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_deny_command_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/deny*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_deny_command_filter` [ESCU - IcedID Exfiltrated Archived File Creation - Rule] action.escu = 0 @@ -12018,7 +12240,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode= 11 (TargetFilename = "*\\passff.tar" OR TargetFilename = "*\\cookie.tar") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id process_name Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_filter` +search = `sysmon` EventCode= 11 (TargetFilename = "*\\passff.tar" OR TargetFilename = "*\\cookie.tar") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id process_name Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_filter` [ESCU - Identify New User Accounts - Rule] action.escu = 0 @@ -12058,7 +12280,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` +search = | from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` [ESCU - Kerberoasting spn request with RC4 encryption - Rule] action.escu = 0 @@ -12133,7 +12355,7 @@ action.correlationsearch.label = ESCU - Known Services Killed by Ransomware - Ru action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Message", "role": ["Other"], "type": "Other"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search detects a suspicioous termination of known services killed by ransomware before encrypting files in a compromised machine. This technique is commonly seen in most of ransomware now a days to avoid exception error while accessing the targetted files it wants to encrypts because of the open handle of those services to the targetted file. action.notable.param.rule_title = Known Services Killed by Ransomware action.notable.param.security_domain = endpoint @@ -12147,7 +12369,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_system` EventCode=7036 Message IN ("*Volume Shadow Copy*","*VSS*", "*backup*", "*sophos*", "*sql*", "*memtas*", "*mepocs*", "*veeam*", "*svc$*") Message="*service entered the stopped state*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message dest Type | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `known_services_killed_by_ransomware_filter` +search = `wineventlog_system` EventCode=7036 Message IN ("*Volume Shadow Copy*","*VSS*", "*backup*", "*sophos*", "*sql*", "*memtas*", "*mepocs*", "*veeam*", "*svc$*") Message="*service entered the stopped state*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message dest Type | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `known_services_killed_by_ransomware_filter` [ESCU - Kubernetes AWS detect RBAC authorization by account - Rule] action.escu = 0 @@ -12334,7 +12556,7 @@ action.correlationsearch.label = ESCU - Kubernetes AWS detect suspicious kubectl action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "kill_chain_phases": ["Lateral Movement"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context action.notable.param.rule_title = Kubernetes AWS detect suspicious kubectl calls action.notable.param.security_domain = threat @@ -12695,7 +12917,7 @@ action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect RBAC action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences action.notable.param.rule_title = Kubernetes GCP detect RBAC authorizations by account action.notable.param.security_domain = threat @@ -12736,7 +12958,7 @@ action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect most action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision action.notable.param.rule_title = Kubernetes GCP detect most active service accounts by pod action.notable.param.security_domain = threat @@ -12777,7 +12999,7 @@ action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect sensi action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "kill_chain_phases": ["Lateral Movement"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets action.notable.param.rule_title = Kubernetes GCP detect sensitive object access action.notable.param.security_domain = threat @@ -12818,7 +13040,7 @@ action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect sensi action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Role Activity"], "kill_chain_phases": ["Lateral Movement"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets action.notable.param.rule_title = Kubernetes GCP detect sensitive role access action.notable.param.security_domain = threat @@ -12859,7 +13081,7 @@ action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect servi action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "kill_chain_phases": ["Lateral Movement"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI action.notable.param.rule_title = Kubernetes GCP detect service accounts forbidden failure access action.notable.param.security_domain = threat @@ -12900,7 +13122,7 @@ action.correlationsearch.label = ESCU - Deprecated - Kubernetes GCP detect suspi action.correlationsearch.annotations = {"analytic_story": ["Kubernetes Sensitive Object Access Activity"], "kill_chain_phases": ["Lateral Movement"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context action.notable.param.rule_title = Kubernetes GCP detect suspicious kubectl calls action.notable.param.security_domain = threat @@ -12916,6 +13138,138 @@ realtime_schedule = 0 is_visible = false search = `google_gcp_pubsub_message` data.protoPayload.requestMetadata.callerSuppliedUserAgent=kubectl* src_user=system:unsecured OR src_user=system:anonymous | table src_ip src_user data.protoPayload.requestMetadata.callerSuppliedUserAgent data.protoPayload.authorizationInfo{}.granted object_path |dedup src_ip src_user |`kubernetes_gcp_detect_suspicious_kubectl_calls_filter` +[ESCU - Kubernetes Nginx Ingress LFI - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search uses the Kubernetes logs from a nginx ingress controller to detect local file inclusion attacks. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search uses the Kubernetes logs from a nginx ingress controller to detect local file inclusion attacks. +action.escu.how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes. +action.escu.known_false_positives = unknown +action.escu.creation_date = 2021-08-20 +action.escu.modification_date = 2021-08-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Nginx Ingress LFI - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Dev Sec Ops"] +action.risk = 1 +action.risk.param._risk_message = Local File Inclusion Attack detected on $host$ +action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 49}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Nginx Ingress LFI - Rule +action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search uses the Kubernetes logs from a nginx ingress controller to detect local file inclusion attacks. +action.notable.param.rule_title = Kubernetes Nginx Ingress LFI +action.notable.param.security_domain = network +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_container_controller` | rex field=_raw "^(?\S+)\s+-\s+-\s+\[(?[^\]]*)\]\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\"(?[^\"]*)\"\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\[(?[^\]]*)\]\s\[(?[^\]]*)\]\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)" | lookup local_file_inclusion_paths local_file_inclusion_paths AS request OUTPUT lfi_path | search lfi_path=yes | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | rex field=request "^(?\S+)\s(?\S+)\s" | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, status, url, http_method, host, http_user_agent, proxy | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_lfi_filter` + +[ESCU - Kubernetes Nginx Ingress RFI - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search uses the Kubernetes logs from a nginx ingress controller to detect remote file inclusion attacks. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search uses the Kubernetes logs from a nginx ingress controller to detect remote file inclusion attacks. +action.escu.how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes. +action.escu.known_false_positives = unknown +action.escu.creation_date = 2021-08-23 +action.escu.modification_date = 2021-08-23 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Nginx Ingress RFI - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Dev Sec Ops"] +action.risk = 1 +action.risk.param._risk_message = Remote File Inclusion Attack detected on $host$ +action.risk.param._risk = [{"risk_object_field": "src_ip", "risk_object_type": "system", "risk_score": 49}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Nginx Ingress RFI - Rule +action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "src_ip", "role": ["Attacker"], "type": "IP Address"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search uses the Kubernetes logs from a nginx ingress controller to detect remote file inclusion attacks. +action.notable.param.rule_title = Kubernetes Nginx Ingress RFI +action.notable.param.security_domain = network +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_container_controller` | rex field=_raw "^(?\S+)\s+-\s+-\s+\[(?[^\]]*)\]\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\"(?[^\"]*)\"\s\"(?[^\"]*)\"\s(?\S*)\s(?\S*)\s\[(?[^\]]*)\]\s\[(?[^\]]*)\]\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)\s(?\S*)" | rex field=request "^(?\S+)?\s(?\S+)\s" | rex field=url "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" | search dest_ip=* | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, dest_ip status, url, http_method, host, http_user_agent, proxy | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_rfi_filter` + +[ESCU - Kubernetes Scanner Image Pulling - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search uses the Kubernetes logs from Splunk Connect from Kubernetes to detect Kubernetes Security Scanner. +action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.data_models = [] +action.escu.eli5 = This search uses the Kubernetes logs from Splunk Connect from Kubernetes to detect Kubernetes Security Scanner. +action.escu.how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes. +action.escu.known_false_positives = unknown +action.escu.creation_date = 2021-08-24 +action.escu.modification_date = 2021-08-24 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Scanner Image Pulling - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Dev Sec Ops"] +action.risk = 1 +action.risk.param._risk_message = Kubernetes Scanner image pulled on host $host$ +action.risk.param._risk = [{"threat_object_field": "host", "threat_object_type": "entity"}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Scanner Image Pulling - Rule +action.correlationsearch.annotations = {"analytic_story": ["Dev Sec Ops"], "cis20": ["CIS 13"], "confidence": 70, "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "observable": [{"name": "host", "type": "Entity"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search uses the Kubernetes logs from Splunk Connect from Kubernetes to detect Kubernetes Security Scanner. +action.notable.param.rule_title = Kubernetes Scanner Image Pulling +action.notable.param.security_domain = network +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kube_objects_events` object.message IN ("Pulling image *kube-hunter*", "Pulling image *kube-bench*", "Pulling image *kube-recon*", "Pulling image *kube-recon*") | rename object.* AS * | rename involvedObject.* AS * | rename source.host AS host | stats min(_time) as firstTime max(_time) as lastTime count by host, name, namespace, kind, reason, message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_scanner_image_pulling_filter` + [ESCU - Large Volume of DNS ANY Queries - Rule] action.escu = 0 action.escu.enabled = 1 @@ -12954,7 +13308,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter` +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter` [ESCU - MacOS - Re-opened Applications - Rule] action.escu = 0 @@ -12981,7 +13335,7 @@ action.correlationsearch.label = ESCU - MacOS - Re-opened Applications - Rule action.correlationsearch.annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. action.notable.param.rule_title = MacOS - Re-opened Applications action.notable.param.security_domain = threat @@ -12995,7 +13349,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter` [ESCU - Mailsniper Invoke functions - Rule] action.escu = 0 @@ -13039,17 +13393,17 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message IN ("*Invoke-GlobalO365MailSearch*", "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mailsniper_invoke_functions_filter` +search = `powershell` EventCode=4104 Message IN ("*Invoke-GlobalO365MailSearch*", "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mailsniper_invoke_functions_filter` [ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. +description = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. +action.escu.eli5 = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. action.escu.creation_date = 2020-11-20 action.escu.modification_date = 2020-11-20 action.escu.confidence = high @@ -13070,8 +13424,8 @@ action.correlationsearch.label = ESCU - Malicious PowerShell Process - Connect T action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Command And Control"], "impact": 90, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process", "role": ["Attacker"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. action.notable.param.rule_title = Malicious PowerShell Process - Connect To Internet With Hidden Window action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -13084,7 +13438,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `malicious_powershell_process___connect_to_internet_with_hidden_window_filter` [ESCU - Malicious PowerShell Process - Encoded Command - Rule] action.escu = 0 @@ -13093,8 +13447,8 @@ description = This search looks for PowerShell processes that have encoded the s action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.known_false_positives = System administrators may use this option, but it's not common. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.known_false_positives = System administrators may use this option, but it's not common. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -13115,7 +13469,7 @@ action.correlationsearch.label = ESCU - Malicious PowerShell Process - Encoded C action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "NOBELIUM Group"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. action.notable.param.rule_title = Malicious PowerShell Process - Encoded Command action.notable.param.security_domain = endpoint @@ -13138,7 +13492,7 @@ description = This search looks for PowerShell processes started with parameters action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -13160,7 +13514,7 @@ action.correlationsearch.label = ESCU - Malicious PowerShell Process - Execution action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. action.notable.param.rule_title = Malicious PowerShell Process - Execution Policy Bypass action.notable.param.security_domain = endpoint @@ -13174,7 +13528,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` +search = | tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter` [ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] action.escu = 0 @@ -13183,8 +13537,8 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. action.escu.creation_date = 2021-01-19 action.escu.modification_date = 2021-01-19 action.escu.confidence = high @@ -13201,7 +13555,7 @@ action.correlationsearch.label = ESCU - Deprecated - Malicious PowerShell Proces action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command action.notable.param.rule_title = Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments action.notable.param.security_domain = endpoint @@ -13224,7 +13578,7 @@ description = This search looks for PowerShell processes launched with arguments action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = These characters might be legitimately on the command-line, but it is not common. action.escu.creation_date = 2021-01-19 action.escu.modification_date = 2021-01-19 @@ -13246,7 +13600,7 @@ action.correlationsearch.label = ESCU - Malicious PowerShell Process With Obfusc action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. action.notable.param.rule_title = Malicious PowerShell Process With Obfuscation Techniques action.notable.param.security_domain = endpoint @@ -13260,7 +13614,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10 [ESCU - Malicious Powershell Executed As A Service - Rule] action.escu = 0 @@ -13291,7 +13645,7 @@ action.correlationsearch.label = ESCU - Malicious Powershell Executed As A Servi action.correlationsearch.annotations = {"analytic_story": ["Malicious Powershell"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 90, "kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1569.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This detection is to identify the abuse the Windows SC.exe to execute malicious commands or payloads via PowerShell. action.notable.param.rule_title = Malicious Powershell Executed As A Service action.notable.param.security_domain = endpoint @@ -13305,7 +13659,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_system` EventCode=7045 | eval l_Service_File_Name=lower(Service_File_Name) | regex l_Service_File_Name="powershell[.\s]|powershell_ise[.\s]|pwsh[.\s]|psexec[.\s]" | regex l_Service_File_Name="-nop[rofile\s]+|-w[indowstyle]*\s+hid[den]*|-noe[xit\s]+|-enc[odedcommand\s]+" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type Service_Account user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_executed_as_a_service_filter` +search = `wineventlog_system` EventCode=7045 | eval l_Service_File_Name=lower(Service_File_Name) | regex l_Service_File_Name="powershell[.\s]|powershell_ise[.\s]|pwsh[.\s]|psexec[.\s]" | regex l_Service_File_Name="-nop[rofile\s]+|-w[indowstyle]*\s+hid[den]*|-noe[xit\s]+|-enc[odedcommand\s]+" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type Service_Account user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_executed_as_a_service_filter` [ESCU - Modification Of Wallpaper - Rule] action.escu = 0 @@ -13349,7 +13703,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode =13 (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Image != "*\\explorer.exe") OR (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Details = "*\\temp\\*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Image TargetObject Details Computer process_guid process_id user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modification_of_wallpaper_filter` +search = `sysmon` EventCode =13 (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Image != "*\\explorer.exe") OR (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Details = "*\\temp\\*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Image TargetObject Details Computer process_guid process_id user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modification_of_wallpaper_filter` [ESCU - Modify ACL permission To Files Or Folder - Rule] action.escu = 0 @@ -13380,7 +13734,7 @@ action.correlationsearch.label = ESCU - Modify ACL permission To Files Or Folder action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies suspicious modification of ACL permission to a files or folder to make it available to everyone. This technique may be used by the adversary to evade ACLs or protected files access. This changes is commonly configured by the file or directory owner with appropriate permission. This behavior is a good indicator if this command seen on a machine utilized by an account with no permission to do so. action.notable.param.rule_title = Modify ACL permission To Files Or Folder action.notable.param.security_domain = endpoint @@ -13394,7 +13748,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "xcacls.exe" AND (Processes.process = "*/G everyone:*" OR Processes.process = "*/G SYSTEM:*") by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modify_acl_permission_to_files_or_folder_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "xcacls.exe" AND (Processes.process = "*/G everyone:*" OR Processes.process = "*/G SYSTEM:*") by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modify_acl_permission_to_files_or_folder_filter` [ESCU - Monitor DNS For Brand Abuse - Rule] action.escu = 0 @@ -13403,7 +13757,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]} action.escu.data_models = ["Network_Resolution"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. -action.escu.how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +action.escu.how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. action.escu.known_false_positives = None at this time action.escu.creation_date = 2017-09-23 action.escu.modification_date = 2017-09-23 @@ -13434,7 +13788,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter` +search = | tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)`| `brand_abuse_dns` | `monitor_dns_for_brand_abuse_filter` [ESCU - Monitor Email For Brand Abuse - Rule] action.escu = 0 @@ -13443,7 +13797,7 @@ description = This search looks for emails claiming to be sent from a domain sim action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} action.escu.data_models = ["Email"] action.escu.eli5 = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. -action.escu.how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +action.escu.how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. action.escu.known_false_positives = None at this time action.escu.creation_date = 2018-01-05 action.escu.modification_date = 2018-01-05 @@ -13474,7 +13828,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter` +search = | tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter` [ESCU - Monitor Registry Keys for Print Monitors - Rule] action.escu = 0 @@ -13505,7 +13859,7 @@ action.correlationsearch.label = ESCU - Monitor Registry Keys for Print Monitors action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "cis20": ["CIS 8", "CIS 5"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Persistence", "Stage:Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.010"], "nist": ["PR.PT", "DE.CM", "PR.AC"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. action.notable.param.rule_title = Monitor Registry Keys for Print Monitors action.notable.param.security_domain = endpoint @@ -13519,7 +13873,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action | `drop_dm_object_name(Registry)` | `monitor_registry_keys_for_print_monitors_filter` [ESCU - Monitor Web Traffic For Brand Abuse - Rule] action.escu = 0 @@ -13528,7 +13882,7 @@ description = This search looks for Web requests to faux domains similar to the action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} action.escu.data_models = ["Web"] action.escu.eli5 = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. -action.escu.how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +action.escu.how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. action.escu.known_false_positives = None at this time action.escu.creation_date = 2017-09-23 action.escu.modification_date = 2017-09-23 @@ -13546,7 +13900,7 @@ action.correlationsearch.label = ESCU - Monitor Web Traffic For Brand Abuse - Ru action.correlationsearch.annotations = {"analytic_story": ["Brand Monitoring"], "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['src'] +action.notable.param.nes_fields = ['src'] action.notable.param.rule_description = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. action.notable.param.rule_title = Monitor Web Traffic For Brand Abuse action.notable.param.security_domain = network @@ -13560,7 +13914,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter` +search = | tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter` [ESCU - Mshta spawning Rundll32 OR Regsvr32 Process - Rule] action.escu = 0 @@ -13591,7 +13945,7 @@ action.correlationsearch.label = ESCU - Mshta spawning Rundll32 OR Regsvr32 Proc action.correlationsearch.annotations = {"analytic_story": ["Trickbot", "IcedID"], "confidence": 80, "context": ["source:endpoint", {"stage": "executions"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a suspicious mshta.exe process that spawn rundll32 or regsvr32 child process. This technique was seen in several malware nowadays like trickbot to load its initial .dll stage loader to execute and download the the actual trickbot payload. action.notable.param.rule_title = Mshta spawning Rundll32 OR Regsvr32 Process action.notable.param.security_domain = endpoint @@ -13605,7 +13959,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "mshta.exe" (Processes.process_name=rundll32.exe OR Processes.process_name=regsvr32.exe) by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `mshta_spawning_rundll32_or_regsvr32_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "mshta.exe" (Processes.process_name=rundll32.exe OR Processes.process_name=regsvr32.exe) by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `mshta_spawning_rundll32_or_regsvr32_process_filter` [ESCU - Msmpeng Application DLL Side Loading - Rule] action.escu = 0 @@ -13632,7 +13986,7 @@ action.correlationsearch.label = ESCU - Msmpeng Application DLL Side Loading - R action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1574.002"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search is to detect a suspicious creation of msmpeng.exe or mpsvc.dll in non default windows defender folder. This technique was seen couple days ago with revil ransomware in Kaseya Supply chain. The approach is to drop an old version of msmpeng.exe to load the actual payload name as mspvc.dll which will load the revil ransomware to the compromise machine action.notable.param.rule_title = Msmpeng Application DLL Side Loading action.notable.param.security_domain = endpoint @@ -13646,7 +14000,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = |tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll") AND Filesystem.file_path != "*\\Program Files\\windows defender\\*" by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_filter` +search = |tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll") AND Filesystem.file_path != "*\\Program Files\\windows defender\\*" by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_filter` [ESCU - Multiple Archive Files Http Post Traffic - Rule] action.escu = 0 @@ -13690,7 +14044,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_http` http_method=POST |eval archive_hdr1=substr(form_data,1,2) | eval archive_hdr2 = substr(form_data,1,4) |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 |where count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `multiple_archive_files_http_post_traffic_filter` +search = `stream_http` http_method=POST |eval archive_hdr1=substr(form_data,1,2) | eval archive_hdr2 = substr(form_data,1,4) |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 |where count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `multiple_archive_files_http_post_traffic_filter` [ESCU - Multiple Disabled Users Failing To Authenticate From Host Using Kerberos - Rule] action.escu = 0 @@ -13743,7 +14097,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4768 Account_Name!="*$" Result_Code=0x12 | bucket span=2m _time | stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_disabled_users_failing_to_authenticate_from_host_using_kerberos_filter` +search = `wineventlog_security` EventCode=4768 Account_Name!="*$" Result_Code=0x12 | bucket span=2m _time | stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_disabled_users_failing_to_authenticate_from_host_using_kerberos_filter` [ESCU - Multiple Invalid Users Failing To Authenticate From Host Using Kerberos - Rule] action.escu = 0 @@ -13796,7 +14150,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4768 Result_Code=0x6 Account_Name!="*$" | bucket span=2m _time | stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_invalid_users_failing_to_authenticate_from_host_using_kerberos_filter` +search = `wineventlog_security` EventCode=4768 Result_Code=0x6 Account_Name!="*$" | bucket span=2m _time | stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_invalid_users_failing_to_authenticate_from_host_using_kerberos_filter` [ESCU - Multiple Invalid Users Failing To Authenticate From Host Using NTLM - Rule] action.escu = 0 @@ -13811,8 +14165,8 @@ action.escu.eli5 = The following analytic identifies one source endpoint failing The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\ This detection will only trigger on domain controllers, not on member servers or workstations.\ The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts. -action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled. -action.escu.known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled. +action.escu.known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. action.escu.creation_date = 2021-04-15 action.escu.modification_date = 2021-04-15 action.escu.confidence = high @@ -13849,7 +14203,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC0000064 action=failure | bucket span=2m _time | stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_invalid_users_failing_to_authenticate_from_host_using_ntlm_filter` +search = `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC0000064 action=failure | bucket span=2m _time | stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_invalid_users_failing_to_authenticate_from_host_using_ntlm_filter` [ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule] action.escu = 0 @@ -13889,7 +14243,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` +search = `okta` outcome.reason=INVALID_CREDENTIALS | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search distinct_users > 5| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` [ESCU - Multiple Users Attempting To Authenticate Using Explicit Credentials - Rule] action.escu = 0 @@ -13942,7 +14296,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4648 | bucket span=2m _time | eval Source_Account = mvindex(Account_Name, 0) | eval Destination_Account = mvindex(Account_Name, 1) | search Source_Account != "*$" Source_Account !="-" Destination_Account !="*$" | stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_account by _time, ComputerName, Source_Account | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_attempting_to_authenticate_using_explicit_credentials_filter` +search = `wineventlog_security` EventCode=4648 | bucket span=2m _time | eval Source_Account = mvindex(Account_Name, 0) | eval Destination_Account = mvindex(Account_Name, 1) | search Source_Account != "*$" Source_Account !="-" Destination_Account !="*$" | stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_account by _time, ComputerName, Source_Account | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_attempting_to_authenticate_using_explicit_credentials_filter` [ESCU - Multiple Users Failing To Authenticate From Host Using Kerberos - Rule] action.escu = 0 @@ -13995,7 +14349,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4771 Failure_Code=0x18 Account_Name!="*$" | bucket span=2m _time | stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_host_using_kerberos_filter` +search = `wineventlog_security` EventCode=4771 Failure_Code=0x18 Account_Name!="*$" | bucket span=2m _time | stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_host_using_kerberos_filter` [ESCU - Multiple Users Failing To Authenticate From Host Using NTLM - Rule] action.escu = 0 @@ -14011,7 +14365,7 @@ The detection calculates the standard deviation for each host and leverages the This detection will only trigger on domain controllers, not on member servers or workstations.\ The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled. -action.escu.known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. +action.escu.known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. action.escu.creation_date = 2021-04-13 action.escu.modification_date = 2021-04-13 action.escu.confidence = high @@ -14048,7 +14402,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC000006A action=failure | bucket span=2m _time | stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_host_using_ntlm_filter` +search = `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC000006A action=failure | bucket span=2m _time | stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_host_using_ntlm_filter` [ESCU - Multiple Users Failing To Authenticate From Process - Rule] action.escu = 0 @@ -14101,7 +14455,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4625 Logon_Type=2 Caller_Process_Name!="-" | bucket span=2m _time | eval Source_Account = mvindex(Account_Name, 0) | eval Destination_Account = mvindex(Account_Name, 1) | stats dc(Destination_Account) AS unique_accounts values(Account_Name) as tried_accounts by _time, Caller_Process_Name, Source_Account, ComputerName | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Caller_Process_Name, Source_Account, ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_process_filter` +search = `wineventlog_security` EventCode=4625 Logon_Type=2 Caller_Process_Name!="-" | bucket span=2m _time | eval Source_Account = mvindex(Account_Name, 0) | eval Destination_Account = mvindex(Account_Name, 1) | stats dc(Destination_Account) AS unique_accounts values(Account_Name) as tried_accounts by _time, Caller_Process_Name, Source_Account, ComputerName | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Caller_Process_Name, Source_Account, ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_failing_to_authenticate_from_process_filter` [ESCU - Multiple Users Remotely Failing To Authenticate From Host - Rule] action.escu = 0 @@ -14154,7 +14508,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4625 Logon_Type=3 Source_Network_Address!="-" | bucket span=2m _time | eval Destination_Account = mvindex(Account_Name, 1) | stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_accounts by _time, Source_Network_Address, ComputerName | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Network_Address, ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_remotely_failing_to_authenticate_from_host_filter` +search = `wineventlog_security` EventCode=4625 Logon_Type=3 Source_Network_Address!="-" | bucket span=2m _time | eval Destination_Account = mvindex(Account_Name, 1) | stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_accounts by _time, Source_Network_Address, ComputerName | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Network_Address, ComputerName | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `multiple_users_remotely_failing_to_authenticate_from_host_filter` [ESCU - NET Profiler UAC bypass - Rule] action.escu = 0 @@ -14185,7 +14539,7 @@ action.correlationsearch.label = ESCU - NET Profiler UAC bypass - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to detect modification of registry to bypass UAC windows feature. This technique is to add a payload dll path on .NET COR file path that will be loaded by mmc.exe as soon it was executed. This detection rely on monitoring the registry key and values in the detection area. It may happened that windows update some dll related to mmc.exe and add dll path in this registry. In this case filtering is needed. action.notable.param.rule_title = NET Profiler UAC bypass action.notable.param.security_domain = endpoint @@ -14199,7 +14553,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" Registry.registry_value_name = "*.dll" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" Registry.registry_value_name = "*.dll" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter` [ESCU - NLTest Domain Trust Discovery - Rule] action.escu = 0 @@ -14230,7 +14584,7 @@ action.correlationsearch.label = ESCU - NLTest Domain Trust Discovery - Rule action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware", "Domain Trust Discovery", "IcedID"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next. action.notable.param.rule_title = NLTest Domain Trust Discovery action.notable.param.security_domain = endpoint @@ -14271,7 +14625,7 @@ action.correlationsearch.label = ESCU - New container uploaded to AWS ECR - Rule action.correlationsearch.annotations = {"analytic_story": ["Container Implantation Monitoring and Investigation"], "mitre_attack": ["T1525"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'src'] +action.notable.param.nes_fields = ['user', 'src'] action.notable.param.rule_description = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. action.notable.param.rule_title = New container uploaded to AWS ECR action.notable.param.security_domain = threat @@ -14285,7 +14639,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` +search = | tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type | `drop_dm_object_name("Compute")` | `new_container_uploaded_to_aws_ecr_filter` [ESCU - Nishang PowershellTCPOneLine - Rule] action.escu = 0 @@ -14316,7 +14670,7 @@ action.correlationsearch.label = ESCU - Nishang PowershellTCPOneLine - Rule action.correlationsearch.annotations = {"analytic_story": ["HAFNIUM Group"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Command and Control"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This query detects the Nishang Invoke-PowerShellTCPOneLine utility that spawns a call back to a remote command and control server. This is a powershell oneliner. In addition, this will capture on the command-line additional utilities used by Nishang. Triage the endpoint and identify any parallel processes that look suspicious. Review the reputation of the remote IP or domain contacted by the powershell process. action.notable.param.rule_title = Nishang PowershellTCPOneLine action.notable.param.security_domain = endpoint @@ -14339,7 +14693,7 @@ description = This search looks for Windows endpoints that have not generated an action.escu.mappings = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} action.escu.data_models = ["Updates"] action.escu.eli5 = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. -action.escu.how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. +action.escu.how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. action.escu.known_false_positives = None identified action.escu.creation_date = 2017-09-15 action.escu.modification_date = 2017-09-15 @@ -14357,7 +14711,7 @@ action.correlationsearch.label = ESCU - No Windows Updates in a time frame - Rul action.correlationsearch.annotations = {"analytic_story": ["Monitor for Updates"], "cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. action.notable.param.rule_title = No Windows Updates in a time frame action.notable.param.security_domain = endpoint @@ -14371,20 +14725,20 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter` +search = | tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0) | `security_content_ctime(lastTime)` | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter` [ESCU - Ntdsutil Export NTDS - Rule] action.escu = 0 action.escu.enabled = 1 description = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ -ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ -This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. +ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ +This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ -ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ -This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ +This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. action.escu.creation_date = 2021-01-28 action.escu.modification_date = 2021-01-28 @@ -14406,10 +14760,10 @@ action.correlationsearch.label = ESCU - Ntdsutil Export NTDS - Rule action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping", "HAFNIUM Group"], "cis20": ["CIS 8", "CIS 16"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 100, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ -ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ -This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. +ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ +This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. action.notable.param.rule_title = Ntdsutil Export NTDS action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -14461,7 +14815,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter` +search = `o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter` [ESCU - O365 Added Service Principal - Rule] action.escu = 0 @@ -14500,7 +14854,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_added_service_principal_filter` +search = `o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." | stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_added_service_principal_filter` [ESCU - O365 Bypass MFA via Trusted IP - Rule] action.escu = 0 @@ -14539,7 +14893,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?<ip_addresses_new_added>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?<ip_addresses_old>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature Workload vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter` +search = `o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature Workload vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter` [ESCU - O365 Disable MFA - Rule] action.escu = 0 @@ -14578,7 +14932,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_disable_mfa_filter` +search = `o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_disable_mfa_filter` [ESCU - O365 Excessive Authentication Failures Alert - Rule] action.escu = 0 @@ -14617,7 +14971,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed | stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter` +search = `o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed | stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter` [ESCU - O365 Excessive SSO logon errors - Rule] action.escu = 0 @@ -14656,7 +15010,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId | where count > 5 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter` +search = `o365_management_activity` Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired | stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId | where count > 5 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter` [ESCU - O365 New Federated Domain Added - Rule] action.escu = 0 @@ -14695,7 +15049,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter` +search = `o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" | stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter` [ESCU - O365 PST export alert - Rule] action.escu = 0 @@ -14734,7 +15088,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter` +search = `o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter` [ESCU - O365 Suspicious Admin Email Forwarding - Rule] action.escu = 0 @@ -14773,7 +15127,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter` +search = `o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_admin_email_forwarding_filter` [ESCU - O365 Suspicious Rights Delegation - Rule] action.escu = 0 @@ -14851,7 +15205,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter` +search = `o365_management_activity` Operation=Set-Mailbox | spath input=Parameters | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress | where count_src_user > 1 |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_user_email_forwarding_filter` [ESCU - Office Application Spawn Regsvr32 process - Rule] action.escu = 0 @@ -14896,7 +15250,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name = "outlook.exe") Processes.process_name=regsvr32.exe by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_application_spawn_regsvr32_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name = "outlook.exe") Processes.process_name=regsvr32.exe by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_application_spawn_regsvr32_process_filter` [ESCU - Office Application Spawn rundll32 process - Rule] action.escu = 0 @@ -14927,7 +15281,7 @@ action.correlationsearch.label = ESCU - Office Application Spawn rundll32 proces action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "Trickbot", "IcedID"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like trickbot that used MS office as its weapon or attack vector to initially infect the machines. action.notable.param.rule_title = Office Application Spawn rundll32 process action.notable.param.security_domain = endpoint @@ -14941,15 +15295,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe") Processes.process_name=rundll32.exe by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `office_application_spawn_rundll32_process_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe") Processes.process_name=rundll32.exe by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `office_application_spawn_rundll32_process_filter` [ESCU - Office Document Creating Schedule Task - Rule] action.escu = 0 action.escu.enabled = 1 -description = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +description = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} action.escu.data_models = [] -action.escu.eli5 = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +action.escu.eli5 = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-04-14 @@ -14972,7 +15326,7 @@ action.correlationsearch.label = ESCU - Office Document Creating Schedule Task - action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +action.notable.param.rule_description = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.notable.param.rule_title = Office Document Creating Schedule Task action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -14985,7 +15339,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded = "*\\taskschd.dll" | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_creating_schedule_task_filter` +search = `sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded = "*\\taskschd.dll" | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_creating_schedule_task_filter` [ESCU - Office Document Executing Macro Code - Rule] action.escu = 0 @@ -15029,7 +15383,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL") | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_executing_macro_code_filter` +search = `sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL") | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_executing_macro_code_filter` [ESCU - Office Document Spawned Child Process To Download - Rule] action.escu = 0 @@ -15073,7 +15427,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=1 parent_process_name IN ("powerpnt.exe", "winword.exe", "excel.exe", "visio.exe") process_name = "*.exe" cmdline IN ("*http:*","*https:*") NOT(OriginalFileName IN("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe")) | stats min(_time) as firstTime max(_time) as lastTime count by parent_process_name process_name parent_process cmdline process_id OriginalFileName ProcessGuid Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_spawned_child_process_to_download_filter` +search = `sysmon` EventCode=1 parent_process_name IN ("powerpnt.exe", "winword.exe", "excel.exe", "visio.exe") process_name = "*.exe" cmdline IN ("*http:*","*https:*") NOT(OriginalFileName IN("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe")) | stats min(_time) as firstTime max(_time) as lastTime count by parent_process_name process_name parent_process cmdline process_id OriginalFileName ProcessGuid Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_spawned_child_process_to_download_filter` [ESCU - Office Product Spawn CMD Process - Rule] action.escu = 0 @@ -15104,7 +15458,7 @@ action.correlationsearch.label = ESCU - Office Product Spawn CMD Process - Rule action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "confidence": 80, "context": ["source:endpoint", {"stage": "executions"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = this search is to detect a suspicious office product process that spawn cmd child process. This is commonly seen in a ms office product having macro to execute shell command to download or execute malicious lolbin relative to its malicious code. This is seen in trickbot spear phishing doc where it execute shell cmd to run mshta payload. action.notable.param.rule_title = Office Product Spawn CMD Process action.notable.param.security_domain = endpoint @@ -15118,7 +15472,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name= "excel.exe" OR Processes.parent_process_name = "powerpnt.exe") Processes.process_name=cmd.exe by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_product_spawn_cmd_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name= "excel.exe" OR Processes.parent_process_name = "powerpnt.exe") Processes.process_name=cmd.exe by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_product_spawn_cmd_process_filter` [ESCU - Office Product Spawning BITSAdmin - Rule] action.escu = 0 @@ -15149,7 +15503,7 @@ action.correlationsearch.label = ESCU - Office Product Spawning BITSAdmin - Rule action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `bitsadmin.exe`. In malicious instances, the command-line of `bitsadmin.exe` will contain a URL to a remote destination or similar command-line arguments as transfer, Download, priority, Foreground. In addition, Threat Research has released a detections identifying suspicious use of `bitsadmin.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `bitsadmin.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity. action.notable.param.rule_title = Office Product Spawning BITSAdmin action.notable.param.security_domain = endpoint @@ -15163,7 +15517,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=bitsadmin.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_bitsadmin_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=bitsadmin.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_bitsadmin_filter` [ESCU - Office Product Spawning CertUtil - Rule] action.escu = 0 @@ -15194,7 +15548,7 @@ action.correlationsearch.label = ESCU - Office Product Spawning CertUtil - Rule action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `certutil.exe`. In malicious instances, the command-line of `certutil.exe` will contain a URL to a remote destination. In addition, Threat Research has released a detections identifying suspicious use of `certutil.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `certutil.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity. action.notable.param.rule_title = Office Product Spawning CertUtil action.notable.param.security_domain = endpoint @@ -15208,7 +15562,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=certutil.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_certutil_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=certutil.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_certutil_filter` [ESCU - Office Product Spawning MSHTA - Rule] action.escu = 0 @@ -15239,7 +15593,7 @@ action.correlationsearch.label = ESCU - Office Product Spawning MSHTA - Rule action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments", "IcedID"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity. action.notable.param.rule_title = Office Product Spawning MSHTA action.notable.param.security_domain = endpoint @@ -15253,7 +15607,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=mshta.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_mshta_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=mshta.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_mshta_filter` [ESCU - Office Product Spawning Rundll32 with no DLL - Rule] action.escu = 0 @@ -15284,7 +15638,7 @@ action.correlationsearch.label = ESCU - Office Product Spawning Rundll32 with no action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies the latest behavior utilized by IcedID malware family. This detection identifies any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. In malicious instances, the command-line of `rundll32.exe` will look like `rundll32 ..\oepddl.igk2,DllRegisterServer`. In addition, Threat Research has released a detection identifying the use of `DllRegisterServer` on the command-line of `rundll32.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze the `DLL` that was dropped to disk. The Office Product will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity. action.notable.param.rule_title = Office Product Spawning Rundll32 with no DLL action.notable.param.security_domain = endpoint @@ -15298,7 +15652,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=rundll32.exe (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_rundll32_with_no_dll_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=rundll32.exe (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_rundll32_with_no_dll_filter` [ESCU - Office Product Spawning Wmic - Rule] action.escu = 0 @@ -15329,7 +15683,7 @@ action.correlationsearch.label = ESCU - Office Product Spawning Wmic - Rule action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 90, "context": ["source:endpoint", {"stage": "recon"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process_name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies the latest behavior utilized by Ursnif malware family. This detection identifies any Windows Office Product spawning `wmic.exe`. In malicious instances, the command-line of `wmic.exe` will contain `wmic process call create`. In addition, Threat Research has released a detection identifying the use of `wmic process call create` on the command-line of `wmic.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `wmic.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity. action.notable.param.rule_title = Office Product Spawning Wmic action.notable.param.security_domain = endpoint @@ -15343,7 +15697,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=wmic.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_wmic_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=wmic.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_wmic_filter` [ESCU - Okta Account Lockout Events - Rule] action.escu = 0 @@ -15383,7 +15737,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` +search = `okta` displayMessage="Max sign in attempts exceeded" | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | table _time, user, country, state, city, src_ip | `okta_account_lockout_events_filter` [ESCU - Okta Failed SSO Attempts - Rule] action.escu = 0 @@ -15423,7 +15777,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` +search = `okta` displayMessage="User attempted unauthorized access to app" | stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_failed_sso_attempts_filter` [ESCU - Okta User Logins From Multiple Cities - Rule] action.escu = 0 @@ -15450,7 +15804,7 @@ action.correlationsearch.label = ESCU - Okta User Logins From Multiple Cities - action.correlationsearch.annotations = {"analytic_story": ["Suspicious Okta Activity"], "cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search detects logins from the same user from different cities in a 24 hour period. action.notable.param.rule_title = Okta User Logins From Multiple Cities action.notable.param.security_domain = access @@ -15464,7 +15818,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1 +search = `okta` displayMessage="User login to Okta" client.geographicalContext.city!=null | stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `okta_user_logins_from_multiple_cities_filter` | search locations > 1 [ESCU - Open Redirect in Splunk Web - Rule] action.escu = 0 @@ -15504,7 +15858,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` +search = index=_internal sourcetype=splunk_web_access return_to="/%09/*" | `open_redirect_in_splunk_web_filter` [ESCU - Osquery pack - ColdRoot detection - Rule] action.escu = 0 @@ -15575,7 +15929,7 @@ action.correlationsearch.label = ESCU - Overwriting Accessibility Binaries - Rul action.correlationsearch.annotations = {"analytic_story": ["Windows Privilege Escalation"], "cis20": ["CIS 8"], "confidence": 90, "context": ["source:endpoint", {"stage": "privilege escalation"}], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.008"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_path", "role": ["Attacker"], "type": "file_path"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. action.notable.param.rule_title = Overwriting Accessibility Binaries action.notable.param.security_domain = endpoint @@ -15620,7 +15974,7 @@ action.correlationsearch.label = ESCU - Permission Modification using Takeown Ap action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1222"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a modification of file or directory permission using takeown.exe windows app. This technique was seen in some ransomware that take the ownership of a folder or files to encrypt or delete it. action.notable.param.rule_title = Permission Modification using Takeown App action.notable.param.security_domain = endpoint @@ -15634,7 +15988,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "takeown.exe" Processes.process = "*/f*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `permission_modification_using_takeown_app_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "takeown.exe" Processes.process = "*/f*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `permission_modification_using_takeown_app_filter` [ESCU - Plain HTTP POST Exfiltrated Data - Rule] action.escu = 0 @@ -15678,7 +16032,51 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `plain_http_post_exfiltrated_data_filter` +search = `stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `plain_http_post_exfiltrated_data_filter` + +[ESCU - PowerShell 4104 Hunting - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = The following Hunting analytic assists with identifying suspicious PowerShell execution using Script Block Logging, or EventCode 4104. This analytic is not meant to be ran hourly, but occasionally to identify malicious or suspicious PowerShell. This analytic is a combination of work completed by Alex Teixeira and Splunk Threat Research Team. +action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"]} +action.escu.data_models = [] +action.escu.eli5 = The following Hunting analytic assists with identifying suspicious PowerShell execution using Script Block Logging, or EventCode 4104. This analytic is not meant to be ran hourly, but occasionally to identify malicious or suspicious PowerShell. This analytic is a combination of work completed by Alex Teixeira and Splunk Threat Research Team. +action.escu.how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +action.escu.known_false_positives = Limited false positives. May filter as needed. +action.escu.creation_date = 2021-08-18 +action.escu.modification_date = 2021-08-18 +action.escu.confidence = high +action.escu.full_search_name = ESCU - PowerShell 4104 Hunting - Rule +action.escu.search_type = detection +action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"] +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Malicious PowerShell"] +action.risk = 1 +action.risk.param._risk_message = An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ executing suspicious commands. +action.risk.param._risk = [{"threat_object_field": "parent_process_name", "threat_object_type": "parent process"}, {"threat_object_field": "process_name", "threat_object_type": "process"}] +action.risk.param.verbose = 0 +cron_schedule = 0 * * * * +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - PowerShell 4104 Hunting - Rule +action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "parent_process_name", "role": ["Parent Process"], "type": "Parent Process"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}]} +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = The following Hunting analytic assists with identifying suspicious PowerShell execution using Script Block Logging, or EventCode 4104. This analytic is not meant to be ran hourly, but occasionally to identify malicious or suspicious PowerShell. This analytic is a combination of work completed by Alex Teixeira and Splunk Threat Research Team. +action.notable.param.rule_title = PowerShell 4104 Hunting +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +disabled = true +enableSched = 1 +allow_skew = 100% +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `powershell` EventCode=4104 | eval DoIt = if(match(Message,"(?i)(\$doit)"), "4", 0) | eval enccom=if(match(Message,"[A-Za-z0-9+\/]{44,}([A-Za-z0-9+\/]{4}|[A-Za-z0-9+\/]{3}=|[A-Za-z0-9+\/]{2}==)") OR match(Message, "(?i)[-]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]"),4,0) | eval suspcmdlet=if(match(Message, "(?i)Add-Exfiltration|Add-Persistence|Add-RegBackdoor|Add-ScrnSaveBackdoor|Check-VM|Do-Exfiltration|Enabled-DuplicateToken|Exploit-Jboss|Find-Fruit|Find-GPOLocation|Find-TrustedDocuments|Get-ApplicationHost|Get-ChromeDump|Get-ClipboardContents|Get-FoxDump|Get-GPPPassword|Get-IndexedItem|Get-Keystrokes|LSASecret|Get-PassHash|Get-RegAlwaysInstallElevated|Get-RegAutoLogon|Get-RickAstley|Get-Screenshot|Get-SecurityPackages|Get-ServiceFilePermission|Get-ServicePermission|Get-ServiceUnquoted|Get-SiteListPassword|Get-System|Get-TimedScreenshot|Get-UnattendedInstallFile|Get-Unconstrained|Get-VaultCredential|Get-VulnAutoRun|Get-VulnSchTask|Gupt-Backdoor|HTTP-Login|Install-SSP|Install-ServiceBinary|Invoke-ACLScanner|Invoke-ADSBackdoor|Invoke-ARPScan|Invoke-AllChecks|Invoke-BackdoorLNK|Invoke-BypassUAC|Invoke-CredentialInjection|Invoke-DCSync|Invoke-DllInjection|Invoke-DowngradeAccount|Invoke-EgressCheck|Invoke-Inveigh|Invoke-InveighRelay|Invoke-Mimikittenz|Invoke-NetRipper|Invoke-NinjaCopy|Invoke-PSInject|Invoke-Paranoia|Invoke-PortScan|Invoke-PoshRat|Invoke-PostExfil|Invoke-PowerDump|Invoke-PowerShellTCP|Invoke-PsExec|Invoke-PsUaCme|Invoke-ReflectivePEInjection|Invoke-ReverseDNSLookup|Invoke-RunAs|Invoke-SMBScanner|Invoke-SSHCommand|Invoke-Service|Invoke-Shellcode|Invoke-Tater|Invoke-ThunderStruck|Invoke-Token|Invoke-UserHunter|Invoke-VoiceTroll|Invoke-WScriptBypassUAC|Invoke-WinEnum|MailRaider|New-HoneyHash|Out-Minidump|Port-Scan|PowerBreach|PowerUp|PowerView|Remove-Update|Set-MacAttribute|Set-Wallpaper|Show-TargetScreen|Start-CaptureServer|VolumeShadowCopyTools|NEEEEWWW|(Computer|User)Property|CachedRDPConnection|get-net\S+|invoke-\S+hunter|Install-Service|get-\S+(credent|password)|remoteps|Kerberos.*(policy|ticket)|netfirewall|Uninstall-Windows|Verb\s+Runas|AmsiBypass|nishang|Invoke-Interceptor|EXEonRemote|NetworkRelay|PowerShelludp|PowerShellIcmp|CreateShortcut|copy-vss|invoke-dll|invoke-mass|out-shortcut|Invoke-ShellCommand"),1,0) | eval base64 = if(match(lower(Message),"frombase64"), "4", 0) | eval empire=if(match(lower(Message),"system.net.webclient") AND match(lower(Message), "frombase64string") ,5,0) | eval mimikatz=if(match(lower(Message),"mimikatz") OR match(lower(Message), "-dumpcr") OR match(lower(Message), "SEKURLSA::Pth") OR match(lower(Message), "kerberos::ptt") OR match(lower(Message), "kerberos::golden") ,5,0) | eval iex = if(match(lower(Message),"iex"), "2", 0) | eval webclient=if(match(lower(Message),"http") OR match(lower(Message),"web(client|request)") OR match(lower(Message),"socket") OR match(lower(Message),"download(file|string)") OR match(lower(Message),"bitstransfer") OR match(lower(Message),"internetexplorer.application") OR match(lower(Message),"xmlhttp"),5,0) | eval get = if(match(lower(Message),"get-"), "1", 0) | eval rundll32 = if(match(lower(Message),"rundll32"), "4", 0) | eval suspkeywrd=if(match(Message, "(?i)(bitstransfer|mimik|metasp|AssemblyBuilderAccess|Reflection\.Assembly|shellcode|injection|cnvert|shell\.application|start-process|Rc4ByteStream|System\.Security\.Cryptography|lsass\.exe|localadmin|LastLoggedOn|hijack|BackupPrivilege|ngrok|comsvcs|backdoor|brute.?force|Port.?Scan|Exfiltration|exploit|DisableRealtimeMonitoring|beacon)"),1,0) | eval syswow64 = if(match(lower(Message),"syswow64"), "3", 0) | eval httplocal = if(match(lower(Message),"http://127.0.0.1"), "4", 0) | eval reflection = if(match(lower(Message),"reflection"), "1", 0) | eval invokewmi=if(match(lower(Message), "(?i)(wmiobject|WMIMethod|RemoteWMI|PowerShellWmi|wmicommand)"),5,0) | eval downgrade=if(match(Message, "(?i)([-]ve*r*s*i*o*n*\s+2)") OR match(lower(Message),"powershell -version"),3,0) | eval compressed=if(match(Message, "(?i)GZipStream|::Decompress|IO.Compression|write-zip|(expand|compress)-Archive"),5,0) | eval invokecmd = if(match(lower(Message),"invoke-command"), "4", 0) | addtotals fieldname=Score DoIt, enccom, suspcmdlet, suspkeywrd, compressed, downgrade, mimikatz, iex, empire, rundll32, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get | stats values(Score) by DoIt, enccom, compressed, downgrade, iex, mimikatz, rundll32, empire, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get, suspcmdlet, suspkeywrd | `powershell_4104_hunting_filter` [ESCU - PowerShell Domain Enumeration - Rule] action.escu = 0 @@ -15778,15 +16176,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message IN ("*[system.reflection.assembly]::load(*","*[reflection.assembly]*") | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_loading_dotnet_into_memory_via_system_reflection_assembly_filter` +search = `powershell` EventCode=4104 Message IN ("*[system.reflection.assembly]::load(*","*[reflection.assembly]*") | stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_loading_dotnet_into_memory_via_system_reflection_assembly_filter` [ESCU - PowerShell Start-BitsTransfer - Rule] action.escu = 0 action.escu.enabled = 1 -description = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? +description = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? +action.escu.eli5 = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Limited false positives. It is possible administrators will utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent process or command-line arguments. action.escu.creation_date = 2021-03-29 @@ -15809,8 +16207,8 @@ action.correlationsearch.label = ESCU - PowerShell Start-BitsTransfer - Rule action.correlationsearch.annotations = {"analytic_story": ["BITS Jobs"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? action.notable.param.rule_title = PowerShell Start-BitsTransfer action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -15867,7 +16265,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message = "*Threading.Mutex*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_creating_thread_mutex_filter` +search = `powershell` EventCode=4104 Message = "*Threading.Mutex*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_creating_thread_mutex_filter` [ESCU - Powershell Disable Security Monitoring - Rule] action.escu = 0 @@ -15894,7 +16292,7 @@ action.correlationsearch.label = ESCU - Powershell Disable Security Monitoring - action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections. action.notable.param.rule_title = Powershell Disable Security Monitoring action.notable.param.security_domain = endpoint @@ -15908,15 +16306,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("powershell.exe", "pwsh.exe", "sqlps.exe", "sqltoolsps.exe") Processes.process="*set-mppreference*" AND Processes.process IN ("*disablerealtimemonitoring*","*disableioavprotection*","*disableintrusionpreventionsystem*","*disablescriptscanning*","*disableblockatfirstseen*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_disable_security_monitoring_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("powershell.exe", "pwsh.exe", "sqlps.exe", "sqltoolsps.exe") Processes.process="*set-mppreference*" AND Processes.process IN ("*disablerealtimemonitoring*","*disableioavprotection*","*disableintrusionpreventionsystem*","*disablescriptscanning*","*disableblockatfirstseen*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_disable_security_monitoring_filter` [ESCU - Powershell Enable SMB1Protocol Feature - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. +description = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027.005"]} action.escu.data_models = [] -action.escu.eli5 = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. +action.escu.eli5 = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event. action.escu.known_false_positives = network operator may enable or disable this windows feature. action.escu.creation_date = 2021-06-22 @@ -15935,7 +16333,7 @@ action.correlationsearch.label = ESCU - Powershell Enable SMB1Protocol Feature - action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027.005"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. +action.notable.param.rule_description = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. action.notable.param.rule_title = Powershell Enable SMB1Protocol Feature action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -15948,7 +16346,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message = "*Enable-WindowsOptionalFeature*" Message = "*SMB1Protocol*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_enable_smb1protocol_feature_filter` +search = `powershell` EventCode=4104 Message = "*Enable-WindowsOptionalFeature*" Message = "*SMB1Protocol*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_enable_smb1protocol_feature_filter` [ESCU - Powershell Execute COM Object - Rule] action.escu = 0 @@ -16142,7 +16540,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message = "*IO.Compression.*" OR Message = "*IO.StreamReader*" OR Message = "*]::Decompress*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_processing_stream_of_data_filter` +search = `powershell` EventCode=4104 Message = "*IO.Compression.*" OR Message = "*IO.StreamReader*" OR Message = "*]::Decompress*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_processing_stream_of_data_filter` [ESCU - Powershell Remote Thread To Known Windows Process - Rule] action.escu = 0 @@ -16186,7 +16584,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode = 8 process_name IN ("powershell_ise.exe", "powershell.exe") TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") | stats min(_time) as firstTime max(_time) as lastTime count by SourceImage process_name SourceProcessId SourceProcessGuid TargetImage TargetProcessId NewThreadId StartAddress Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_thread_to_known_windows_process_filter` +search = `sysmon` EventCode = 8 process_name IN ("powershell_ise.exe", "powershell.exe") TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") | stats min(_time) as firstTime max(_time) as lastTime count by SourceImage process_name SourceProcessId SourceProcessGuid TargetImage TargetProcessId NewThreadId StartAddress Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_thread_to_known_windows_process_filter` [ESCU - Powershell Using memory As Backing Store - Rule] action.escu = 0 @@ -16230,7 +16628,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message = "*New-Object IO.MemoryStream*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_using_memory_as_backing_store_filter` +search = `powershell` EventCode=4104 Message = "*New-Object IO.MemoryStream*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_using_memory_as_backing_store_filter` [ESCU - Prevent Automatic Repair Mode using Bcdedit - Rule] action.escu = 0 @@ -16261,7 +16659,7 @@ action.correlationsearch.label = ESCU - Prevent Automatic Repair Mode using Bcde action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 80, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a suspicious bcdedit.exe execution to ignore all failures. This technique was used by ransomware to prevent the compromise machine automatically boot in repair mode. action.notable.param.rule_title = Prevent Automatic Repair Mode using Bcdedit action.notable.param.security_domain = endpoint @@ -16275,18 +16673,18 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "bcdedit.exe" Processes.process = "*bootstatuspolicy*" Processes.process = "*ignoreallfailures*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `prevent_automatic_repair_mode_using_bcdedit_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "bcdedit.exe" Processes.process = "*bootstatuspolicy*" Processes.process = "*ignoreallfailures*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `prevent_automatic_repair_mode_using_bcdedit_filter` [ESCU - Print Spooler Adding A Printer Driver - Rule] action.escu = 0 action.escu.enabled = 1 description = The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ +Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012"]} action.escu.data_models = [] action.escu.eli5 = The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ +Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began. action.escu.how_to_implement = You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems. action.escu.known_false_positives = Unknown. This may require filtering. @@ -16311,7 +16709,7 @@ action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2 schedule_window = auto action.notable = 1 action.notable.param.rule_description = The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ +Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began. action.notable.param.rule_title = Print Spooler Adding A Printer Driver action.notable.param.security_domain = endpoint @@ -16325,19 +16723,19 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `printservice` EventCode=316 category = "Adding a printer driver" Message = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_adding_a_printer_driver_filter` +search = `printservice` EventCode=316 category = "Adding a printer driver" Message = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_adding_a_printer_driver_filter` [ESCU - Print Spooler Failed to Load a Plug-in - Rule] action.escu = 0 action.escu.enabled = 1 description = The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ +Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ The analytic is based on file path and failure to load the plug-in. \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012"]} action.escu.data_models = [] action.escu.eli5 = The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ +Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ The analytic is based on file path and failure to load the plug-in. \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events. action.escu.how_to_implement = You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems. @@ -16363,7 +16761,7 @@ action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2 schedule_window = auto action.notable = 1 action.notable.param.rule_description = The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ +Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ The analytic is based on file path and failure to load the plug-in. \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events. action.notable.param.rule_title = Print Spooler Failed to Load a Plug-in @@ -16378,7 +16776,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909")) OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\")) | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_failed_to_load_a_plug_in_filter` +search = `printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909")) OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\")) | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_failed_to_load_a_plug_in_filter` [ESCU - Process Creating LNK file in Suspicious Location - Rule] action.escu = 0 @@ -16409,7 +16807,7 @@ action.correlationsearch.label = ESCU - Process Creating LNK file in Suspicious action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "cis20": ["CIS 7", "CIS 8"], "confidence": 90, "context": ["source:endpoint", {"stage": "Initial Access"}], "impact": 70, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.002"], "nist": ["ID.AM", "PR.DS"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools. action.notable.param.rule_title = Process Creating LNK file in Suspicious Location action.notable.param.security_domain = network @@ -16423,7 +16821,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*" by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_id as lnk_pid | join lnk_pid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process | `drop_dm_object_name(Processes)` | rename parent_process_id as lnk_pid | fields _time lnk_pid process_id dest process_name process_path process] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter` [ESCU - Process Deleting Its Process File Path - Rule] action.escu = 0 @@ -16432,7 +16830,7 @@ description = This detection is to identify a suspicious process that tries to d action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"]} action.escu.data_models = [] action.escu.eli5 = This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-03-17 action.escu.modification_date = 2021-03-17 @@ -16454,7 +16852,7 @@ action.correlationsearch.label = ESCU - Process Deleting Its Process File Path - action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "confidence": 100, "context": ["source:endpoint", {"stage": "Credential Access"}], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. action.notable.param.rule_title = Process Deleting Its Process File Path action.notable.param.security_domain = endpoint @@ -16468,7 +16866,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" |eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_deleting_its_process_file_path_filter` +search = `sysmon` EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" |eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_deleting_its_process_file_path_filter` [ESCU - Process Execution via WMI - Rule] action.escu = 0 @@ -16477,7 +16875,7 @@ description = The following analytic identifies `WmiPrvSE.exe` spawning a proces action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = The following analytic identifies `WmiPrvSE.exe` spawning a process. This typically occurs when a process is instantiated from a local or remote process using `wmic.exe`. During triage, review parallel processes for suspicious behavior or commands executed. Review the process and command-line spawning from `wmiprvse.exe`. Contain and remediate the endpoint as necessary. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 @@ -16499,7 +16897,7 @@ action.correlationsearch.label = ESCU - Process Execution via WMI - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 3", "CIS 5"], "confidence": 70, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies `WmiPrvSE.exe` spawning a process. This typically occurs when a process is instantiated from a local or remote process using `wmic.exe`. During triage, review parallel processes for suspicious behavior or commands executed. Review the process and command-line spawning from `wmiprvse.exe`. Contain and remediate the endpoint as necessary. action.notable.param.rule_title = Process Execution via WMI action.notable.param.security_domain = endpoint @@ -16544,7 +16942,7 @@ action.correlationsearch.label = ESCU - Process Kill Base On File Path - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 80, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of `wmic.exe` using `delete` to remove a executable path. This is typically ran via a batch file during beginning stages of an adversary setting up for mining on an endpoint. action.notable.param.rule_title = Process Kill Base On File Path action.notable.param.security_domain = endpoint @@ -16558,7 +16956,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wmic.exe" AND Processes.process="*process*" AND Processes.process="*executablepath*" AND Processes.process="*delete*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_kill_base_on_file_path_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wmic.exe" AND Processes.process="*process*" AND Processes.process="*executablepath*" AND Processes.process="*delete*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_kill_base_on_file_path_filter` [ESCU - Processes Tapping Keyboard Events - Rule] action.escu = 0 @@ -16608,7 +17006,7 @@ action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Ob action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. action.escu.how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -action.escu.known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. +action.escu.known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. action.escu.creation_date = 2020-11-23 action.escu.modification_date = 2020-11-23 action.escu.confidence = high @@ -16625,7 +17023,7 @@ action.correlationsearch.label = ESCU - Deprecated - Processes created by netsh action.correlationsearch.annotations = {"analytic_story": ["Netsh Abuse"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. action.notable.param.rule_title = Processes created by netsh action.notable.param.security_domain = endpoint @@ -16670,7 +17068,7 @@ action.correlationsearch.label = ESCU - Processes launching netsh - Rule action.correlationsearch.annotations = {"analytic_story": ["Netsh Abuse", "Disabling Security Tools", "DHS Report TA18-074A"], "cis20": ["CIS 8"], "confidence": 70, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. action.notable.param.rule_title = Processes launching netsh action.notable.param.security_domain = endpoint @@ -16684,16 +17082,16 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter` [ESCU - Prohibited Network Traffic Allowed - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. +description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "PR.AC"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. -action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. +action.escu.eli5 = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. +action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. action.escu.known_false_positives = None identified action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -16711,7 +17109,7 @@ action.correlationsearch.label = ESCU - Prohibited Network Traffic Allowed - Rul action.correlationsearch.annotations = {"analytic_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "PR.AC"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. +action.notable.param.rule_description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. action.notable.param.rule_title = Prohibited Network Traffic Allowed action.notable.param.security_domain = network action.notable.param.severity = high @@ -16724,7 +17122,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter` [ESCU - Prohibited Software On Endpoint - Rule] action.escu = 0 @@ -16733,7 +17131,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as prohibited. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search <code>Add Prohibited Processes to Enterprise Security</code>. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. action.escu.known_false_positives = None identified action.escu.creation_date = 2019-10-11 action.escu.modification_date = 2019-10-11 @@ -16751,7 +17149,7 @@ action.correlationsearch.label = ESCU - Deprecated - Prohibited Software On Endp action.correlationsearch.annotations = {"analytic_story": ["Monitor for Unauthorized Software", "Emotet Malware DHS Report TA18-201A ", "SamSam Ransomware"], "cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as prohibited. action.notable.param.rule_title = Prohibited Software On Endpoint action.notable.param.security_domain = endpoint @@ -16805,19 +17203,19 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter` [ESCU - Protocols passing authentication in cleartext - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. +description = The following analytic identifies cleartext protocols at risk of leaking sensitive information. Currently, this consists of legacy protocols such as telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21) sessions. While some of these protocols may be used over SSL, they typically are found on different assigned ports in those instances. action.escu.mappings = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} action.escu.data_models = ["Network_Traffic"] -action.escu.eli5 = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. -action.escu.how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. +action.escu.eli5 = The following analytic identifies cleartext protocols at risk of leaking sensitive information. Currently, this consists of legacy protocols such as telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21) sessions. While some of these protocols may be used over SSL, they typically are found on different assigned ports in those instances. +action.escu.how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. For more accurate result it's better to limit destination to organization private and public IP range, like All_Traffic.dest IN(192.168.0.0/16,172.16.0.0/12,10.0.0.0/8, x.x.x.x/22) action.escu.known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare. -action.escu.creation_date = 2020-11-04 -action.escu.modification_date = 2020-11-04 +action.escu.creation_date = 2021-08-19 +action.escu.modification_date = 2021-08-19 action.escu.confidence = high action.escu.full_search_name = ESCU - Protocols passing authentication in cleartext - Rule action.escu.search_type = detection @@ -16832,8 +17230,8 @@ action.correlationsearch.label = ESCU - Protocols passing authentication in clea action.correlationsearch.annotations = {"analytic_story": ["Use of Cleartext Protocols"], "cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest', 'src'] -action.notable.param.rule_description = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. +action.notable.param.nes_fields = ['user', 'dest', 'src'] +action.notable.param.rule_description = The following analytic identifies cleartext protocols at risk of leaking sensitive information. Currently, this consists of legacy protocols such as telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21) sessions. While some of these protocols may be used over SSL, they typically are found on different assigned ports in those instances. action.notable.param.rule_title = Protocols passing authentication in cleartext action.notable.param.security_domain = network action.notable.param.severity = high @@ -16846,7 +17244,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action!=blocked AND All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter` [ESCU - Ransomware Notes bulk creation - Rule] action.escu = 0 @@ -16890,7 +17288,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") |bin _time span=10s | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name | where unique_readme_path_count >= 15 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ransomware_notes_bulk_creation_filter` +search = `sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") |bin _time span=10s | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name | where unique_readme_path_count >= 15 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ransomware_notes_bulk_creation_filter` [ESCU - Recon AVProduct Through Pwh or WMI - Rule] action.escu = 0 @@ -16934,7 +17332,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 (Message = "*SELECT*" OR Message = "*WMIC*") AND (Message = "*AntiVirusProduct*" OR Message = "*AntiSpywareProduct*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_avproduct_through_pwh_or_wmi_filter` +search = `powershell` EventCode=4104 (Message = "*SELECT*" OR Message = "*WMIC*") AND (Message = "*AntiVirusProduct*" OR Message = "*AntiSpywareProduct*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_avproduct_through_pwh_or_wmi_filter` [ESCU - Recon Using WMI Class - Rule] action.escu = 0 @@ -16978,7 +17376,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 (Message= "*SELECT*" OR Message= "*Get-WmiObject*") AND (Message= "*Win32_Bios*" OR Message= "*Win32_OperatingSystem*" OR Message= "*Win32_Processor*" OR Message= "*Win32_ComputerSystem*" OR Message= "*Win32_ComputerSystemProduct*" OR Message= "*Win32_ShadowCopy*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_using_wmi_class_filter` +search = `powershell` EventCode=4104 (Message= "*SELECT*" OR Message= "*Get-WmiObject*") AND (Message= "*Win32_Bios*" OR Message= "*Win32_OperatingSystem*" OR Message= "*Win32_Processor*" OR Message= "*Win32_ComputerSystem*" OR Message= "*Win32_ComputerSystemProduct*" OR Message= "*Win32_ShadowCopy*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_using_wmi_class_filter` [ESCU - Recursive Delete of Directory In Batch CMD - Rule] action.escu = 0 @@ -17005,7 +17403,7 @@ action.correlationsearch.label = ESCU - Recursive Delete of Directory In Batch C action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1070.004"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a suspicious commandline designed to delete files or directory recursive using batch command. This technique was seen in ransomware (reddot) where it it tries to delete the files in recycle bin to impaire user from recovering deleted files. action.notable.param.rule_title = Recursive Delete of Directory In Batch CMD action.notable.param.security_domain = endpoint @@ -17019,7 +17417,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*/c* Processes.process=* rd * Processes.process="*/s*" Processes.process="*/q*" by Processes.user Processes.process_name Processes.parent_process_name Processes.parent_process Processes.process Processes.process_id Processes.dest |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recursive_delete_of_directory_in_batch_cmd_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*/c* Processes.process=* rd * Processes.process="*/s*" Processes.process="*/q*" by Processes.user Processes.process_name Processes.parent_process_name Processes.parent_process Processes.process Processes.process_id Processes.dest |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recursive_delete_of_directory_in_batch_cmd_filter` [ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule] action.escu = 0 @@ -17050,7 +17448,7 @@ action.correlationsearch.label = ESCU - Reg exe Manipulating Windows Services Re action.correlationsearch.annotations = {"analytic_story": ["Windows Service Abuse", "Windows Persistence Techniques"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "confidence": 60, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation", "Defense Evasion"], "impact": 75, "kill_chain_phases": ["Installation"], "mitre_attack": ["T1574.011"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. action.notable.param.rule_title = Reg exe Manipulating Windows Services Registry Keys action.notable.param.security_domain = endpoint @@ -17064,7 +17462,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter` [ESCU - Reg exe used to hide files directories via registry keys - Rule] action.escu = 0 @@ -17073,7 +17471,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for command-line arguments used to hide a file or directory using the reg add command. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None at the moment action.escu.creation_date = 2019-02-27 action.escu.modification_date = 2019-02-27 @@ -17091,7 +17489,7 @@ action.correlationsearch.label = ESCU - Deprecated - Reg exe used to hide files action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for command-line arguments used to hide a file or directory using the reg add command. action.notable.param.rule_title = Reg exe used to hide files directories via registry keys action.notable.param.security_domain = endpoint @@ -17105,7 +17503,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = reg.exe Processes.process="*add*" Processes.process="*Hidden*" Processes.process="*REG_DWORD*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`| regex process = "(/d\s+2)" | `reg_exe_used_to_hide_files_directories_via_registry_keys_filter` [ESCU - Registry Keys Used For Persistence - Rule] action.escu = 0 @@ -17136,7 +17534,7 @@ action.correlationsearch.label = ESCU - Registry Keys Used For Persistence - Rul action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Suspicious MSHTA Activity", "DHS Report TA18-074A", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware", "Windows Persistence Techniques", "Emotet Malware DHS Report TA18-201A ", "IcedID"], "cis20": ["CIS 8"], "confidence": 95, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.001"], "nist": ["PR.PT", "DE.CM", "DE.AE"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The search looks for modifications to registry keys that can be used to launch an application or service at system startup. action.notable.param.rule_title = Registry Keys Used For Persistence action.notable.param.security_domain = endpoint @@ -17150,15 +17548,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_persistence_filter` [ESCU - Registry Keys Used For Privilege Escalation - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. +description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.012"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = [] -action.escu.eli5 = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. +action.escu.eli5 = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. action.escu.known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. action.escu.creation_date = 2020-11-27 @@ -17181,8 +17579,8 @@ action.correlationsearch.label = ESCU - Registry Keys Used For Privilege Escalat action.correlationsearch.annotations = {"analytic_story": ["Windows Privilege Escalation", "Suspicious Windows Registry Activities", "Cloud Federated Credential Abuse"], "cis20": ["CIS 8"], "confidence": 95, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation"], "impact": 80, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.012"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. action.notable.param.rule_title = Registry Keys Used For Privilege Escalation action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -17195,7 +17593,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `registry_keys_used_for_privilege_escalation_filter` [ESCU - Registry Keys for Creating SHIM Databases - Rule] action.escu = 0 @@ -17226,7 +17624,7 @@ action.correlationsearch.label = ESCU - Registry Keys for Creating SHIM Database action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "cis20": ["CIS 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. action.notable.param.rule_title = Registry Keys for Creating SHIM Databases action.notable.param.security_domain = endpoint @@ -17250,7 +17648,7 @@ action.escu.mappings = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phas action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. action.escu.how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. +action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -17267,7 +17665,7 @@ action.correlationsearch.label = ESCU - Remote Desktop Network Bruteforce - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. action.notable.param.rule_title = Remote Desktop Network Bruteforce action.notable.param.security_domain = network @@ -17281,7 +17679,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 | where count>(avg + stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev | `remote_desktop_network_bruteforce_filter` [ESCU - Remote Desktop Network Traffic - Rule] action.escu = 0 @@ -17290,7 +17688,7 @@ description = This search looks for network traffic on TCP/3389, the default por action.escu.mappings = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. -action.escu.how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. +action.escu.how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. action.escu.creation_date = 2020-07-07 action.escu.modification_date = 2020-07-07 @@ -17308,7 +17706,7 @@ action.correlationsearch.label = ESCU - Remote Desktop Network Traffic - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware", "Hidden Cobra Malware", "Lateral Movement"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. action.notable.param.rule_title = Remote Desktop Network Traffic action.notable.param.security_domain = network @@ -17322,16 +17720,16 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter` [ESCU - Remote Desktop Process Running On System - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. +description = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. action.escu.mappings = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. +action.escu.eli5 = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -17349,8 +17747,8 @@ action.correlationsearch.label = ESCU - Remote Desktop Process Running On System action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware", "Lateral Movement"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. action.notable.param.rule_title = Remote Desktop Process Running On System action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -17372,8 +17770,8 @@ description = This search looks for wmic.exe being launched with parameters to s action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. action.escu.creation_date = 2020-11-30 action.escu.modification_date = 2020-11-30 action.escu.confidence = high @@ -17394,7 +17792,7 @@ action.correlationsearch.label = ESCU - Remote Process Instantiation via WMI - R action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Suspicious WMI Use"], "cis20": ["CIS 3", "CIS 5"], "confidence": 70, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. action.notable.param.rule_title = Remote Process Instantiation via WMI action.notable.param.security_domain = endpoint @@ -17408,7 +17806,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter` [ESCU - Remote Registry Key modifications - Rule] action.escu = 0 @@ -17417,8 +17815,8 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search monitors for remote modifications to registry keys. -action.escu.how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. -action.escu.known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. +action.escu.how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. +action.escu.known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. action.escu.creation_date = 2020-03-02 action.escu.modification_date = 2020-03-02 action.escu.confidence = high @@ -17435,7 +17833,7 @@ action.correlationsearch.label = ESCU - Deprecated - Remote Registry Key modific action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search monitors for remote modifications to registry keys. action.notable.param.rule_title = Remote Registry Key modifications action.notable.param.security_domain = endpoint @@ -17449,7 +17847,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `remote_registry_key_modifications_filter` [ESCU - Remote WMI Command Attempt - Rule] action.escu = 0 @@ -17458,7 +17856,7 @@ description = The following analytic identifies usage of `wmic.exe` spawning a l action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = The following analytic identifies usage of `wmic.exe` spawning a local or remote process, identified by the `node` switch. During triage, review parallel processes for additional commands executed. Look for any file modifications before and after `wmic.exe` execution. In addition, identify the remote endpoint and confirm execution or file modifications. Contain and isolate the endpoint as needed. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. Filter as needed. action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 @@ -17480,7 +17878,7 @@ action.correlationsearch.label = ESCU - Remote WMI Command Attempt - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 3", "CIS 5"], "confidence": 60, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies usage of `wmic.exe` spawning a local or remote process, identified by the `node` switch. During triage, review parallel processes for additional commands executed. Look for any file modifications before and after `wmic.exe` execution. In addition, identify the remote endpoint and confirm execution or file modifications. Contain and isolate the endpoint as needed. action.notable.param.rule_title = Remote WMI Command Attempt action.notable.param.security_domain = endpoint @@ -17525,7 +17923,7 @@ action.correlationsearch.label = ESCU - Resize ShadowStorage volume - Rule action.correlationsearch.annotations = {"analytic_story": ["Clop Ransomware"], "confidence": 90, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible action.notable.param.rule_title = Resize ShadowStorage volume action.notable.param.security_domain = endpoint @@ -17539,7 +17937,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name = "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `resize_shadowstorage_volume_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name = "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `resize_shadowstorage_volume_filter` [ESCU - Revil Common Exec Parameter - Rule] action.escu = 0 @@ -17570,7 +17968,7 @@ action.correlationsearch.label = ESCU - Revil Common Exec Parameter - Rule action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies suspicious commandline parameter that are commonly used by REVIL ransomware to encrypts the compromise machine. action.notable.param.rule_title = Revil Common Exec Parameter action.notable.param.security_domain = endpoint @@ -17584,7 +17982,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* -nolan *" OR Processes.process = "* -nolocal *" OR Processes.process = "* -fast *" OR Processes.process = "* -full *" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `revil_common_exec_parameter_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* -nolan *" OR Processes.process = "* -nolocal *" OR Processes.process = "* -fast *" OR Processes.process = "* -full *" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `revil_common_exec_parameter_filter` [ESCU - Revil Registry Entry - Rule] action.escu = 0 @@ -17615,7 +18013,7 @@ action.correlationsearch.label = ESCU - Revil Registry Entry - Rule action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Revil Ransomware"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1112"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies suspicious modification in registry entry to keep some malware data during its infection. This technique seen in several apt implant, malware and ransomware like REVIL where it keep some information like the random generated file extension it uses for all the encrypted files and ransomware notes file name in the compromised host. action.notable.param.rule_title = Revil Registry Entry action.notable.param.security_domain = endpoint @@ -17629,7 +18027,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") AND (Registry.registry_value_name = "\.*" OR Registry.registry_value_name = "Binary Data") by Registry.registry_value_name Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `revil_registry_entry_filter` +search = | tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") AND (Registry.registry_value_name = "\.*" OR Registry.registry_value_name = "Binary Data") by Registry.registry_value_name Registry.dest Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `revil_registry_entry_filter` [ESCU - RunDLL Loading DLL By Ordinal - Rule] action.escu = 0 @@ -17638,7 +18036,7 @@ description = This search looks for executing scripts with rundll32. Adversaries action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process action.escu.creation_date = 2020-11-30 action.escu.modification_date = 2020-11-30 @@ -17660,7 +18058,7 @@ action.correlationsearch.label = ESCU - RunDLL Loading DLL By Ordinal - Rule action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "cis20": ["CIS 8"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Installation"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. action.notable.param.rule_title = RunDLL Loading DLL By Ordinal action.notable.param.security_domain = endpoint @@ -17718,15 +18116,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe" | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_create_remote_thread_to_a_process_filter` +search = `sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe" | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_create_remote_thread_to_a_process_filter` [ESCU - Rundll32 CreateRemoteThread In Browser - Rule] action.escu = 0 action.escu.enabled = 1 -description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. +description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]} action.escu.data_models = [] -action.escu.eli5 = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. +action.escu.eli5 = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-07-26 @@ -17749,7 +18147,7 @@ action.correlationsearch.label = ESCU - Rundll32 CreateRemoteThread In Browser - action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "SourceImage", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. +action.notable.param.rule_description = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. action.notable.param.rule_title = Rundll32 CreateRemoteThread In Browser action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -17762,15 +18160,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_createremotethread_in_browser_filter` +search = `sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_createremotethread_in_browser_filter` [ESCU - Rundll32 DNSQuery - Rule] action.escu = 0 action.escu.enabled = 1 -description = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. +description = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.011"]} action.escu.data_models = [] -action.escu.eli5 = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. +action.escu.eli5 = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-07-26 @@ -17793,7 +18191,7 @@ action.correlationsearch.label = ESCU - Rundll32 DNSQuery - Rule action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.011"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. +action.notable.param.rule_description = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. action.notable.param.rule_title = Rundll32 DNSQuery action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -17806,7 +18204,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=22 process_name="rundll32.exe" | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus ProcessId direction Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_dnsquery_filter` +search = `sysmon` EventCode=22 process_name="rundll32.exe" | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus ProcessId direction Computer | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_dnsquery_filter` [ESCU - Rundll32 Process Creating Exe Dll Files - Rule] action.escu = 0 @@ -17837,7 +18235,7 @@ action.correlationsearch.label = ESCU - Rundll32 Process Creating Exe Dll Files action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.011"], "observable": [{"name": "Computer", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to detect a suspicious rundll32 process that drops executable (.exe or .dll) files. this behavior seen in rundll32 process of IcedID that tries to drop copy of itself in temp folder or download executable drop it either appdata or programdata as part of its execution. action.notable.param.rule_title = Rundll32 Process Creating Exe Dll Files action.notable.param.security_domain = endpoint @@ -17851,7 +18249,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=11 process_name="rundll32.exe" TargetFilename IN ("*.exe", "*.dll",) | stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename ProcessGuid dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_process_creating_exe_dll_files_filter` +search = `sysmon` EventCode=11 process_name="rundll32.exe" TargetFilename IN ("*.exe", "*.dll",) | stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename ProcessGuid dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_process_creating_exe_dll_files_filter` [ESCU - Rundll32 with no Command Line Arguments with Network - Rule] action.escu = 0 @@ -17882,7 +18280,7 @@ action.correlationsearch.label = ESCU - Rundll32 with no Command Line Arguments action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Cobalt Strike", "PrintNightmare CVE-2021-34527"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.011"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "processname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies rundll32.exe with no command line arguments and performing a network connection. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Rundll32 with no Command Line Arguments with Network action.notable.param.security_domain = endpoint @@ -17896,7 +18294,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(rundll32\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port| `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `rundll32_with_no_command_line_arguments_with_network_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(rundll32\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port| `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `rundll32_with_no_command_line_arguments_with_network_filter` [ESCU - Ryuk Test Files Detected - Rule] action.escu = 0 @@ -17940,7 +18338,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter` [ESCU - Ryuk Wake on LAN Command - Rule] action.escu = 0 @@ -17971,7 +18369,7 @@ action.correlationsearch.label = ESCU - Ryuk Wake on LAN Command - Rule action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 70, "kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1059.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public\). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged. action.notable.param.rule_title = Ryuk Wake on LAN Command action.notable.param.security_domain = endpoint @@ -17985,16 +18383,16 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ryuk_wake_on_lan_command_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ryuk_wake_on_lan_command_filter` [ESCU - SAM Database File Access Attempt - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. +description = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"]} action.escu.data_models = [] -action.escu.eli5 = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. -action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +action.escu.eli5 = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. +action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." action.escu.known_false_positives = Natively, `dllhost.exe` will access the files. Every environment will have additional native processes that do as well. Filter by process_name. As an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`. action.escu.creation_date = 2021-07-23 action.escu.modification_date = 2021-07-23 @@ -18016,8 +18414,8 @@ action.correlationsearch.label = ESCU - SAM Database File Access Attempt - Rule action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Credential Access"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Child Process"], "type": "Process"}, {"name": "Object_Name", "role": ["Other"], "type": "File"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. action.notable.param.rule_title = SAM Database File Access Attempt action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -18030,7 +18428,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` (EventCode=4663) process_name!=*\\dllhost.exe Object_Name IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*") | stats values(Accesses) count by process_name Object_Name dest user | `sam_database_file_access_attempt_filter` +search = `wineventlog_security` (EventCode=4663) process_name!=*\\dllhost.exe Object_Name IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*") | stats values(Accesses) count by process_name Object_Name dest user | `sam_database_file_access_attempt_filter` [ESCU - SLUI RunAs Elevated - Rule] action.escu = 0 @@ -18061,7 +18459,7 @@ action.correlationsearch.label = ESCU - SLUI RunAs Elevated - Rule action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Windows Defense Evasion Tactics"], "confidence": 90, "context": ["source:endpoint", {"stage": "Privilege Escalation"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, elevating access using the `-verb runas` function. This particular bypass utilizes a registry key/value. Identified by two sources, the registry keys are `HKCU\Software\Classes\exefile\shell` and `HKCU\Software\Classes\launcher.Systemsettings\Shell\open\command`. To simulate this behavior, multiple POC are available. The analytic identifies the use of `runas` by `slui.exe`. action.notable.param.rule_title = SLUI RunAs Elevated action.notable.param.security_domain = endpoint @@ -18106,7 +18504,7 @@ action.correlationsearch.label = ESCU - SLUI Spawning a Process - Rule action.correlationsearch.annotations = {"analytic_story": ["DarkSide Ransomware", "Windows Defense Evasion Tactics"], "confidence": 90, "context": ["source:endpoint", {"stage": "Privilege Escalation"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, spawning a child process. This behavior is associated with publicly known UAC bypass. `slui.exe` is commonly associated with software updates and is most often spawned by `svchost.exe`. The `slui.exe` process should not have child processes, and any processes spawning from it will be running with elevated privileges. During triage, review the child process and additional parallel processes. Identify any file modifications that may have lead to the bypass. action.notable.param.rule_title = SLUI Spawning a Process action.notable.param.security_domain = endpoint @@ -18147,7 +18545,7 @@ action.correlationsearch.label = ESCU - SMB Traffic Spike - Rule action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['src'] +action.notable.param.nes_fields = ['src'] action.notable.param.rule_description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. action.notable.param.rule_title = SMB Traffic Spike action.notable.param.security_domain = network @@ -18161,7 +18559,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as stdev by src | eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` +search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter` [ESCU - SMB Traffic Spike - MLTK - Rule] action.escu = 0 @@ -18170,8 +18568,8 @@ description = This search uses the Machine Learning Toolkit (MLTK) to identify s action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["DE.CM"]} action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. -action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ +action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` action.escu.known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results @@ -18191,7 +18589,7 @@ action.correlationsearch.label = ESCU - SMB Traffic Spike - MLTK - Rule action.correlationsearch.annotations = {"analytic_story": ["Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. action.notable.param.rule_title = SMB Traffic Spike - MLTK action.notable.param.security_domain = network @@ -18205,7 +18603,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` +search = | tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter` [ESCU - SQL Injection with Long URLs - Rule] action.escu = 0 @@ -18215,7 +18613,7 @@ action.escu.mappings = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phas action.escu.data_models = ["Web"] action.escu.eli5 = This search looks for long URLs that have several SQL commands visible within them. action.escu.how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. -action.escu.known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. +action.escu.known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 action.escu.confidence = high @@ -18232,7 +18630,7 @@ action.correlationsearch.label = ESCU - SQL Injection with Long URLs - Rule action.correlationsearch.annotations = {"analytic_story": ["SQL Injection"], "cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1190"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = This search looks for long URLs that have several SQL commands visible within them. action.notable.param.rule_title = SQL Injection with Long URLs action.notable.param.security_domain = network @@ -18246,15 +18644,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter` +search = | tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter` [ESCU - Samsam Test File Write - Rule] action.escu = 0 action.escu.enabled = 1 -description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. +description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. +action.escu.eli5 = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. action.escu.known_false_positives = No false positives have been identified. action.escu.creation_date = 2018-12-14 @@ -18277,8 +18675,8 @@ action.correlationsearch.label = ESCU - Samsam Test File Write - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware"], "cis20": ["CIS 8"], "confidence": 20, "context": ["source:endpoint", {"stage": "Impact"}], "impact": 60, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. action.notable.param.rule_title = Samsam Test File Write action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -18322,7 +18720,7 @@ action.correlationsearch.label = ESCU - Sc exe Manipulating Windows Services - R action.correlationsearch.annotations = {"analytic_story": ["Windows Service Abuse", "DHS Report TA18-074A", "Orangeworm Attack Group", "Windows Persistence Techniques", "Disabling Security Tools", "NOBELIUM Group"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Persistence"}, "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Installation"], "mitre_attack": ["T1543.003"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. action.notable.param.rule_title = Sc exe Manipulating Windows Services action.notable.param.security_domain = endpoint @@ -18336,15 +18734,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter` [ESCU - Schedule Task with HTTP Command Arguments - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"]} action.escu.data_models = [] -action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-04-19 @@ -18367,7 +18765,7 @@ action.correlationsearch.label = ESCU - Schedule Task with HTTP Command Argument action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Arguments", "role": ["Attacker"], "type": "Arguments"}]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.notable.param.rule_title = Schedule Task with HTTP Command Arguments action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -18380,15 +18778,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4698 | xmlkv Message| search Arguments IN ("*http*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_http_command_arguments_filter` +search = `wineventlog_security` EventCode=4698 | xmlkv Message| search Arguments IN ("*http*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_http_command_arguments_filter` [ESCU - Schedule Task with Rundll32 Command Trigger - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"]} action.escu.data_models = [] -action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +action.escu.eli5 = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment. action.escu.known_false_positives = unknown action.escu.creation_date = 2021-04-19 @@ -18411,7 +18809,7 @@ action.correlationsearch.label = ESCU - Schedule Task with Rundll32 Command Trig action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques", "Trickbot", "IcedID"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Arguments", "role": ["Attacker"], "type": "Arguments"}]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +action.notable.param.rule_description = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' action.notable.param.rule_title = Schedule Task with Rundll32 Command Trigger action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -18424,7 +18822,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*rundll32*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_rundll32_command_trigger_filter` +search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*rundll32*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_rundll32_command_trigger_filter` [ESCU - Scheduled Task Deleted Or Created via CMD - Rule] action.escu = 0 @@ -18433,7 +18831,7 @@ description = This search looks for flags passed to schtasks.exe on the command- action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Tasks should not be manually created via CLI, this is rarely done by admins as well action.escu.creation_date = 2020-12-17 action.escu.modification_date = 2020-12-17 @@ -18455,7 +18853,7 @@ action.correlationsearch.label = ESCU - Scheduled Task Deleted Or Created via CM action.correlationsearch.annotations = {"analytic_story": ["DHS Report TA18-074A", "NOBELIUM Group"], "cis20": ["CIS 3"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. action.notable.param.rule_title = Scheduled Task Deleted Or Created via CMD action.notable.param.security_domain = endpoint @@ -18478,7 +18876,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = No known false positives action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -18496,7 +18894,7 @@ action.correlationsearch.label = ESCU - Deprecated - Scheduled tasks used in Bad action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection action.notable.param.rule_title = Scheduled tasks used in BadRabbit ransomware action.notable.param.security_domain = endpoint @@ -18510,7 +18908,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process= "*create*" OR Processes.process= "*delete*") by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter` [ESCU - Schtasks Run Task On Demand - Rule] action.escu = 0 @@ -18530,7 +18928,7 @@ action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splun action.escu.providing_technologies = [] action.escu.analytic_story = ["XMRig"] action.risk = 1 -action.risk.param._risk_message = A "on demand" execution of schedule task process $process_name$ using commandline $process$ in host $dest$ +action.risk.param._risk_message = A "on demand" execution of schedule task process $process_name$ using commandline $process$ in host $dest$ action.risk.param._risk = [] action.risk.param.verbose = 0 cron_schedule = 0 * * * * @@ -18541,7 +18939,7 @@ action.correlationsearch.label = ESCU - Schtasks Run Task On Demand - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic identifies an on demand run of a Windows Schedule Task through shell or command-line. This technique has been used by adversaries that force to run their created Schedule Task as their persistence mechanism or for lateral movement as part of their malicious attack to the compromised machine. action.notable.param.rule_title = Schtasks Run Task On Demand action.notable.param.security_domain = endpoint @@ -18555,7 +18953,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/run*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_run_task_on_demand_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/run*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_run_task_on_demand_filter` [ESCU - Schtasks scheduling job on remote system - Rule] action.escu = 0 @@ -18564,7 +18962,7 @@ description = This search looks for flags passed to schtasks.exe on the command- action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. action.escu.creation_date = 2020-07-21 action.escu.modification_date = 2020-07-21 @@ -18586,7 +18984,7 @@ action.correlationsearch.label = ESCU - Schtasks scheduling job on remote system action.correlationsearch.annotations = {"analytic_story": ["Lateral Movement", "NOBELIUM Group"], "cis20": ["CIS 3"], "confidence": 90, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"], "observable": [{"name": "Processes.dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Processes.user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. action.notable.param.rule_title = Schtasks scheduling job on remote system action.notable.param.security_domain = endpoint @@ -18600,7 +18998,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter` [ESCU - Schtasks used for forcing a reboot - Rule] action.escu = 0 @@ -18631,7 +19029,7 @@ action.correlationsearch.label = ESCU - Schtasks used for forcing a reboot - Rul action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques", "Ransomware"], "cis20": ["CIS 3"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}, "Persistence", "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. action.notable.param.rule_title = Schtasks used for forcing a reboot action.notable.param.security_domain = endpoint @@ -18645,7 +19043,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter` [ESCU - Script Execution via WMI - Rule] action.escu = 0 @@ -18654,7 +19052,7 @@ description = This search looks for scripts launched via WMI. action.escu.mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for scripts launched via WMI. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. Filter as needed. action.escu.creation_date = 2020-03-16 action.escu.modification_date = 2020-03-16 @@ -18676,7 +19074,7 @@ action.correlationsearch.label = ESCU - Script Execution via WMI - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 3", "CIS 5"], "confidence": 60, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for scripts launched via WMI. action.notable.param.rule_title = Script Execution via WMI action.notable.param.security_domain = endpoint @@ -18721,7 +19119,7 @@ action.correlationsearch.label = ESCU - Sdclt UAC Bypass - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to detect a suspicious sdclt.exe registry modification. This technique is commonly seen when attacker try to bypassed UAC by using sdclt.exe application by modifying some registry that sdclt.exe tries to open or query with payload file path on it to be executed. action.notable.param.rule_title = Sdclt UAC Bypass action.notable.param.security_domain = endpoint @@ -18735,7 +19133,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*" OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_key_name = "(Default)" OR Registry.registry_key_name = "IsolatedCommand") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdclt_uac_bypass_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*" OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_key_name = "(Default)" OR Registry.registry_key_name = "IsolatedCommand") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdclt_uac_bypass_filter` [ESCU - SearchProtocolHost with no Command Line with Network - Rule] action.escu = 0 @@ -18766,7 +19164,7 @@ action.correlationsearch.label = ESCU - SearchProtocolHost with no Command Line action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 100, "context": ["source:endpoint", {"stage": "Defense Evasion"}, "Privilege Escalation"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "processname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies searchprotocolhost.exe with no command line arguments and with a network connection. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = SearchProtocolHost with no Command Line with Network action.notable.param.security_domain = endpoint @@ -18780,7 +19178,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(searchprotocolhost\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `searchprotocolhost_with_no_command_line_with_network_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(searchprotocolhost\.exe.{0,4}$)" | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port | `drop_dm_object_name(Ports)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port | `searchprotocolhost_with_no_command_line_with_network_filter` [ESCU - SecretDumps Offline NTDS Dumping Tool - Rule] action.escu = 0 @@ -18811,7 +19209,7 @@ action.correlationsearch.label = ESCU - SecretDumps Offline NTDS Dumping Tool - action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "confidence": 100, "context": ["source:endpoint", {"stage": "Credential Access"}], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This analytic detects a potential usage of secretsdump.py tool for dumping credentials (ntlm hash) from a copy of ntds.dit and SAM.Security,SYSTEM registrry hive. This technique was seen in some attacker that dump ntlm hashes offline after having a copy of ntds.dit and SAM/SYSTEM/SECURITY registry hive. action.notable.param.rule_title = SecretDumps Offline NTDS Dumping Tool action.notable.param.security_domain = endpoint @@ -18825,7 +19223,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `secretdumps_offline_ntds_dumping_tool_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `secretdumps_offline_ntds_dumping_tool_filter` [ESCU - Services Escalate Exe - Rule] action.escu = 0 @@ -18856,7 +19254,7 @@ action.correlationsearch.label = ESCU - Services Escalate Exe - Rule action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 95, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548"], "observable": [{"name": "Processes.dest", "role": ["Victim"], "type": "Hostname"}, {"name": "Processes.user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of `svc-exe` with Cobalt Strike. The behavior typically follows after an adversary has already gained initial access and is escalating privileges. Using `svc-exe`, a randomly named binary will be downloaded from the remote Teamserver and placed on disk within `C:\Windows\400619a.exe`. Following, the binary will be added to the registry under key `HKLM\System\CurrentControlSet\Services\400619a\` with multiple keys and values added to look like a legitimate service. Upon loading, `services.exe` will spawn the randomly named binary from `\\127.0.0.1\ADMIN$\400619a.exe`. The process lineage is completed with `400619a.exe` spawning rundll32.exe, which is the default `spawnto_` value for Cobalt Strike. The `spawnto_` value is arbitrary and may be any process on disk (typically system32/syswow64 binary). The `spawnto_` process will also contain a network connection. During triage, review parallel procesess and identify any additional file modifications. action.notable.param.rule_title = Services Escalate Exe action.notable.param.security_domain = endpoint @@ -18875,12 +19273,12 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule] action.escu = 0 action.escu.enabled = 1 -description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. +description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. +action.escu.eli5 = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. -action.escu.known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. +action.escu.known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. action.escu.creation_date = 2020-11-06 action.escu.modification_date = 2020-11-06 action.escu.confidence = high @@ -18901,8 +19299,8 @@ action.correlationsearch.label = ESCU - Set Default PowerShell Execution Policy action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell", "Credential Dumping", "HAFNIUM Group"], "cis20": ["CIS 3", "CIS 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Execution"}], "impact": 60, "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "registry_path", "role": ["Others"], "type": "RegistryPath"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] -action.notable.param.rule_description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. action.notable.param.rule_title = Set Default PowerShell Execution Policy To Unrestricted or Bypass action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -18946,7 +19344,7 @@ action.correlationsearch.label = ESCU - Shim Database File Creation - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 8"], "confidence": 80, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "file_path", "role": ["Others"], "type": "file path"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. action.notable.param.rule_title = Shim Database File Creation action.notable.param.security_domain = endpoint @@ -18969,7 +19367,7 @@ description = This search detects the process execution and arguments required t action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified action.escu.creation_date = 2020-11-23 action.escu.modification_date = 2020-11-23 @@ -18991,7 +19389,7 @@ action.correlationsearch.label = ESCU - Shim Database Installation With Suspicio action.correlationsearch.annotations = {"analytic_story": ["Windows Persistence Techniques"], "cis20": ["CIS 8"], "confidence": 90, "context": ["source:endpoint", {"stage": "Privilege Escalation"}, "Persistence"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. action.notable.param.rule_title = Shim Database Installation With Suspicious Parameters action.notable.param.security_domain = endpoint @@ -19036,7 +19434,7 @@ action.correlationsearch.label = ESCU - Short Lived Windows Accounts - Rule action.correlationsearch.annotations = {"analytic_story": ["Account Monitoring and Controls"], "cis20": ["CIS 16"], "confidence": 90, "context": ["source:endpoint", {"stage": "Persistence"}], "impact": 70, "mitre_attack": ["T1136.001"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "user", "role": ["Victim"], "type": "user"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search detects accounts that were created and deleted in a short time period. action.notable.param.rule_title = Short Lived Windows Accounts action.notable.param.security_domain = access @@ -19050,7 +19448,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` +search = | tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter` [ESCU - SilentCleanup UAC Bypass - Rule] action.escu = 0 @@ -19081,7 +19479,7 @@ action.correlationsearch.label = ESCU - SilentCleanup UAC Bypass - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence,", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to detect a suspicious modification of registry that may related to UAC bypassed. This registry will be trigger once the attacker abuse the silentcleanup task schedule to gain high privilege execution that will bypass User control account. action.notable.param.rule_title = SilentCleanup UAC Bypass action.notable.param.security_domain = endpoint @@ -19095,7 +19493,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_name = "*.exe*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `silentcleanup_uac_bypass_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_name = "*.exe*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `silentcleanup_uac_bypass_filter` [ESCU - Single Letter Process On Endpoint - Rule] action.escu = 0 @@ -19104,7 +19502,7 @@ description = This search looks for process names that consist only of a single action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for process names that consist only of a single letter. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. action.escu.creation_date = 2020-12-08 action.escu.modification_date = 2020-12-08 @@ -19139,7 +19537,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter` [ESCU - Spectre and Meltdown Vulnerable Systems - Rule] action.escu = 0 @@ -19166,7 +19564,7 @@ action.correlationsearch.label = ESCU - Deprecated - Spectre and Meltdown Vulner action.correlationsearch.annotations = {"analytic_story": ["Spectre And Meltdown Vulnerabilities"], "cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. action.notable.param.rule_title = Spectre and Meltdown Vulnerable Systems action.notable.param.security_domain = endpoint @@ -19180,7 +19578,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter` +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest | `drop_dm_object_name(Vulnerabilities)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spectre_and_meltdown_vulnerable_systems_filter` [ESCU - Spike in File Writes - Rule] action.escu = 0 @@ -19207,7 +19605,7 @@ action.correlationsearch.label = ESCU - Spike in File Writes - Rule action.correlationsearch.annotations = {"analytic_story": ["SamSam Ransomware", "Ryuk Ransomware", "Ransomware"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The search looks for a sharp increase in the number of files written to a particular host action.notable.param.rule_title = Spike in File Writes action.notable.param.security_domain = endpoint @@ -19221,7 +19619,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time<relative_time(maxtime, "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"), count, null))) as stdev by "dest" | eval upperBound=(avg+stdev*4), isOutlier=if((count > upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter` [ESCU - Splunk Enterprise Information Disclosure - Rule] action.escu = 0 @@ -19248,7 +19646,7 @@ action.correlationsearch.label = ESCU - Deprecated - Splunk Enterprise Informati action.correlationsearch.annotations = {"analytic_story": ["Splunk Enterprise Vulnerability CVE-2018-11409"], "cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. action.notable.param.rule_title = Splunk Enterprise Information Disclosure action.notable.param.security_domain = network @@ -19262,7 +19660,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter` +search = index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `splunk_enterprise_information_disclosure_filter` [ESCU - Spoolsv Spawning Rundll32 - Rule] action.escu = 0 @@ -19293,7 +19691,7 @@ action.correlationsearch.label = ESCU - Spoolsv Spawning Rundll32 - Rule action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Local"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process_id", "role": ["Parent Process", "Attacker"], "type": "Process"}, {"name": "process_id", "role": ["Child Process"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies a suspicious child process, `rundll32.exe`, with no command-line arguments being spawned from `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to spawn a process. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events. action.notable.param.rule_title = Spoolsv Spawning Rundll32 action.notable.param.security_domain = endpoint @@ -19351,7 +19749,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*" ImageLoaded = "*.dll" | stats dc(ImageLoaded) as countImgloaded values(ImageLoaded) as ImgLoaded count min(_time) as firstTime max(_time) as lastTime by Image Computer process_id EventCode | where countImgloaded >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_loaded_modules_filter` +search = `sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*" ImageLoaded = "*.dll" | stats dc(ImageLoaded) as countImgloaded values(ImageLoaded) as ImgLoaded count min(_time) as firstTime max(_time) as lastTime by Image Computer process_id EventCode | where countImgloaded >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_loaded_modules_filter` [ESCU - Spoolsv Suspicious Process Access - Rule] action.escu = 0 @@ -19395,7 +19793,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*" TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff | stats count min(_time) as firstTime max(_time) as lastTime by Computer SourceImage TargetImage GrantedAccess CallTrace EventCode ProcessID| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_process_access_filter` +search = `sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*" TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff | stats count min(_time) as firstTime max(_time) as lastTime by Computer SourceImage TargetImage GrantedAccess CallTrace EventCode ProcessID| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_process_access_filter` [ESCU - Spoolsv Writing a DLL - Rule] action.escu = 0 @@ -19415,7 +19813,7 @@ action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splun action.escu.providing_technologies = [] action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"] action.risk = 1 -action.risk.param._risk_message = $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. +action.risk.param._risk_message = $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. action.risk.param._risk = [{"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_path", "threat_object_type": "file"}] action.risk.param.verbose = 0 cron_schedule = 0 * * * * @@ -19426,7 +19824,7 @@ action.correlationsearch.label = ESCU - Spoolsv Writing a DLL - Rule action.correlationsearch.annotations = {"analytic_story": ["PrintNightmare CVE-2021-34527"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Defense Evasion"], "impact": 80, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "process_id", "role": ["Process"], "type": "Process"}, {"name": "file_path", "role": ["Other"], "type": "File"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events. action.notable.param.rule_title = Spoolsv Writing a DLL action.notable.param.security_domain = endpoint @@ -19440,7 +19838,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=spoolsv.exe by _time Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*" Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `spoolsv_writing_a_dll_filter` +search = | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=spoolsv.exe by _time Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*" Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `spoolsv_writing_a_dll_filter` [ESCU - Spoolsv Writing a DLL - Sysmon - Rule] action.escu = 0 @@ -19460,7 +19858,7 @@ action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splun action.escu.providing_technologies = [] action.escu.analytic_story = ["PrintNightmare CVE-2021-34527"] action.risk = 1 -action.risk.param._risk_message = $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. +action.risk.param._risk_message = $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare. action.risk.param._risk = [{"threat_object_field": "process_id", "threat_object_type": "process"}, {"threat_object_field": "file_path", "threat_object_type": "file"}] action.risk.param.verbose = 0 cron_schedule = 0 * * * * @@ -19484,7 +19882,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*" file_name=*.dll | stats count min(_time) as firstTime max(_time) as lastTime by dest, UserID, process_name, file_path, file_name, TargetFilename, process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_writing_a_dll___sysmon_filter` +search = `sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*" file_name=*.dll | stats count min(_time) as firstTime max(_time) as lastTime by dest, UserID, process_name, file_path, file_name, TargetFilename, process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_writing_a_dll___sysmon_filter` [ESCU - Sqlite Module In Temp Folder - Rule] action.escu = 0 @@ -19528,7 +19926,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") |stats count min(_time) as firstTime max(_time) as lastTime by process_name TargetFilename EventCode ProcessId Image | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sqlite_module_in_temp_folder_filter` +search = `sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") |stats count min(_time) as firstTime max(_time) as lastTime by process_name TargetFilename EventCode ProcessId Image | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sqlite_module_in_temp_folder_filter` [ESCU - Start Up During Safe Mode Boot - Rule] action.escu = 0 @@ -19559,7 +19957,7 @@ action.correlationsearch.label = ESCU - Start Up During Safe Mode Boot - Rule action.correlationsearch.annotations = {"analytic_story": ["Ransomware"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Persistence"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to detect a modification or registry add to the safeboot registry as an autostart mechanism. This technique was seen in some ransomware to automatically execute its code upon a safe mode boot. action.notable.param.rule_title = Start Up During Safe Mode Boot action.notable.param.security_domain = endpoint @@ -19573,7 +19971,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SafeBoot\\Minimal\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `start_up_during_safe_mode_boot_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SafeBoot\\Minimal\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `start_up_during_safe_mode_boot_filter` [ESCU - Sunburst Correlation DLL and Network Event - Rule] action.escu = 0 @@ -19600,7 +19998,7 @@ action.correlationsearch.label = ESCU - Sunburst Correlation DLL and Network Eve action.correlationsearch.annotations = {"analytic_story": ["NOBELIUM Group"], "cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events. action.notable.param.rule_title = Sunburst Correlation DLL and Network Event action.notable.param.security_domain = endpoint @@ -19641,7 +20039,7 @@ action.correlationsearch.label = ESCU - Supernova Webshell - Rule action.correlationsearch.annotations = {"analytic_story": ["NOBELIUM Group"], "cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1505.003"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest', 'src'] +action.notable.param.nes_fields = ['user', 'dest', 'src'] action.notable.param.rule_description = This search aims to detect the Supernova webshell used in the SUNBURST attack. action.notable.param.rule_title = Supernova Webshell action.notable.param.security_domain = network @@ -19682,7 +20080,7 @@ action.correlationsearch.label = ESCU - Deprecated - Suspicious Changes to File action.correlationsearch.annotations = {"analytic_story": ["Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse"], "cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.001"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. action.notable.param.rule_title = Suspicious Changes to File Associations action.notable.param.security_domain = endpoint @@ -19696,7 +20094,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count from datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name!=Explorer.exe AND Processes.process_name!=OpenWith.exe by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join [| tstats `security_content_summariesonly` values(Registry.registry_path) as registry_path count from datamodel=Endpoint.Registry where Registry.registry_path=*\\Explorer\\FileExts* by Registry.process_id Registry.dest | `drop_dm_object_name("Registry")` | table process_id dest registry_path]| `suspicious_changes_to_file_associations_filter` [ESCU - Suspicious Curl Network Connection - Rule] action.escu = 0 @@ -19723,7 +20121,7 @@ action.correlationsearch.label = ESCU - Suspicious Curl Network Connection - Rul action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow", "Ingress Tool Transfer"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1105"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of a curl contacting suspicious remote domains to checkin to command and control servers or download further implants. In the context of Silver Sparrow, curl is identified contacting s3.amazonaws.com. This particular behavior is common with MacOS adware-malicious software. action.notable.param.rule_title = Suspicious Curl Network Connection action.notable.param.security_domain = endpoint @@ -19768,7 +20166,7 @@ action.correlationsearch.label = ESCU - Suspicious DLLHost no Command Line Argum action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies DLLHost.exe with no command line arguments. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Suspicious DLLHost no Command Line Arguments action.notable.param.security_domain = endpoint @@ -19782,7 +20180,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventID=1 (process_name=dllhost.exe OR OriginalFileName=dllhost.exe) | regex CommandLine="(dllhost\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_dllhost_no_command_line_arguments_filter` +search = `sysmon` EventID=1 (process_name=dllhost.exe OR OriginalFileName=dllhost.exe) | regex CommandLine="(dllhost\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_dllhost_no_command_line_arguments_filter` [ESCU - Suspicious Driver Loaded Path - Rule] action.escu = 0 @@ -19826,7 +20224,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=6 ImageLoaded = "*.sys" NOT (ImageLoaded IN("*\\WINDOWS\\inf","*\\WINDOWS\\System32\\drivers\\*", "*\\WINDOWS\\System32\\DriverStore\\FileRepository\\*")) | stats min(_time) as firstTime max(_time) as lastTime count by Computer ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_driver_loaded_path_filter` +search = `sysmon` EventCode=6 ImageLoaded = "*.sys" NOT (ImageLoaded IN("*\\WINDOWS\\inf","*\\WINDOWS\\System32\\drivers\\*", "*\\WINDOWS\\System32\\DriverStore\\FileRepository\\*")) | stats min(_time) as firstTime max(_time) as lastTime count by Computer ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_driver_loaded_path_filter` [ESCU - Suspicious Email - UBA Anomaly - Rule] action.escu = 0 @@ -19835,7 +20233,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566"], "nist": ["PR.IP"]} action.escu.data_models = ["UEBA"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). -action.escu.how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. +action.escu.how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. action.escu.known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender. action.escu.creation_date = 2020-07-22 action.escu.modification_date = 2020-07-22 @@ -19853,7 +20251,7 @@ action.correlationsearch.label = ESCU - Deprecated - Suspicious Email - UBA Anom action.correlationsearch.annotations = {"analytic_story": ["Suspicious Emails"], "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566"], "nist": ["PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). action.notable.param.rule_title = Suspicious Email - UBA Anomaly action.notable.param.security_domain = threat @@ -19867,7 +20265,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_email___uba_anomaly_filter` +search = |tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model | `drop_dm_object_name(All_UEBA_Events)` | `drop_dm_object_name(UEBA_Anomalies)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_email___uba_anomaly_filter` [ESCU - Suspicious Email Attachment Extensions - Rule] action.escu = 0 @@ -19876,9 +20274,9 @@ description = This search looks for emails that have attachments with suspicious action.escu.mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566.001"], "nist": ["DE.AE", "PR.IP"]} action.escu.data_models = ["Email"] action.escu.eli5 = This search looks for emails that have attachments with suspicious file extensions. -action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ +action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ **Splunk Phantom Playbook Integration**\ -If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. +If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. action.escu.known_false_positives = None identified action.escu.creation_date = 2020-07-22 action.escu.modification_date = 2020-07-22 @@ -19909,7 +20307,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachment_extensions_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachment_extensions_filter` [ESCU - Suspicious Event Log Service Behavior - Rule] action.escu = 0 @@ -19940,7 +20338,7 @@ action.correlationsearch.label = ESCU - Suspicious Event Log Service Behavior - action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"], "cis20": ["CIS 3", "CIS 5", "CIS 6"], "confidence": 30, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"], "observable": [{"name": "ComputerName", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic utilizes Windows Event ID 1100 to identify when Windows event log service is shutdown. Note that this is a voluminous analytic that will require tuning or restricted to specific endpoints based on criticality. This event generates every time Windows Event Log service has shut down. It also generates during normal system shutdown. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred. action.notable.param.rule_title = Suspicious Event Log Service Behavior action.notable.param.security_domain = endpoint @@ -19964,7 +20362,7 @@ action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Ob action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for files created with names that have been linked to malicious activity. action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. -action.escu.known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. +action.escu.known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. action.escu.creation_date = 2019-04-25 action.escu.modification_date = 2019-04-25 action.escu.confidence = high @@ -19981,7 +20379,7 @@ action.correlationsearch.label = ESCU - Deprecated - Suspicious File Write - Rul action.correlationsearch.annotations = {"analytic_story": ["Hidden Cobra Malware"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for files created with names that have been linked to malicious activity. action.notable.param.rule_title = Suspicious File Write action.notable.param.security_domain = endpoint @@ -20026,7 +20424,7 @@ action.correlationsearch.label = ESCU - Suspicious GPUpdate no Command Line Argu action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies gpupdate.exe with no command line arguments. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Suspicious GPUpdate no Command Line Arguments action.notable.param.security_domain = endpoint @@ -20040,15 +20438,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventID=1 (process_name=gpupdate.exe OR OriginalFileName=GPUpdate.exe) | regex CommandLine="(gpupdate\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_gpupdate_no_command_line_arguments_filter` +search = `sysmon` EventID=1 (process_name=gpupdate.exe OR OriginalFileName=GPUpdate.exe) | regex CommandLine="(gpupdate\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_gpupdate_no_command_line_arguments_filter` [ESCU - Suspicious IcedID Regsvr32 Cmdline - Rule] action.escu = 0 action.escu.enabled = 1 -description = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. +description = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.010"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. +action.escu.eli5 = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed regsvr32.exe may be used. action.escu.known_false_positives = minimal. but network operator can use this application to load dll. action.escu.creation_date = 2021-07-27 @@ -20071,8 +20469,8 @@ action.correlationsearch.label = ESCU - Suspicious IcedID Regsvr32 Cmdline - Rul action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.010"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. action.notable.param.rule_title = Suspicious IcedID Regsvr32 Cmdline action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -20116,7 +20514,7 @@ action.correlationsearch.label = ESCU - Suspicious IcedID Rundll32 Cmdline - Rul action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.011"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a suspicious rundll32.exe commandline to execute dll file. This technique was seen in IcedID malware to load its payload dll with the following parameter to load encrypted dll payload which is the license.dat. action.notable.param.rule_title = Suspicious IcedID Rundll32 Cmdline action.notable.param.security_domain = endpoint @@ -20157,7 +20555,7 @@ action.correlationsearch.label = ESCU - Suspicious Java Classes - Rule action.correlationsearch.annotations = {"analytic_story": ["Apache Struts Vulnerability"], "cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest', 'src'] +action.notable.param.nes_fields = ['dest', 'src'] action.notable.param.rule_description = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. action.notable.param.rule_title = Suspicious Java Classes action.notable.param.security_domain = threat @@ -20171,7 +20569,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_java_classes_filter` +search = `stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_java_classes_filter` [ESCU - Suspicious MSBuild Rename - Rule] action.escu = 0 @@ -20202,7 +20600,7 @@ action.correlationsearch.label = ESCU - Suspicious MSBuild Rename - Rule action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild", "Cobalt Strike", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild. action.notable.param.rule_title = Suspicious MSBuild Rename action.notable.param.security_domain = endpoint @@ -20247,7 +20645,7 @@ action.correlationsearch.label = ESCU - Suspicious MSBuild Spawn - Rule action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated. action.notable.param.rule_title = Suspicious MSBuild Spawn action.notable.param.security_domain = endpoint @@ -20267,22 +20665,22 @@ search = | tstats `security_content_summariesonly` count values(Processes.proces action.escu = 0 action.escu.enabled = 1 description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. action.escu.mappings = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. action.escu.known_false_positives = Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm. @@ -20302,14 +20700,14 @@ action.correlationsearch.label = ESCU - Suspicious PlistBuddy Usage - Rule action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. action.notable.param.rule_title = Suspicious PlistBuddy Usage action.notable.param.security_domain = endpoint @@ -20329,22 +20727,22 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime action.escu = 0 action.escu.enabled = 1 description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. action.escu.mappings = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001"]} action.escu.data_models = [] action.escu.eli5 = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. action.escu.how_to_implement = OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct. action.escu.known_false_positives = Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm. @@ -20365,12 +20763,12 @@ action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow"], "k schedule_window = auto action.notable = 1 action.notable.param.rule_description = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. action.notable.param.rule_title = Suspicious PlistBuddy Usage via OSquery action.notable.param.security_domain = endpoint @@ -20384,7 +20782,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" | `suspicious_plistbuddy_usage_via_osquery_filter` +search = `osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" | `suspicious_plistbuddy_usage_via_osquery_filter` [ESCU - Suspicious Process File Path - Rule] action.escu = 0 @@ -20415,7 +20813,7 @@ action.correlationsearch.label = ESCU - Suspicious Process File Path - Rule action.correlationsearch.annotations = {"analytic_story": ["XMRig"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1543"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic will detect a suspicious process running in a file path where a process is not commonly seen and is most commonly used by malicious softtware. This behavior has been used by adversaries where they drop and run an exe in a path that is accessible without admin privileges. action.notable.param.rule_title = Suspicious Process File Path action.notable.param.security_domain = endpoint @@ -20429,7 +20827,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*" OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*" OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path.file_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path.file_path = "*\\Windows\\servicing\\*" OR Processes.process_path.file_path = "*\\Users\\Default\\*" OR Processes.process_path.file_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" by Processes.parent_process_name Processes.parent_process Processes.process_path Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_file_path_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*" OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*" OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path.file_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path.file_path = "*\\Windows\\servicing\\*" OR Processes.process_path.file_path = "*\\Users\\Default\\*" OR Processes.process_path.file_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" by Processes.parent_process_name Processes.parent_process Processes.process_path Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_file_path_filter` [ESCU - Suspicious Reg exe Process - Rule] action.escu = 0 @@ -20438,8 +20836,8 @@ description = This search looks for reg.exe being launched from a command prompt action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]} action.escu.data_models = [] action.escu.eli5 = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. action.escu.creation_date = 2020-07-22 action.escu.modification_date = 2020-07-22 action.escu.confidence = high @@ -20460,7 +20858,7 @@ action.correlationsearch.label = ESCU - Suspicious Reg exe Process - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics", "Disabling Security Tools", "DHS Report TA18-074A"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. action.notable.param.rule_title = Suspicious Reg exe Process action.notable.param.security_domain = endpoint @@ -20474,7 +20872,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter` [ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule] action.escu = 0 @@ -20483,7 +20881,7 @@ description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious action.escu.mappings = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. action.escu.known_false_positives = Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. action.escu.creation_date = 2021-01-28 action.escu.modification_date = 2021-01-28 @@ -20505,7 +20903,7 @@ action.correlationsearch.label = ESCU - Suspicious Regsvr32 Register Suspicious action.correlationsearch.annotations = {"analytic_story": ["Suspicious Regsvr32 Activity", "Iceid"], "cis20": ["CIS 8", "CIS 16"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. action.notable.param.rule_title = Suspicious Regsvr32 Register Suspicious Path action.notable.param.security_domain = endpoint @@ -20550,7 +20948,7 @@ action.correlationsearch.label = ESCU - Suspicious Rundll32 PluginInit - Rule action.correlationsearch.annotations = {"analytic_story": ["IcedID"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 60, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.011"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}, {"name": "process_name", "role": ["Attacker"], "type": "process name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is to detect a suspicious rundll32.exe process with plugininit parameter. This technique is commonly seen in IceID malware to execute its initial dll stager to download another payload to the compromised machine. action.notable.param.rule_title = Suspicious Rundll32 PluginInit action.notable.param.security_domain = endpoint @@ -20595,7 +20993,7 @@ action.correlationsearch.label = ESCU - Suspicious Rundll32 Rename - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011", "T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics. action.notable.param.rule_title = Suspicious Rundll32 Rename action.notable.param.security_domain = endpoint @@ -20640,7 +21038,7 @@ action.correlationsearch.label = ESCU - Suspicious Rundll32 StartW - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Cobalt Strike", "Trickbot"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not. action.notable.param.rule_title = Suspicious Rundll32 StartW action.notable.param.security_domain = endpoint @@ -20685,7 +21083,7 @@ action.correlationsearch.label = ESCU - Suspicious Rundll32 dllregisterserver - action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_path.file_path", "role": ["Attacker"], "type": "File Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Suspicious Rundll32 dllregisterserver action.notable.param.security_domain = endpoint @@ -20730,7 +21128,7 @@ action.correlationsearch.label = ESCU - Suspicious Rundll32 no Command Line Argu action.correlationsearch.annotations = {"analytic_story": ["Suspicious Rundll32 Activity", "Cobalt Strike", "PrintNightmare CVE-2021-34527"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Suspicious Rundll32 no Command Line Arguments action.notable.param.security_domain = endpoint @@ -20744,7 +21142,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_command_line_arguments_filter` +search = `sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) | regex CommandLine="(rundll32\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_no_command_line_arguments_filter` [ESCU - Suspicious SQLite3 LSQuarantine Behavior - Rule] action.escu = 0 @@ -20771,7 +21169,7 @@ action.correlationsearch.label = ESCU - Suspicious SQLite3 LSQuarantine Behavior action.correlationsearch.annotations = {"analytic_story": ["Silver Sparrow"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1074"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations. action.notable.param.rule_title = Suspicious SQLite3 LSQuarantine Behavior action.notable.param.security_domain = endpoint @@ -20816,7 +21214,7 @@ action.correlationsearch.label = ESCU - Suspicious Scheduled Task from Public Di action.correlationsearch.annotations = {"analytic_story": ["Ransomware", "Ryuk Ransomware", "Windows Persistence Techniques"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1053.005"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies Scheduled Tasks registering (creating a new task) a binary or script to run from a public directory which includes users\public, \programdata\ and \windows\temp. Upon triage, review the binary or script in the command line for legitimacy, whether an approved binary/script or not. In addition, capture the binary or script in question and analyze for further behaviors. Identify the source and contain the endpoint. action.notable.param.rule_title = Suspicious Scheduled Task from Public Directory action.notable.param.security_domain = endpoint @@ -20861,7 +21259,7 @@ action.correlationsearch.label = ESCU - Suspicious SearchProtocolHost no Command action.correlationsearch.annotations = {"analytic_story": ["Cobalt Strike"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Initial Access", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies searchprotocolhost.exe with no command line arguments. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. action.notable.param.rule_title = Suspicious SearchProtocolHost no Command Line Arguments action.notable.param.security_domain = endpoint @@ -20875,7 +21273,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventID=1 (process_name=searchprotocolhost.exe OR OriginalFileName=SearchProtocolHost.exe) | regex CommandLine="(searchprotocolhost\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_searchprotocolhost_no_command_line_arguments_filter` +search = `sysmon` EventID=1 (process_name=searchprotocolhost.exe OR OriginalFileName=SearchProtocolHost.exe) | regex CommandLine="(searchprotocolhost\.exe.{0,4}$)" | stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_searchprotocolhost_no_command_line_arguments_filter` [ESCU - Suspicious microsoft workflow compiler rename - Rule] action.escu = 0 @@ -20906,7 +21304,7 @@ action.correlationsearch.label = ESCU - Suspicious microsoft workflow compiler r action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution", "Cobalt Strike", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127", "T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect. action.notable.param.rule_title = Suspicious microsoft workflow compiler rename action.notable.param.security_domain = endpoint @@ -20951,7 +21349,7 @@ action.correlationsearch.label = ESCU - Suspicious microsoft workflow compiler u action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications. action.notable.param.rule_title = Suspicious microsoft workflow compiler usage action.notable.param.security_domain = endpoint @@ -20975,7 +21373,7 @@ action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation" action.escu.data_models = ["Endpoint"] action.escu.eli5 = The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. action.escu.how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. -action.escu.known_false_positives = Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. +action.escu.known_false_positives = Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. action.escu.creation_date = 2021-01-12 action.escu.modification_date = 2021-01-12 action.escu.confidence = high @@ -20996,7 +21394,7 @@ action.correlationsearch.label = ESCU - Suspicious msbuild path - Rule action.correlationsearch.annotations = {"analytic_story": ["Trusted Developer Utilities Proxy Execution MSBuild", "Cobalt Strike", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. action.notable.param.rule_title = Suspicious msbuild path action.notable.param.security_domain = endpoint @@ -21015,10 +21413,10 @@ search = | tstats `security_content_summariesonly` count values(Processes.proces [ESCU - Suspicious mshta child process - Rule] action.escu = 0 action.escu.enabled = 1 -description = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. +description = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. +action.escu.eli5 = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. action.escu.creation_date = 2021-01-12 @@ -21041,8 +21439,8 @@ action.correlationsearch.label = ESCU - Suspicious mshta child process - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 50, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}, {"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "parent_process", "role": ["Parent Process"], "type": "Process Name"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. action.notable.param.rule_title = Suspicious mshta child process action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -21086,7 +21484,7 @@ action.correlationsearch.label = ESCU - Suspicious mshta spawn - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious MSHTA Activity"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe. action.notable.param.rule_title = Suspicious mshta spawn action.notable.param.security_domain = endpoint @@ -21109,7 +21507,7 @@ description = The wevtutil.exe application is the windows event log utility. Thi action.escu.mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. action.escu.creation_date = 2020-07-22 action.escu.modification_date = 2020-07-22 @@ -21131,7 +21529,7 @@ action.correlationsearch.label = ESCU - Suspicious wevtutil Usage - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"], "cis20": ["CIS 3", "CIS 5", "CIS 6"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. action.notable.param.rule_title = Suspicious wevtutil Usage action.notable.param.security_domain = endpoint @@ -21145,15 +21543,15 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter` [ESCU - Suspicious writes to System Volume Information - Rule] action.escu = 0 action.escu.enabled = 1 -description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. +description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. action.escu.mappings = {"cis20": ["CIS 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]} action.escu.data_models = [] -action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. +action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. action.escu.creation_date = 2020-07-22 @@ -21172,7 +21570,7 @@ action.correlationsearch.label = ESCU - Deprecated - Suspicious writes to System action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. +action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. action.notable.param.rule_title = Suspicious writes to System Volume Information action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -21216,7 +21614,7 @@ action.correlationsearch.label = ESCU - Suspicious writes to windows Recycle Bin action.correlationsearch.annotations = {"analytic_story": ["Collection and Staging"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 40, "mitre_attack": ["T1036"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_name", "role": ["Attacker"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search detects writes to the recycle bin by a process other than explorer.exe. action.notable.param.rule_title = Suspicious writes to windows Recycle Bin action.notable.param.security_domain = endpoint @@ -21230,7 +21628,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`| search [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest| `drop_dm_object_name("Processes")` | table process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter` [ESCU - System Information Discovery Detection - Rule] action.escu = 0 @@ -21261,7 +21659,7 @@ action.correlationsearch.label = ESCU - System Information Discovery Detection - action.correlationsearch.annotations = {"analytic_story": ["Discovery Techniques"], "cis20": ["CIS 6", "CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Recon", "Stage:Discovery"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "User", "role": ["Attacker"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it. action.notable.param.rule_title = System Information Discovery Detection action.notable.param.security_domain = endpoint @@ -21275,7 +21673,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter` [ESCU - System Processes Run From Unexpected Locations - Rule] action.escu = 0 @@ -21310,7 +21708,7 @@ action.correlationsearch.label = ESCU - System Processes Run From Unexpected Loc action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware", "Masquerading - Rename System Utilities"], "cis20": ["CIS 8"], "confidence": 70, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_name", "role": ["Attacker"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for system processes that typically execute from `C:\Windows\System32\` or `C:\Windows\SysWOW64`. This may indicate a malicious process that is trying to hide as a legitimate process.\ This detection utilizes a lookup that is deduped `system32` and `syswow64` directories from Server 2016 and Windows 10.\ During triage, review the parallel processes - what process moved the native Windows binary? identify any artifacts on disk and review. If a remote destination is contacted, what is the reputation? @@ -21326,7 +21724,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file` | `system_processes_run_from_unexpected_locations_filter` [ESCU - TOR Traffic - Rule] action.escu = 0 @@ -21335,7 +21733,7 @@ description = This search looks for network traffic identified as The Onion Rout action.escu.mappings = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.001"], "nist": ["DE.AE"]} action.escu.data_models = ["Network_Traffic"] action.escu.eli5 = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. -action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. +action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. action.escu.known_false_positives = None at this time action.escu.creation_date = 2020-07-22 action.escu.modification_date = 2020-07-22 @@ -21366,7 +21764,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter` [ESCU - Trickbot Named Pipe - Rule] action.escu = 0 @@ -21410,7 +21808,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" | stats min(_time) as firstTime max(_time) as lastTime count by Computer user_id EventCode PipeName signature Image process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `trickbot_named_pipe_filter` +search = `sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" | stats min(_time) as firstTime max(_time) as lastTime count by Computer user_id EventCode PipeName signature Image process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `trickbot_named_pipe_filter` [ESCU - UAC Bypass MMC Load Unsigned Dll - Rule] action.escu = 0 @@ -21454,7 +21852,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=7 ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false Company != "Microsoft Corporation" | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed ProcessId OriginalFileName Computer EventCode Company | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_mmc_load_unsigned_dll_filter` +search = `sysmon` EventCode=7 ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false Company != "Microsoft Corporation" | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed ProcessId OriginalFileName Computer EventCode Company | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_mmc_load_unsigned_dll_filter` [ESCU - UAC Bypass With Colorui COM Object - Rule] action.escu = 0 @@ -21507,7 +21905,7 @@ description = The fsutil.exe application is a legitimate Windows utility used to action.escu.mappings = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. action.escu.known_false_positives = None identified action.escu.creation_date = 2018-12-03 action.escu.modification_date = 2018-12-03 @@ -21529,7 +21927,7 @@ action.correlationsearch.label = ESCU - USN Journal Deletion - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware"], "cis20": ["CIS 6", "CIS 8", "CIS 10"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. action.notable.param.rule_title = USN Journal Deletion action.notable.param.security_domain = endpoint @@ -21543,7 +21941,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter` [ESCU - Uncommon Processes On Endpoint - Rule] action.escu = 0 @@ -21552,7 +21950,7 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as uncommon. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. action.escu.known_false_positives = None identified action.escu.creation_date = 2020-07-22 action.escu.modification_date = 2020-07-22 @@ -21570,7 +21968,7 @@ action.correlationsearch.label = ESCU - Deprecated - Uncommon Processes On Endpo action.correlationsearch.annotations = {"analytic_story": ["Windows Privilege Escalation", "Unusual Processes"], "cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as uncommon. action.notable.param.rule_title = Uncommon Processes On Endpoint action.notable.param.security_domain = endpoint @@ -21589,10 +21987,10 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime [ESCU - Unified Messaging Service Spawning a Process - Rule] action.escu = 0 action.escu.enabled = 1 -description = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. +description = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]} action.escu.data_models = ["Endpoint"] -action.escu.eli5 = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. +action.escu.eli5 = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. action.escu.known_false_positives = Unknown. Tune out child processes as needed to limit volume of false positives. action.escu.creation_date = 2021-03-02 @@ -21615,8 +22013,8 @@ action.correlationsearch.label = ESCU - Unified Messaging Service Spawning a Pro action.correlationsearch.annotations = {"analytic_story": ["HAFNIUM Group"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] -action.notable.param.rule_description = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. +action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.rule_description = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. action.notable.param.rule_title = Unified Messaging Service Spawning a Process action.notable.param.security_domain = endpoint action.notable.param.severity = high @@ -21629,7 +22027,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="umworkerprocess.exe" OR Processes.parent_process_name="UMService.exe" (Processes.process_name!="wermgr.exe" OR Processes.process_name!="werfault.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `unified_messaging_service_spawning_a_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="umworkerprocess.exe" OR Processes.parent_process_name="UMService.exe" (Processes.process_name!="wermgr.exe" OR Processes.process_name!="werfault.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `unified_messaging_service_spawning_a_process_filter` [ESCU - Uninstall App Using MsiExec - Rule] action.escu = 0 @@ -21683,7 +22081,7 @@ description = Attackers often disable security tools to avoid detection. This se action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. action.escu.known_false_positives = action.escu.creation_date = 2020-07-22 action.escu.modification_date = 2020-07-22 @@ -21705,7 +22103,7 @@ action.correlationsearch.label = ESCU - Unload Sysmon Filter Driver - Rule action.correlationsearch.annotations = {"analytic_story": ["Disabling Security Tools"], "cis20": ["CIS 8"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 50, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. action.notable.param.rule_title = Unload Sysmon Filter Driver action.notable.param.security_domain = endpoint @@ -21719,18 +22117,18 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process +search = | tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv* by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`unload_sysmon_filter_driver_filter`| table firstTime lastTime dest user count process_name process_id parent_process_name process [ESCU - Unloading AMSI via Reflection - Rule] action.escu = 0 action.escu.enabled = 1 description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \ -This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ +This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block. action.escu.mappings = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562"]} action.escu.data_models = [] action.escu.eli5 = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \ -This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ +This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block. action.escu.how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell. action.escu.known_false_positives = Potential for some third party applications to disable AMSI upon invocation. Filter as needed. @@ -21755,7 +22153,7 @@ action.correlationsearch.annotations = {"analytic_story": ["Malicious PowerShell schedule_window = auto action.notable = 1 action.notable.param.rule_description = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \ -This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ +This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block. action.notable.param.rule_title = Unloading AMSI via Reflection action.notable.param.security_domain = endpoint @@ -21796,7 +22194,7 @@ action.correlationsearch.label = ESCU - Deprecated - Unsigned Image Loaded by LS action.correlationsearch.annotations = {"analytic_story": ["Credential Dumping"], "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects loading of unsigned images by LSASS. Deprecated because too noisy. action.notable.param.rule_title = Unsigned Image Loaded by LSASS action.notable.param.security_domain = endpoint @@ -21850,7 +22248,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter` +search = `netbackup` | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `security_content_ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature | `unsuccessful_netbackup_backups_filter` [ESCU - Unusually Long Command Line - Rule] action.escu = 0 @@ -21881,7 +22279,7 @@ action.correlationsearch.label = ESCU - Unusually Long Command Line - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"], "cis20": ["CIS 8"], "confidence": 60, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "Processes.process_name", "role": ["Attacker"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Command lines that are extremely long may be indicative of malicious activity on your hosts. action.notable.param.rule_title = Unusually Long Command Line action.notable.param.security_domain = endpoint @@ -21895,7 +22293,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost) [ESCU - Unusually Long Command Line - MLTK - Rule] action.escu = 0 @@ -21904,7 +22302,7 @@ description = Command lines that are extremely long may be indicative of malicio action.escu.mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} action.escu.data_models = [] action.escu.eli5 = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. -action.escu.how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. +action.escu.how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. action.escu.known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. action.escu.creation_date = 2019-05-08 action.escu.modification_date = 2019-05-08 @@ -21922,7 +22320,7 @@ action.correlationsearch.label = ESCU - Unusually Long Command Line - MLTK - Rul action.correlationsearch.annotations = {"analytic_story": ["Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Ransomware"], "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. action.notable.param.rule_title = Unusually Long Command Line - MLTK action.notable.param.security_domain = endpoint @@ -21936,7 +22334,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter` [ESCU - Unusually Long Content-Type Length - Rule] action.escu = 0 @@ -21976,7 +22374,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_http` | eval cs_content_type_length = len(cs_content_type) | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length cs_content_type url | `unusually_long_content_type_length_filter` +search = `stream_http` | eval cs_content_type_length = len(cs_content_type) | where cs_content_type_length > 100 | table endtime src_ip dest_ip cs_content_type_length cs_content_type url | `unusually_long_content_type_length_filter` [ESCU - W3WP Spawning Shell - Rule] action.escu = 0 @@ -22007,7 +22405,7 @@ action.correlationsearch.label = ESCU - W3WP Spawning Shell - Rule action.correlationsearch.annotations = {"analytic_story": ["HAFNIUM Group"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Initial Access", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505.003"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This query identifies a shell, PowerShell.exe or Cmd.exe, spawning from W3WP.exe, or IIS. In addition to IIS logs, this behavior with an EDR product will capture potential webshell activity, similar to the HAFNIUM Group abusing CVEs, on publicly available Exchange mail servers. During triage, review the parent process and child process of the shell being spawned. Review the command-line arguments and any file modifications that may occur. Identify additional parallel process, child processes, that may highlight further commands executed. After triaging, work to contain the threat and patch the system that is vulnerable. action.notable.param.rule_title = W3WP Spawning Shell action.notable.param.security_domain = endpoint @@ -22052,7 +22450,7 @@ action.correlationsearch.label = ESCU - WBAdmin Delete System Backups - Rule action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware", "Ransomware"], "cis20": ["CIS 8"], "confidence": 50, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery. action.notable.param.rule_title = WBAdmin Delete System Backups action.notable.param.security_domain = endpoint @@ -22066,7 +22464,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter` [ESCU - WMI Permanent Event Subscription - Rule] action.escu = 0 @@ -22093,7 +22491,7 @@ action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Rule action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for the creation of WMI permanent event subscriptions. action.notable.param.rule_title = WMI Permanent Event Subscription action.notable.param.security_domain = endpoint @@ -22107,7 +22505,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?<consumer>[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter` +search = `wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter` [ESCU - WMI Permanent Event Subscription - Sysmon - Rule] action.escu = 0 @@ -22148,7 +22546,7 @@ action.correlationsearch.label = ESCU - WMI Permanent Event Subscription - Sysmo action.correlationsearch.annotations = {"analytic_story": ["Suspicious WMI Use"], "cis20": ["CIS 3", "CIS 5"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Privilege Escalation", "Stage:Persistence"], "impact": 30, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.003"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "observable": [{"name": "host", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This analytic looks for the creation of WMI permanent event subscriptions. The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation. WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\ All event subscriptions have three components \ 1. Filter - WQL Query for the events we want. EventID = 19 \ @@ -22211,7 +22609,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `powershell` EventCode=4104 Message= "*SELECT*" AND (Message="*Win32_Process*" OR Message="*Win32_Service*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmi_recon_running_process_or_services_filter` +search = `powershell` EventCode=4104 Message= "*SELECT*" AND (Message="*Win32_Process*" OR Message="*Win32_Service*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmi_recon_running_process_or_services_filter` [ESCU - WMI Temporary Event Subscription - Rule] action.escu = 0 @@ -22251,7 +22649,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?<query>[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter` +search = `wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter` [ESCU - WSReset UAC Bypass - Rule] action.escu = 0 @@ -22282,7 +22680,7 @@ action.correlationsearch.label = ESCU - WSReset UAC Bypass - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Defense Evasion Tactics"], "confidence": 90, "context": ["Source:Endpoint", "Stage:Persistence", "Stage:Privilege Escalation", "Stage:Defense Evasion", "Scope:Incoming"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1548.002"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Hostname"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search is to detect a suspicious modification of registry related to UAC bypass. This technique is to modify the registry in this detection, create a registry value with the path of the payload and run WSreset.exe to bypass User account Control. action.notable.param.rule_title = WSReset UAC Bypass action.notable.param.security_domain = endpoint @@ -22296,7 +22694,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" (Registry.registry_key_name = "(Default)" OR Registry.registry_key_name = "DelegateExecute") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" (Registry.registry_key_name = "(Default)" OR Registry.registry_key_name = "DelegateExecute") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter` [ESCU - Wbemprox COM Object Execution - Rule] action.escu = 0 @@ -22340,7 +22738,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=7 ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wbemprox_com_object_execution_filter` +search = `sysmon` EventCode=7 ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wbemprox_com_object_execution_filter` [ESCU - Web Fraud - Account Harvesting - Rule] action.escu = 0 @@ -22349,8 +22747,8 @@ description = WARNING, this detection has been marked deprecated by the Splunk T action.escu.mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} action.escu.data_models = [] action.escu.eli5 = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to identify the creation of multiple user accounts using the same email domain name. -action.escu.how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. -action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment&#151;improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. +action.escu.how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. +action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. action.escu.creation_date = 2018-10-08 action.escu.modification_date = 2018-10-08 action.escu.confidence = high @@ -22367,7 +22765,7 @@ action.correlationsearch.label = ESCU - Deprecated - Web Fraud - Account Harvest action.correlationsearch.annotations = {"analytic_story": ["Web Fraud Detection"], "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to identify the creation of multiple user accounts using the same email domain name. action.notable.param.rule_title = Web Fraud - Account Harvesting action.notable.param.security_domain = threat @@ -22381,7 +22779,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?<SessionID>\w+)" | rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)" | search Username=* | rex field=Username "@(?<email_domain>.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` +search = `stream_http` http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)" | stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain | where UniqueUsernames> 25 | `web_fraud___account_harvesting_filter` [ESCU - Web Fraud - Anomalous User Clickspeed - Rule] action.escu = 0 @@ -22421,7 +22819,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_http` http_content_type=text* | rex field=cookie "form_key=(?<session_id>\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) | `web_fraud___anomalous_user_clickspeed_filter` +search = `stream_http` http_content_type=text* | rex field=cookie "form_key=(?\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) | `web_fraud___anomalous_user_clickspeed_filter` [ESCU - Web Fraud - Password Sharing Across Accounts - Rule] action.escu = 0 @@ -22448,7 +22846,7 @@ action.correlationsearch.label = ESCU - Deprecated - Web Fraud - Password Sharin action.correlationsearch.annotations = {"analytic_story": ["Web Fraud Detection"], "cis20": ["CIS 16"], "nist": ["DE.DP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to identify user accounts that share a common password. action.notable.param.rule_title = Web Fraud - Password Sharing Across Accounts action.notable.param.security_domain = threat @@ -22462,7 +22860,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?<Username>[^&|^$]+)" | rex field=form_data "login\[password\]=(?<Password>[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 | `web_fraud___password_sharing_across_accounts_filter` +search = `stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 | `web_fraud___password_sharing_across_accounts_filter` [ESCU - Web Servers Executing Suspicious Processes - Rule] action.escu = 0 @@ -22471,7 +22869,7 @@ description = This search looks for suspicious processes on all systems labeled action.escu.mappings = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} action.escu.data_models = ["Endpoint"] action.escu.eli5 = This search looks for suspicious processes on all systems labeled as web servers. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. action.escu.known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. action.escu.creation_date = 2019-04-01 action.escu.modification_date = 2019-04-01 @@ -22489,7 +22887,7 @@ action.correlationsearch.label = ESCU - Web Servers Executing Suspicious Process action.correlationsearch.annotations = {"analytic_story": ["Apache Struts Vulnerability"], "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = This search looks for suspicious processes on all systems labeled as web servers. action.notable.param.rule_title = Web Servers Executing Suspicious Processes action.notable.param.security_domain = endpoint @@ -22503,7 +22901,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter` [ESCU - Wermgr Process Connecting To IP Check Web Services - Rule] action.escu = 0 @@ -22547,7 +22945,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") | stats min(_time) as firstTime max(_time) as lastTime count by process_path process_name process_id QueryName QueryStatus QueryResults Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_connecting_to_ip_check_web_services_filter` +search = `sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") | stats min(_time) as firstTime max(_time) as lastTime count by process_path process_name process_id QueryName QueryStatus QueryResults Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_connecting_to_ip_check_web_services_filter` [ESCU - Wermgr Process Create Executable File - Rule] action.escu = 0 @@ -22578,7 +22976,7 @@ action.correlationsearch.label = ESCU - Wermgr Process Create Executable File - action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = this search is designed to detect potential malicious wermgr.exe process that drops or create executable file. Since wermgr.exe is an application trigger when error encountered in a process, it is really un ussual to this process to drop executable file. This technique is commonly seen in trickbot malware where it injects it code to this process to execute it malicious behavior like downloading other payload action.notable.param.rule_title = Wermgr Process Create Executable File action.notable.param.security_domain = endpoint @@ -22592,7 +22990,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" | stats min(_time) as firstTime max(_time) as lastTime count by Image TargetFilename process_name dest EventCode ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_create_executable_file_filter` +search = `sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" | stats min(_time) as firstTime max(_time) as lastTime count by Image TargetFilename process_name dest EventCode ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_create_executable_file_filter` [ESCU - Wermgr Process Spawned CMD Or Powershell Process - Rule] action.escu = 0 @@ -22623,7 +23021,7 @@ action.correlationsearch.label = ESCU - Wermgr Process Spawned CMD Or Powershell action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Execution"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search is designed to detect suspicious cmd and powershell process spawned by wermgr.exe process. This suspicious behavior are commonly seen in code injection technique technique like trickbot to execute a shellcode, dll modules to run malicious behavior. action.notable.param.rule_title = Wermgr Process Spawned CMD Or Powershell Process action.notable.param.security_domain = endpoint @@ -22637,7 +23035,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" Processes.process_name = "cmd.exe" OR Processes.process_name = "powershell.exe" by Processes.parent_process_name Processes.parent_process_id Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_spawned_cmd_or_powershell_process_filter` +search = | tstats `security_content_summariesonly` values(Processes.process) as cmdline min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" Processes.process_name = "cmd.exe" OR Processes.process_name = "powershell.exe" by Processes.parent_process_name Processes.parent_process_id Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_spawned_cmd_or_powershell_process_filter` [ESCU - WinEvent Scheduled Task Created Within Public Path - Rule] action.escu = 0 @@ -22693,7 +23091,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_within_public_path_filter` +search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_within_public_path_filter` [ESCU - WinEvent Scheduled Task Created to Spawn Shell - Rule] action.escu = 0 @@ -22749,7 +23147,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_to_spawn_shell_filter` +search = `wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_to_spawn_shell_filter` [ESCU - WinRM Spawning a Process - Rule] action.escu = 0 @@ -22776,7 +23174,7 @@ action.correlationsearch.label = ESCU - WinRM Spawning a Process - Rule action.correlationsearch.annotations = {"analytic_story": ["Unusual Processes"], "kill_chain_phases": ["Exploitation", "Privilege Escalation", "Denial of Service"], "mitre_attack": ["T1190"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following analytic identifies suspicious processes spawning from WinRM (wsmprovhost.exe). This analytic is related to potential exploitation of CVE-2021-31166. which is a kernel-mode device driver http.sys vulnerability. Current proof of concept code will blue-screen the operating system. However, http.sys used by many different Windows processes, including WinRM. In this case, identifying suspicious process create (child processes) from `wsmprovhost.exe` is what this analytic is identifying. action.notable.param.rule_title = WinRM Spawning a Process action.notable.param.security_domain = endpoint @@ -22790,7 +23188,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winrm_spawning_a_process_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winrm_spawning_a_process_filter` [ESCU - Windows AdFind Exe - Rule] action.escu = 0 @@ -22817,7 +23215,7 @@ action.correlationsearch.label = ESCU - Windows AdFind Exe - Rule action.correlationsearch.annotations = {"analytic_story": ["NOBELIUM Group", "Domain Trust Discovery"], "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1018"], "nist": ["PR.PT", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller. action.notable.param.rule_title = Windows AdFind Exe action.notable.param.security_domain = endpoint @@ -22851,7 +23249,7 @@ action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splun action.escu.providing_technologies = [] action.escu.analytic_story = ["Ryuk Ransomware", "Windows Defense Evasion Tactics"] action.risk = 1 -action.risk.param._risk_message = Windows DisableAntiSpyware registry key set to 'disabled' on $dest$ +action.risk.param._risk_message = Windows DisableAntiSpyware registry key set to 'disabled' on $dest$ action.risk.param._risk = [] action.risk.param.verbose = 0 cron_schedule = 0 * * * * @@ -22862,7 +23260,7 @@ action.correlationsearch.label = ESCU - Windows DisableAntiSpyware Registry - Ru action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware", "Windows Defense Evasion Tactics"], "cis20": ["CIS 8"], "confidence": 80, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 30, "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1562.001"], "nist": ["PR.PT", "DE.CM"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. This particular behavior is typically executed when an ransomware actor gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated. action.notable.param.rule_title = Windows DisableAntiSpyware Registry action.notable.param.security_domain = endpoint @@ -22876,7 +23274,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000001)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000001)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter` [ESCU - Windows Event Log Cleared - Rule] action.escu = 0 @@ -22907,7 +23305,7 @@ action.correlationsearch.label = ESCU - Windows Event Log Cleared - Rule action.correlationsearch.annotations = {"analytic_story": ["Windows Log Manipulation", "Ransomware", "Clop Ransomware"], "cis20": ["CIS 3", "CIS 5", "CIS 6"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Defense Evasion"], "impact": 70, "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = The following analytic utilizes Windows Security Event ID 1102 or System log event 104 to identify when a Windows event log is cleared. Note that this analytic will require tuning or restricted to specific endpoints based on criticality. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred. action.notable.param.rule_title = Windows Event Log Cleared action.notable.param.security_domain = endpoint @@ -22965,7 +23363,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter` [ESCU - Windows connhost exe started forcefully - Rule] action.escu = 0 @@ -22992,7 +23390,7 @@ action.correlationsearch.label = ESCU - Deprecated - Windows connhost exe starte action.correlationsearch.annotations = {"analytic_story": ["Ryuk Ransomware"], "cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk. action.notable.param.rule_title = Windows connhost exe started forcefully action.notable.param.security_domain = endpoint @@ -23006,7 +23404,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process="*C:\\Windows\\system32\\conhost.exe* 0xffffffff *-ForceV1*" by Processes.user Processes.process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_connhost_exe_started_forcefully_filter` [ESCU - Windows hosts file modification - Rule] action.escu = 0 @@ -23033,7 +23431,7 @@ action.correlationsearch.label = ESCU - Deprecated - Windows hosts file modifica action.correlationsearch.annotations = {"analytic_story": ["Host Redirection"], "cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['dest'] +action.notable.param.nes_fields = ['dest'] action.notable.param.rule_description = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for modifications to the hosts file on all Windows endpoints across your environment. action.notable.param.rule_title = Windows hosts file modification action.notable.param.security_domain = endpoint @@ -23078,7 +23476,7 @@ action.correlationsearch.label = ESCU - Winword Spawning Cmd - Rule action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_name", "role": ["Target"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies Microsoft Word spawning `cmd.exe`. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). Cmd.exe spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line will indicate what is being executed. During triage, review parallel processes and identify any files that may have been written. It is possible that COM is utilized to trampoline the child process to `explorer.exe` or `wmiprvse.exe`. action.notable.param.rule_title = Winword Spawning Cmd action.notable.param.security_domain = endpoint @@ -23123,7 +23521,7 @@ action.correlationsearch.label = ESCU - Winword Spawning PowerShell - Rule action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachments"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_name", "role": ["Target"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies Microsoft Word spawning PowerShell. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written. action.notable.param.rule_title = Winword Spawning PowerShell action.notable.param.security_domain = endpoint @@ -23137,7 +23535,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `winword_spawning_powershell_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `winword_spawning_powershell_filter` [ESCU - Winword Spawning Windows Script Host - Rule] action.escu = 0 @@ -23168,7 +23566,7 @@ action.correlationsearch.label = ESCU - Winword Spawning Windows Script Host - R action.correlationsearch.annotations = {"analytic_story": ["Spearphishing Attachment"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Initial Access"], "impact": 70, "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"], "observable": [{"name": "dest", "role": ["Victim"], "type": "Endpoint"}, {"name": "user", "role": ["Victim"], "type": "User"}, {"name": "process_name", "role": ["Target"], "type": "Process"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user', 'dest'] +action.notable.param.nes_fields = ['user', 'dest'] action.notable.param.rule_description = The following detection identifies Microsoft Winword.exe spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64\`. `cscript.exe` or `wscript.exe` spawning from Winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly. action.notable.param.rule_title = Winword Spawning Windows Script Host action.notable.param.security_domain = endpoint @@ -23182,7 +23580,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winword_spawning_windows_script_host_filter` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winword_spawning_windows_script_host_filter` [ESCU - Write Executable in SMB Share - Rule] action.escu = 0 @@ -23213,7 +23611,7 @@ action.correlationsearch.label = ESCU - Write Executable in SMB Share - Rule action.correlationsearch.annotations = {"analytic_story": ["Trickbot"], "confidence": 100, "context": ["Source:Endpoint", "Stage:Lateral Movement"], "impact": 70, "kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1021.002"], "observable": [{"name": "user", "role": ["Victim"], "type": "User"}]} schedule_window = auto action.notable = 1 -action.notable.param.nes_fields = ['user'] +action.notable.param.nes_fields = ['user'] action.notable.param.rule_description = This search is to detect suspicious dropping or creating an executable file in known sensitive SMB share. This technique is commonly used for lateral movement like how trickbot try to infect other machine in the infected network. This detection catch the access event (FILE WRITE) access to a share. action.notable.param.rule_title = Write Executable in SMB Share action.notable.param.security_domain = endpoint @@ -23227,7 +23625,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `wineventlog_security` EventCode=5145 Relative_Target_Name IN ("*.exe","*.dll") Object_Type=File Share_Name IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") Access_Mask= "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode Share_Name Relative_Target_Name Object_Type Access_Mask user src_port Source_Address | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `write_executable_in_smb_share_filter` +search = `wineventlog_security` EventCode=5145 Relative_Target_Name IN ("*.exe","*.dll") Object_Type=File Share_Name IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") Access_Mask= "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode Share_Name Relative_Target_Name Object_Type Access_Mask user src_port Source_Address | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `write_executable_in_smb_share_filter` [ESCU - XMRIG Driver Loaded - Rule] action.escu = 0 @@ -23271,7 +23669,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" | stats min(_time) as firstTime max(_time) as lastTime count by Computer ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `xmrig_driver_loaded_filter` +search = `sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" | stats min(_time) as firstTime max(_time) as lastTime count by Computer ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `xmrig_driver_loaded_filter` [ESCU - aws detect attach to role policy - Rule] action.escu = 0 @@ -23351,7 +23749,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter` +search = `aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter` [ESCU - aws detect role creation - Rule] action.escu = 0 @@ -23560,7 +23958,7 @@ action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. disabled = true is_visible = false -search = | tstats count as api_calls from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time api_calls, user, HourOfDay, isWeekend | eventstats dc(api_calls) as api_calls by user, HourOfDay, isWeekend | where api_calls >= 1 | fit DensityFunction api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_api_calls_v1 dist=norm show_density=true +search = | tstats count as api_calls from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time api_calls, user, HourOfDay, isWeekend | eventstats dc(api_calls) as api_calls by user, HourOfDay, isWeekend | where api_calls >= 1 | fit DensityFunction api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_api_calls_v1 dist=norm show_density=true [ESCU - Baseline Of Cloud Instances Destroyed] action.escu = 0 @@ -23583,7 +23981,7 @@ action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, i More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. disabled = true is_visible = false -search = | tstats count as instances_destroyed from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h | makecontinuous span=1h _time | eval instances_destroyed=coalesce(instances_destroyed, (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time instances_destroyed, HourOfDay, isWeekend | fit DensityFunction instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1 dist=expon show_density=true +search = | tstats count as instances_destroyed from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h | makecontinuous span=1h _time | eval instances_destroyed=coalesce(instances_destroyed, (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time instances_destroyed, HourOfDay, isWeekend | fit DensityFunction instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1 dist=expon show_density=true [ESCU - Baseline Of Cloud Instances Launched] action.escu = 0 @@ -23606,7 +24004,7 @@ action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, i More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. disabled = true is_visible = false -search = | tstats count as instances_launched from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h | makecontinuous span=1h _time | eval instances_launched=coalesce(instances_launched, (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time instances_launched, HourOfDay, isWeekend | fit DensityFunction instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1 dist=expon show_density=true +search = | tstats count as instances_launched from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h | makecontinuous span=1h _time | eval instances_launched=coalesce(instances_launched, (random()%2)*0.0000000001) | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time instances_launched, HourOfDay, isWeekend | fit DensityFunction instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1 dist=expon show_density=true [ESCU - Baseline Of Cloud Security Group API Calls Per User] action.escu = 0 @@ -23628,7 +24026,7 @@ action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK action.escu.how_to_implement = You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. disabled = true is_visible = false -search = | tstats count as security_group_api_calls from datamodel=Change where All_Changes.object_category=firewall All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time security_group_api_calls, user, HourOfDay, isWeekend | eventstats dc(security_group_api_calls) as security_group_api_calls by user, HourOfDay, isWeekend | where security_group_api_calls >= 1 | fit DensityFunction security_group_api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_security_group_api_calls_v1 dist=norm show_density=true +search = | tstats count as security_group_api_calls from datamodel=Change where All_Changes.object_category=firewall All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | table _time security_group_api_calls, user, HourOfDay, isWeekend | eventstats dc(security_group_api_calls) as security_group_api_calls by user, HourOfDay, isWeekend | where security_group_api_calls >= 1 | fit DensityFunction security_group_api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_security_group_api_calls_v1 dist=norm show_density=true [ESCU - Baseline of API Calls per User ARN] action.escu = 0 @@ -23669,7 +24067,7 @@ dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line. -action.escu.how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +action.escu.how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. disabled = true is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`| eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel @@ -23691,10 +24089,10 @@ dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query. -action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +action.escu.how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel +search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel [ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK] action.escu = 0 @@ -23714,7 +24112,7 @@ schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ -In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ +In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. disabled = true is_visible = false @@ -23738,7 +24136,7 @@ schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window. action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ -In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ +In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. disabled = true is_visible = false @@ -23805,10 +24203,10 @@ dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week. -action.escu.how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +action.escu.how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel +search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel [ESCU - Baseline of Security Group Activity by ARN] action.escu = 0 @@ -23874,7 +24272,7 @@ action.escu.eli5 = The search counts the number of times a connection was observ action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count +search = | tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count [ESCU - Count of assets by category] action.escu = 0 @@ -23940,7 +24338,7 @@ action.escu.eli5 = This search creates permutations of your existing domains, re action.escu.how_to_implement = To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**. disabled = true is_visible = false -search = | dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count +search = | dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count [ESCU - Discover DNS records] action.escu = 0 @@ -23962,7 +24360,7 @@ action.escu.eli5 = The search takes corporate and common cloud provider domains action.escu.how_to_implement = To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation disabled = true is_visible = false -search = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?<domain>\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records +search = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records [ESCU - Identify Systems Creating Remote Desktop Traffic] action.escu = 0 @@ -23984,7 +24382,7 @@ action.escu.eli5 = This search counts the numbers of times the system has genera action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count +search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count [ESCU - Identify Systems Receiving Remote Desktop Traffic] action.escu = 0 @@ -24006,7 +24404,7 @@ action.escu.eli5 = This search counts the numbers of times the system has create action.escu.how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count +search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count [ESCU - Identify Systems Using Remote Desktop] action.escu = 0 @@ -24028,7 +24426,7 @@ action.escu.eli5 = This search counts the numbers of times the remote desktop pr action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count +search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count [ESCU - Monitor Successful Backups] action.escu = 0 @@ -24050,7 +24448,7 @@ action.escu.eli5 = This search is intended to give you a feel for how often succ action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. disabled = true is_visible = false -search = `netbackup` "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE +search = `netbackup` "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE [ESCU - Monitor Unsuccessful Backups] action.escu = 0 @@ -24072,7 +24470,7 @@ action.escu.eli5 = This search is intended to give you a feel for how often back action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. disabled = true is_visible = false -search = `netbackup` "An error occurred, failed to backup." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE +search = `netbackup` "An error occurred, failed to backup." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE [ESCU - Previously Seen AWS Cross Account Activity] action.escu = 0 @@ -24116,7 +24514,7 @@ action.escu.eli5 = This search looks for **AssumeRole** events where the request action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later)and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | table requestingAccountId requestedAccountId firstTime lastTime | outputlookup previously_seen_aws_cross_account_activity +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | table requestingAccountId requestedAccountId firstTime lastTime | outputlookup previously_seen_aws_cross_account_activity [ESCU - Previously Seen AWS Cross Account Activity - Update] action.escu = 0 @@ -24138,7 +24536,7 @@ action.escu.eli5 = This search looks for **AssumeRole** events where the request action.escu.how_to_implement = You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | stats min(firstTime) as firstTime max(lastTime) as lastTime by requestingAccountId requestedAccountId | outputlookup previously_seen_aws_cross_account_activity +search = | tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | stats min(firstTime) as firstTime max(lastTime) as lastTime by requestingAccountId requestedAccountId | outputlookup previously_seen_aws_cross_account_activity [ESCU - Previously Seen AWS Provisioning Activity Sources] action.escu = 0 @@ -24167,7 +24565,7 @@ action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support action.escu.full_search_name = ESCU - Previously Seen AWS Regions -description = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days +description = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days action.escu.creation_date = 2018-01-08 action.escu.modification_date = 2018-01-08 action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] @@ -24178,7 +24576,7 @@ dispatch.earliest_time = -1450m@m dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] -action.escu.eli5 = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days +action.escu.eli5 = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. disabled = true is_visible = false @@ -24204,7 +24602,7 @@ action.escu.eli5 = This search builds a table of the first and last times seen f action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role [ESCU - Previously Seen Cloud API Calls Per User Role - Update] action.escu = 0 @@ -24226,7 +24624,7 @@ action.escu.eli5 = This search updates the table of the first and last times see action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | table user, command, firstTimeSeen, lastTimeSeen | inputlookup previously_seen_cloud_api_calls_per_user_role append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by user, command | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_api_calls_per_user_role_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | table user, command, firstTimeSeen, lastTimeSeen | inputlookup previously_seen_cloud_api_calls_per_user_role append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by user, command | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_api_calls_per_user_role_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table user, command, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_api_calls_per_user_role [ESCU - Previously Seen Cloud Compute Creations By User - Initial] action.escu = 0 @@ -24248,7 +24646,7 @@ action.escu.eli5 = This search builds a table of previously seen users that have action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user | `drop_dm_object_name("All_Changes")` | outputlookup previously_seen_cloud_compute_creations_by_user | stats count +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user | `drop_dm_object_name("All_Changes")` | outputlookup previously_seen_cloud_compute_creations_by_user | stats count [ESCU - Previously Seen Cloud Compute Creations By User - Update] action.escu = 0 @@ -24270,7 +24668,7 @@ action.escu.eli5 = This search builds a table of previously seen users that have action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user| `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_compute_creations_by_user | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user | where lastTimeSeen > relative_time(now(), "-90d@d") | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_creations_by_user +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user| `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_compute_creations_by_user | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user | where lastTimeSeen > relative_time(now(), "-90d@d") | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_creations_by_user [ESCU - Previously Seen Cloud Compute Images - Initial] action.escu = 0 @@ -24292,7 +24690,7 @@ action.escu.eli5 = This search builds a table of previously seen images used to action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_images +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_images [ESCU - Previously Seen Cloud Compute Images - Update] action.escu = 0 @@ -24314,7 +24712,7 @@ action.escu.eli5 = This search builds a table of previously seen images used to action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | inputlookup append=t previously_seen_cloud_compute_images | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by image_id | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_images +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | inputlookup append=t previously_seen_cloud_compute_images | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by image_id | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_images [ESCU - Previously Seen Cloud Compute Instance Types - Initial] action.escu = 0 @@ -24336,7 +24734,7 @@ action.escu.eli5 = This search builds a table of previously seen cloud compute i action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type != "unknown" | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type != "unknown" | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types [ESCU - Previously Seen Cloud Compute Instance Types - Update] action.escu = 0 @@ -24358,7 +24756,7 @@ action.escu.eli5 = This search builds a table of previously seen cloud compute i action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type != "unknown" | inputlookup append=t previously_seen_cloud_compute_instance_types | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by instance_type | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_instance_type_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type | `drop_dm_object_name("All_Changes.Instance_Changes")` | where instance_type != "unknown" | inputlookup append=t previously_seen_cloud_compute_instance_types | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by instance_type | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_instance_type_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_compute_instance_types [ESCU - Previously Seen Cloud Instance Modifications By User - Initial] action.escu = 0 @@ -24380,7 +24778,7 @@ action.escu.eli5 = This search builds a table of previously seen users that have action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 c=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 c=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user [ESCU - Previously Seen Cloud Instance Modifications By User - Update] action.escu = 0 @@ -24402,7 +24800,7 @@ action.escu.eli5 = This search updates a table of previously seen Cloud Instance action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_instance_modifications_by_user | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_instance_modifications_by_user | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | outputlookup previously_seen_cloud_instance_modifications_by_user [ESCU - Previously Seen Cloud Provisioning Activity Sources - Initial] action.escu = 0 @@ -24424,7 +24822,7 @@ action.escu.eli5 = This search builds a table of the first and last times seen f action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_provisioning_activity_sources +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_provisioning_activity_sources [ESCU - Previously Seen Cloud Provisioning Activity Sources - Update] action.escu = 0 @@ -24446,14 +24844,14 @@ action.escu.eli5 = This returns the first and last times seen for every IP addre action.escu.how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | table src, firstTimeSeen, lastTimeSeen, City, Country, Region | inputlookup previously_seen_cloud_provisioning_activity_sources append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by src, City, Country, Region | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_provisioning_activity_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_provisioning_activity_sources +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | table src, firstTimeSeen, lastTimeSeen, City, Country, Region | inputlookup previously_seen_cloud_provisioning_activity_sources append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by src, City, Country, Region | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_provisioning_activity_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) | table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data | outputlookup previously_seen_cloud_provisioning_activity_sources [ESCU - Previously Seen Cloud Regions - Initial] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support action.escu.full_search_name = ESCU - Previously Seen Cloud Regions - Initial -description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days +description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days action.escu.creation_date = 2020-09-02 action.escu.modification_date = 2020-09-02 action.escu.analytic_story = ["Cloud Cryptomining"] @@ -24464,18 +24862,18 @@ dispatch.earliest_time = -90d@d dispatch.latest_time = -1d@d schedule_window = auto action.escu.providing_technologies = [] -action.escu.eli5 = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days +action.escu.eli5 = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_regions +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_regions [ESCU - Previously Seen Cloud Regions - Update] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support action.escu.full_search_name = ESCU - Previously Seen Cloud Regions - Update -description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days +description = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days action.escu.creation_date = 2020-09-02 action.escu.modification_date = 2020-09-02 action.escu.analytic_story = ["Cloud Cryptomining"] @@ -24486,11 +24884,11 @@ dispatch.earliest_time = -1450m@m dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] -action.escu.eli5 = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days +action.escu.eli5 = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days action.escu.how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. disabled = true is_visible = false -search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_regions | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by vendor_region | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_region_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_regions | stats count +search = | tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | inputlookup append=t previously_seen_cloud_regions | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by vendor_region | where lastTimeSeen > relative_time(now(), `previously_seen_cloud_region_forget_window`) | eventstats min(firstTimeSeen) as globalFirstTime | eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) | outputlookup previously_seen_cloud_regions | stats count [ESCU - Previously Seen EC2 AMIs] action.escu = 0 @@ -24534,7 +24932,7 @@ action.escu.eli5 = This search builds a table of previously seen EC2 instance ty action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. disabled = true is_visible = false -search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count +search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count [ESCU - Previously Seen EC2 Launches By User] action.escu = 0 @@ -24600,7 +24998,7 @@ action.escu.eli5 = This collects the services that have been started across your action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. disabled = true is_visible = false -search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | outputlookup previously_seen_running_windows_services +search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | outputlookup previously_seen_running_windows_services [ESCU - Previously Seen Running Windows Services - Update] action.escu = 0 @@ -24622,7 +25020,7 @@ action.escu.eli5 = This search returns the first and last time a Windows service action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above. disabled = true is_visible = false -search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | inputlookup previously_seen_running_windows_services append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by service | where lastTimeSeen > relative_time(now(), "`previously_seen_windows_service_forget_window`") | outputlookup previously_seen_running_windows_services +search = `wineventlog_system` EventCode=7036 | rex field=Message "The (?[-\(\)\s\w]+) service entered the (?\w+) state" | where state="running" | stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service | inputlookup previously_seen_running_windows_services append=t | stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by service | where lastTimeSeen > relative_time(now(), "`previously_seen_windows_service_forget_window`") | outputlookup previously_seen_running_windows_services [ESCU - Previously Seen Users In CloudTrail - Update] action.escu = 0 @@ -24710,7 +25108,7 @@ action.escu.eli5 = This search returns the first and last time a process was see action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table firstTimeSeen, lastTimeSeen, process_name, dest | inputlookup zoom_first_time_child_process append=t | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest | where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") | outputlookup zoom_first_time_child_process +search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table firstTimeSeen, lastTimeSeen, process_name, dest | inputlookup zoom_first_time_child_process append=t | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest | where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") | outputlookup zoom_first_time_child_process [ESCU - Previously seen API call per user roles in CloudTrail] action.escu = 0 @@ -24739,7 +25137,7 @@ action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support action.escu.full_search_name = ESCU - Previously seen S3 bucket access by remote IP -description = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" +description = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" action.escu.creation_date = 2018-06-28 action.escu.modification_date = 2018-06-28 action.escu.analytic_story = ["Suspicious AWS S3 Activities"] @@ -24750,7 +25148,7 @@ dispatch.earliest_time = -1450m@m dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] -action.escu.eli5 = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" +action.escu.eli5 = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search. disabled = true is_visible = false @@ -24773,10 +25171,10 @@ dispatch.latest_time = -10m@m schedule_window = auto action.escu.providing_technologies = [] action.escu.eli5 = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)` +search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)` [ESCU - Previously seen users in CloudTrail] action.escu = 0 @@ -24798,7 +25196,7 @@ action.escu.eli5 = This search looks for CloudTrail events where a user logs int action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search. disabled = true is_visible = false -search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail | stats count +search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail | stats count [ESCU - Systems Ready for Spectre-Meltdown Windows Patch] action.escu = 0 @@ -24820,7 +25218,7 @@ action.escu.eli5 = Some AV applications can cause the Spectre/Meltdown patch for action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. disabled = true is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` [ESCU - Update previously seen users in CloudTrail] action.escu = 0 @@ -24842,7 +25240,7 @@ action.escu.eli5 = This search looks for CloudTrail events where a user logs int action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search. disabled = true is_visible = false -search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail +search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country | inputlookup append=t previously_seen_users_console_logins_cloudtrail | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins_cloudtrail [ESCU - Windows Updates Install Failures] action.escu = 0 @@ -24864,7 +25262,7 @@ action.escu.eli5 = This search is intended to give you a feel for how often Wind action.escu.how_to_implement = You must be ingesting your Windows Update Logs disabled = true is_visible = false -search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d +search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d [ESCU - Windows Updates Install Successes] action.escu = 0 @@ -24886,7 +25284,7 @@ action.escu.eli5 = This search is intended to give you a feel for how often succ action.escu.how_to_implement = You must be ingesting your Windows Update Logs disabled = true is_visible = false -search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed by _time span=1d +search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed by _time span=1d @@ -24911,14 +25309,14 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = `aws_securityhub_firehose` "findings{}.Resources{}.Type"=AWSEC2Instance | rex field=findings{}.Resources{}.Id .*instance/(?<instance>.*)| rename instance as dest| search dest = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text as Remediation | table dest Title ProductArn Description FirstObservedAt RecordState Remediation +search = `aws_securityhub_firehose` "findings{}.Resources{}.Type"=AWSEC2Instance | rex field=findings{}.Resources{}.Id .*instance/(?.*)| rename instance as dest| search dest = $dest$ |rename findings{}.* as * | rename Remediation.Recommendation.Text as Remediation | table dest Title ProductArn Description FirstObservedAt RecordState Remediation [ESCU - AWS Investigate User Activities By ARN - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - AWS Investigate User Activities By ARN - Response Task -description = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. +description = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. action.escu.creation_date = 2019-04-30 action.escu.modification_date = 2019-04-30 action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "Cloud Cryptomining", "Command and Control", "Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Unusual AWS EC2 Modifications", "Suspicious Cloud User Activities", "AWS Suspicious Provisioning Activities", "Suspicious Cloud Instance Activities", "AWS Security Hub Alerts"] @@ -24926,7 +25324,7 @@ action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] action.escu.data_models = [] -action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. +action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information. action.escu.how_to_implement = none action.escu.known_false_positives = None at this time disabled = true @@ -25191,7 +25589,7 @@ action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Get Certificate logs for a domain - Response Task -description = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers. +description = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers. action.escu.creation_date = 2019-04-29 action.escu.modification_date = 2019-04-29 action.escu.analytic_story = ["Common Phishing Frameworks"] @@ -25199,7 +25597,7 @@ action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] action.escu.data_models = [] -action.escu.eli5 = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers. +action.escu.eli5 = This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers. action.escu.how_to_implement = none action.escu.known_false_positives = None at this time disabled = true @@ -25247,7 +25645,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic All_Traffic.dest_port=53 by All_Traffic.src All_Traffic.dest| `drop_dm_object_name(All_Traffic)` | rename src as src_ip | rename dest as dest_ip | search src_ip=$src_ip$ | search dest_ip = $dest_ip | eval ratio = (bytes_out/bytes_in) | table ratio +search = | tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic All_Traffic.dest_port=53 by All_Traffic.src All_Traffic.dest| `drop_dm_object_name(All_Traffic)` | rename src as src_ip | rename dest as dest_ip | search src_ip=$src_ip$ | search dest_ip = $dest_ip | eval ratio = (bytes_out/bytes_in) | table ratio [ESCU - Get EC2 Instance Details by instanceId - Response Task] action.escu = 0 @@ -25268,7 +25666,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = `aws_description` | dedup id sortby -_time |rename id as instanceId| search instanceId=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC +search = `aws_description` | dedup id sortby -_time |rename id as instanceId| search instanceId=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC [ESCU - Get EC2 Launch Details - Response Task] action.escu = 0 @@ -25394,7 +25792,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = `wineventlog_security` (signature_id=4718 OR signature_id=4717) dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature +search = `wineventlog_security` (signature_id=4718 OR signature_id=4717) dest=$dest$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature [ESCU - Get Logon Rights Modifications For User - Response Task] action.escu = 0 @@ -25415,7 +25813,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = `wineventlog_security` (signature_id=4718 OR signature_id=4717) user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature +search = `wineventlog_security` (signature_id=4718 OR signature_id=4717) user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature [ESCU - Get Notable History - Response Task] action.escu = 0 @@ -25478,7 +25876,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")` | search parent_process_name= $parent_process_name$ |search dest = $dest$ | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")` | search parent_process_name= $parent_process_name$ |search dest = $dest$ | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` [ESCU - Get Process File Activity - Response Task] action.escu = 0 @@ -25520,7 +25918,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")` | search process_name= $process_name$ | search dest = $dest$ | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` +search = | tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name("Processes")` | search process_name= $process_name$ | search dest = $dest$ | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` [ESCU - Get Process Information For Port Activity - Response Task] action.escu = 0 @@ -25583,7 +25981,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = `sysmon` EventCode>18 EventCode<22 | rename host as dest | search dest=$dest$| table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter +search = `sysmon` EventCode>18 EventCode<22 | rename host as dest | search dest=$dest$| table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter [ESCU - Get Web Session Information via session id - Response Task] action.escu = 0 @@ -25611,7 +26009,7 @@ action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate AWS User Activities by user field - Response Task -description = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information. +description = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information. action.escu.creation_date = 2018-03-12 action.escu.modification_date = 2018-03-12 action.escu.analytic_story = ["AWS User Monitoring", "Suspicious Cloud Authentication Activities"] @@ -25619,7 +26017,7 @@ action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] action.escu.data_models = [] -action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information. +action.escu.eli5 = This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information. action.escu.how_to_implement = none action.escu.known_false_positives = None at this time disabled = true @@ -25667,7 +26065,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication where Authentication.action=failure by Authentication.user | where distinct_count_dest > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` | search user=$user$ +search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication where Authentication.action=failure by Authentication.user | where distinct_count_dest > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` | search user=$user$ [ESCU - Investigate Network Traffic From src ip - Response Task] action.escu = 0 @@ -25772,14 +26170,14 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?<new_user>[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest| search dest=$dest$ | where sum_count/max_count!=2 | rename new_user AS user +search = `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest| search dest=$dest$ | where sum_count/max_count!=2 | rename new_user AS user [ESCU - Investigate Previous Unseen User - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate Previous Unseen User - Response Task -description = This search returns previous unseen user, which didn't log in for 30 days. +description = This search returns previous unseen user, which didn't log in for 30 days. action.escu.creation_date = 2019-12-10 action.escu.modification_date = 2019-12-10 action.escu.analytic_story = ["Credential Dumping"] @@ -25787,13 +26185,13 @@ action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] action.escu.data_models = ["Authentication"] -action.escu.eli5 = This search returns previous unseen user, which didn't log in for 30 days. +action.escu.eli5 = This search returns previous unseen user, which didn't log in for 30 days. action.escu.how_to_implement = none action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` | search dest=$dest$ +search = | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` | search dest=$dest$ [ESCU - Investigate Successful Remote Desktop Authentications - Response Task] action.escu = 0 @@ -25814,14 +26212,14 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")` | search dest=$dest$ | table firstTime lastTime src src_nt_domain dest user app count | sort count +search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("Authentication")` | search dest=$dest$ | table firstTime lastTime src src_nt_domain dest user app count | sort count [ESCU - Investigate Suspicious Strings in HTTP Header - Response Task] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = investigative action.escu.full_search_name = ESCU - Investigate Suspicious Strings in HTTP Header - Response Task -description = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest. +description = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest. action.escu.creation_date = 2017-10-20 action.escu.modification_date = 2017-10-20 action.escu.analytic_story = ["Apache Struts Vulnerability"] @@ -25829,13 +26227,13 @@ action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] action.escu.data_models = [] -action.escu.eli5 = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest. +action.escu.eli5 = This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest. action.escu.how_to_implement = none action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = `stream_http` | search src_ip=$src_ip$ | search dest_ip=$dest_ip$ | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?<suspicious_strings>cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url +search = `stream_http` | search src_ip=$src_ip$ | search dest_ip=$dest_ip$ | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url [ESCU - Investigate User Activities In Okta - Response Task] action.escu = 0 @@ -25877,7 +26275,7 @@ action.escu.known_false_positives = None at this time disabled = true schedule_window = auto is_visible = false -search = | tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| search http_method, "POST" | search src=$src$ +search = | tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| search http_method, "POST" | search src=$src$ [ESCU - Rundll32 LockWorkStation - Response Task] action.escu = 0 diff --git a/dist/escu/default/transforms.conf b/dist/escu/default/transforms.conf index 8d846d28cc..cac6d8d226 100644 --- a/dist/escu/default/transforms.conf +++ b/dist/escu/default/transforms.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security_content -# On Date: 2021-08-20T22:00:44 UTC +# On Date: 2021-08-27T14:05:22 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -88,6 +88,14 @@ min_matches = 1 filename = legit_domains.csv # description = A list of legit domains to be used as an ignore list for possible phishing sites +[local_file_inclusion_paths] +filename = local_file_inclusion_paths.csv +default_match = false +case_sensitive_match = false +# description = A list of interesting files in a local file inclusion attack +match_type = WILDCARD(local_file_inclusion_paths) +min_matches = 1 + [lookup_rare_process_allow_list_default] filename = rare_process_allow_list_default.csv default_match = false diff --git a/dist/escu/default/use_case_library.conf b/dist/escu/default/use_case_library.conf index 31f3de62c7..b6d2d030b1 100644 --- a/dist/escu/default/use_case_library.conf +++ b/dist/escu/default/use_case_library.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security_content -# On Date: 2021-08-20T22:00:45 UTC +# On Date: 2021-08-27T14:05:22 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -16,7 +16,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 searches = ["ESCU - AWS Investigate User Activities By AccessKeyId - Rule", "ESCU - Get Notable History - Rule", "ESCU - aws detect attach to role policy - Rule", "ESCU - aws detect permanent key creation - Rule", "ESCU - aws detect role creation - Rule", "ESCU - aws detect sts assume role abuse - Rule", "ESCU - aws detect sts get session token abuse - Rule", "ESCU - AWS Investigate User Activities By AccessKeyId - Response Task", "ESCU - Get Notable History - Response Task"] description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. -narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ +narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ This Analytic Story includes searches that will help you monitor your AWS CloudTrail logs for evidence of suspicious cross-account activity. For example, while accessing multiple AWS accounts and roles may be perfectly valid behavior, it may be suspicious when an account requests privileges of an account it has not accessed in the past. After identifying suspicious activities, you can use the provided investigative searches to help you probe more deeply. @@ -62,8 +62,8 @@ references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices. maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 searches = ["ESCU - AWS Excessive Security Scanning - Rule", "ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Get Notable History - Rule", "ESCU - Investigate AWS User Activities by user field - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Investigate AWS User Activities by user field - Response Task"] -description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. -narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ +description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. +narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ Fortunately, you can leverage Amazon Web Services (AWS) CloudTrail--a tool that helps you enable governance, compliance, and risk auditing of your AWS account--to give you increased visibility into your user and resource activity by recording AWS Management Console actions and API calls. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred.\ The detection searches in this Analytic Story are designed to help you uncover AWS API activities from users not listed in the identity table, as well as similar activities from disabled accounts. @@ -90,14 +90,14 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 searches = ["ESCU - Get Notable History - Rule", "ESCU - Investigate Suspicious Strings in HTTP Header - Rule", "ESCU - Investigate Web POSTs From src - Rule", "ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Investigate Suspicious Strings in HTTP Header - Response Task", "ESCU - Investigate Web POSTs From src - Response Task"] description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. -narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's <a href=https://www.usatoday.com/story/tech/2017/09/07/nations-biggest-hacks-and-data-breaches-millions/644311001/> 5th largest data breach</a>. The target, credit giant Equifax, <a href=https://money.cnn.com/2017/09/16/technology/equifax-breach-security-hole/index.html>told investigators</a> that it had become aware of the vulnerability two months before the attack. \ +narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ This Analytic Story contains two different searches that help to identify activity that may be related to this issue. The first search looks for characteristics of the `Content-Type` header consistent with attempts to exploit the vulnerability. This should be a relatively pertinent indicator, as the `Content-Type` header is generally consistent and does not have a large degree of variation.\ The second search looks for the execution of various commands typically entered on the command shell when an attacker first lands on a system. These commands are not generally executed on web servers during the course of day-to-day operation, but they may be used when the system is undergoing maintenance or troubleshooting.\ First, it is helpful is to understand how often the notable event is generated, as well as the commonalities in some of these events. This may help determine whether this is a common occurrence that is of a lesser concern or a rare event that may require more extensive investigation. It can also help to understand whether the issue is restricted to a single user or system or is broader in scope.\ When looking at the target of the behavior illustrated by the event, you should note the sensitivity of the user and or/system to help determine the potential impact. It is also helpful to see what other events involving the target have occurred in the recent past. This can help tie different events together and give further situational awareness regarding the target.\ Various types of information for external systems should be reviewed and (potentially) collected if the incident is, indeed, judged to be malicious. Information like this can be useful in generating your own threat intelligence to create alerts in the future.\ -Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ +Looking at the country, responsible party, and fully qualified domain names associated with the external IP address--as well as the registration information associated with those domain names, if they are frequently visited by others--can help you answer the question of "who," in regard to the external system. Answering that can help qualify the event and may serve useful for tracking. In addition, there are various sources that can provide some reputation information on the IP address or domain name, which can assist in determining if the event is malicious in nature. Finally, determining whether or not there are other events associated with the IP address may help connect some dots or show other events that should be brought into scope.\ Gathering various data elements on the system of interest can sometimes help quickly determine that something suspicious may be happening. Some of these items include determining who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted.\ hen a specific service or application is targeted, it is often helpful to know the associated version to help determine whether or not it is vulnerable to a specific exploit.\ hen it is suspected there is an attack targeting a web server, it is helpful to look at some of the behavior of the web service to see if there is evidence that the service has been compromised. Some indications of this might be network connections to external resources, the web service spawning child processes that are not associated with typical behavior, and whether the service wrote any files that might be malicious in nature.\ @@ -136,7 +136,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Shannon Davis"}] spec_version = 3 searches = ["ESCU - Detect Baron Samedit CVE-2021-3156 - Rule", "ESCU - Detect Baron Samedit CVE-2021-3156 Segfault - Rule", "ESCU - Detect Baron Samedit CVE-2021-3156 via OSQuery - Rule"] description = Uncover activity consistent with CVE-2021-3156. Discovered by the Qualys Research Team, this vulnerability has been found to affect sudo across multiple Linux distributions (Ubuntu 20.04 and prior, Debian 10 and prior, Fedora 33 and prior). As this vulnerability was committed to code in July 2011, there will be many distributions affected. Successful exploitation of this vulnerability allows any unprivileged user to gain root privileges on the vulnerable host. -narrative = A non-privledged user is able to execute the sudoedit command to trigger a buffer overflow. After the successful buffer overflow, they are then able to gain root privileges on the affected host. The conditions needed to be run are a trailing "\" along with shell and edit flags. Monitoring the /var/log directory on Linux hosts using the Splunk Universal Forwarder will allow you to pick up this behavior when using the provided detection. +narrative = A non-privledged user is able to execute the sudoedit command to trigger a buffer overflow. After the successful buffer overflow, they are then able to gain root privileges on the affected host. The conditions needed to be run are a trailing "\" along with shell and edit flags. Monitoring the /var/log directory on Linux hosts using the Splunk Universal Forwarder will allow you to pick up this behavior when using the provided detection. [analytic_story://Brand Monitoring] category = Abuse @@ -147,7 +147,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 searches = ["ESCU - Get Email Info - Rule", "ESCU - Get Emails From Specific Sender - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Process Responsible For The DNS Traffic - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Get Email Info - Response Task", "ESCU - Get Emails From Specific Sender - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task"] description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. -narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible&#51;phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ +narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ Notable events will include IP addresses, URLs, and user data. Drilling down can provide you with even more actionable intelligence, including likely geographic information, contextual searches to help you scope the problem, and investigative searches. @@ -171,8 +171,8 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 searches = ["ESCU - AWS Investigate Security Hub alerts by dest - Rule", "ESCU - AWS Investigate User Activities By ARN - Rule", "ESCU - Abnormally High Number Of Cloud Instances Launched - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Get EC2 Instance Details by instanceId - Rule", "ESCU - Get EC2 Launch Details - Rule", "ESCU - Get Notable History - Rule", "ESCU - Investigate AWS activities via region name - Rule", "ESCU - AWS Investigate Security Hub alerts by dest - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get EC2 Instance Details by instanceId - Response Task", "ESCU - Get EC2 Launch Details - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Investigate AWS activities via region name - Response Task"] description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. -narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ -Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ +narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ +Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ When malicious miners appropriate a cloud instance, often spinning up hundreds of new instances, the costs can become astronomical for the account holder. So it is critically important to monitor your systems for suspicious activities that could indicate that your network has been infiltrated. \ This Analytic Story is focused on detecting suspicious new instances in your cloud environment to help prevent cryptominers from gaining a foothold. It contains detection searches that will detect when a previously unused instance type or AMI is used. It also contains support searches to build lookup files to ensure proper execution of the detection searches. @@ -184,7 +184,7 @@ references = ["https://www.cyberark.com/resources/threat-research-blog/golden-sa maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] spec_version = 3 searches = ["ESCU - AWS SAML Access by Provider User and Principal - Rule", "ESCU - AWS SAML Update identity provider - Rule", "ESCU - Certutil exe certificate extraction - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - O365 Add App Role Assignment Grant User - Rule", "ESCU - O365 Added Service Principal - Rule", "ESCU - O365 Excessive SSO logon errors - Rule", "ESCU - O365 New Federated Domain Added - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule"] -description = This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. +description = This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements. narrative = This story is composed of detection searches based on endpoint that addresses the use of Mimikatz, Escalation of Privileges and Abnormal processes that may indicate the extraction of Federated directory objects such as passwords, Oauth2 tokens, certificates and keys. Cloud environment (AWS, Azure) related events are also addressed in specific cloud environment detection searches. [analytic_story://Cobalt Strike] @@ -216,8 +216,8 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Jose Hernandez"}] spec_version = 3 searches = ["ESCU - Get Notable History - Rule", "ESCU - Investigate Network Traffic From src ip - Rule", "ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Investigate Network Traffic From src ip - Response Task"] description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. -narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before&#151;a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ -This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ +narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ +This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ Searches in this Analytic Story leverage the capabilities of OSquery to address ColdRoot detection from several different angles, such as looking for the existence of associated files and processes, and monitoring for signs of an installed keylogger. [analytic_story://Collection and Staging] @@ -253,7 +253,7 @@ references = ["https://github.com/splunk/cloud-datamodel-security-research"] maintainers = [{"company": "Rico Valdez, Splunk", "email": "-", "name": "Rod Soto"}] spec_version = 3 searches = ["ESCU - GCP GCR container uploaded - Rule", "ESCU - New container uploaded to AWS ECR - Rule"] -description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. +description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. narrative = Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. [analytic_story://Credential Dumping] @@ -263,9 +263,9 @@ version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Creation of lsass Dump with Taskmgr - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Dump LSASS via procdump - Rule", "ESCU - Dump LSASS via procdump Rename - Rule", "ESCU - Extract SAM from Registry - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations - Rule", "ESCU - Investigate Pass the Hash Attempts - Rule", "ESCU - Investigate Pass the Ticket Attempts - Rule", "ESCU - Investigate Previous Unseen User - Rule", "ESCU - Ntdsutil Export NTDS - Rule", "ESCU - SAM Database File Access Attempt - Rule", "ESCU - SecretDumps Offline NTDS Dumping Tool - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations - Response Task", "ESCU - Investigate Pass the Hash Attempts - Response Task", "ESCU - Investigate Pass the Ticket Attempts - Response Task", "ESCU - Investigate Previous Unseen User - Response Task"] +searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Creation of lsass Dump with Taskmgr - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Copy of ShadowCopy with Script Block Logging - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Dump LSASS via procdump - Rule", "ESCU - Dump LSASS via procdump Rename - Rule", "ESCU - Esentutl SAM Copy - Rule", "ESCU - Extract SAM from Registry - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations - Rule", "ESCU - Investigate Pass the Hash Attempts - Rule", "ESCU - Investigate Pass the Ticket Attempts - Rule", "ESCU - Investigate Previous Unseen User - Rule", "ESCU - Ntdsutil Export NTDS - Rule", "ESCU - SAM Database File Access Attempt - Rule", "ESCU - SecretDumps Offline NTDS Dumping Tool - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations - Response Task", "ESCU - Investigate Pass the Hash Attempts - Response Task", "ESCU - Investigate Pass the Ticket Attempts - Response Task", "ESCU - Investigate Previous Unseen User - Response Task"] description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. -narrative = Credential dumping&#151;gathering credentials from a target system, often hashed or encrypted&#151;is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ +narrative = Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ The detection searches in this Analytic Story monitor access to the Local Security Authority Subsystem Service (LSASS) process, the usage of shadowcopies for credential dumping and some other techniques for credential dumping. @@ -279,7 +279,7 @@ spec_version = 3 searches = ["ESCU - Create local admin accounts using net exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect Renamed PSExec - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process File Activity - Rule", "ESCU - Get Process Info - Rule", "ESCU - Get Process Information For Port Activity - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Deleted Or Created via CMD - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process File Activity - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Process Information For Port Activity - Response Task"] description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. narrative = The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ -There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic data&#1151;all of which could confer advantages in various arenas. They may also target critical infrastructure. \ +There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ One joint Technical Alert (TA) issued by the Department of Homeland and the FBI in mid-March of 2018 attributed some cyber activity targeting utility infrastructure to operatives sponsored by the Russian government. The hackers executed spearfishing attacks, installed malware, employed watering-hole domains, and more. While they caused no physical damage, the attacks provoked fears that a nation-state could turn off water, redirect power, or compromise a nuclear power plant.\ Suspicious activities--spikes in SMB traffic, processes that launch netsh (to modify the network configuration), suspicious registry modifications, and many more--may all be events you may wish to investigate further. While the use of these technique may be an indication that a nation-state actor is attempting to compromise your environment, it is important to note that these techniques are often employed by other groups, as well. @@ -291,9 +291,9 @@ references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.impe maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 searches = ["ESCU - Get Notable History - Rule", "ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Notable History - Response Task"] -description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. -narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ -The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. +description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. +narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ +The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. [analytic_story://DNS Hijacking] category = Adversary Tactics @@ -306,10 +306,10 @@ searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. narrative = Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ -On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity and Infrastructure Security Agency (CISA) raised awareness of some high-profile DNS hijacking attacks against infrastructure, both in the United States and abroad. It issued Emergency Directive 19-01 (see https://cyber.dhs.gov/ed/19-01/), which summarized the activity and required government agencies to take the following four actions, all within 10 days: \ +On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity and Infrastructure Security Agency (CISA) raised awareness of some high-profile DNS hijacking attacks against infrastructure, both in the United States and abroad. It issued Emergency Directive 19-01 (see https://cyber.dhs.gov/ed/19-01/), which summarized the activity and required government agencies to take the following four actions, all within 10 days: \ 1. For all .gov or other agency-managed domains, audit public DNS records on all authoritative and secondary DNS servers, verify that they resolve to the intended location or report them to CISA.\ -1. Update the passwords for all accounts on systems that can make changes to each agency 2019's DNS records.\ -1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency's 2019 DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ +1. Update the passwords for all accounts on systems that can make changes to each agency 2019's DNS records.\ +1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency's 2019 DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ 1. CISA will begin regular delivery of newly added certificates to Certificate Transparency (CT) logs for agency domains via the Cyber Hygiene service. Upon receipt, agencies must immediately begin monitoring CT log data for certificates issued that they did not request. If an agency confirms that a certificate was unauthorized, it must report the certificate to the issuing certificate authority and to CISA. Of course, it makes sense to put equivalent actions in place within your environment, as well. \ In DNS hijacking, the attacker assumes control over an account or makes use of a DNS service exploit to make changes to DNS records. Once they gain access, attackers can substitute their own MX records, name-server records, and addresses, redirecting emails and traffic through their infrastructure, where they can read, copy, or modify information seen. They can also generate valid encryption certificates to help them avoid browser-certificate checks. In one notable attack on the Internet service provider, GoDaddy, the hackers altered Sender Policy Framework (SPF) records a relatively minor change that did not inflict excessive damage but allowed for more effective spam campaigns.\ The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment. @@ -376,7 +376,7 @@ version = 1 references = ["https://www.redhat.com/en/topics/devops/what-is-devsecops"] maintainers = [{"company": "Splunk", "email": "-", "name": "Patrick Bareiss"}] spec_version = 3 -searches = ["ESCU - AWS ECR Container Scanning Findings High - Rule", "ESCU - AWS ECR Container Scanning Findings Low Informational Unknown - Rule", "ESCU - AWS ECR Container Scanning Findings Medium - Rule", "ESCU - AWS ECR Container Upload Outside Business Hours - Rule", "ESCU - AWS ECR Container Upload Unknown User - Rule"] +searches = ["ESCU - AWS ECR Container Scanning Findings High - Rule", "ESCU - AWS ECR Container Scanning Findings Low Informational Unknown - Rule", "ESCU - AWS ECR Container Scanning Findings Medium - Rule", "ESCU - AWS ECR Container Upload Outside Business Hours - Rule", "ESCU - AWS ECR Container Upload Unknown User - Rule", "ESCU - Kubernetes Nginx Ingress LFI - Rule", "ESCU - Kubernetes Nginx Ingress RFI - Rule", "ESCU - Kubernetes Scanner Image Pulling - Rule"] description = This story is focused around detecting attacks on a DevSecOps lifeccycle which consists of the phases plan, code, build, test, release, deploy, operate and monitor. narrative = DevSecOps is a collaborative framework, which thinks about application and infrastructure security from the start. This means that security tools are part of the continuous integration and continuous deployment pipeline. In this analytics story, we focused on detections around the tools used in this framework such as GitHub as a version control system, GDrive for the documentation, CircleCI as the CI/CD pipeline, Kubernetes as the container execution engine and multiple security tools such as Semgrep and Kube-Hunter. @@ -411,7 +411,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 searches = ["ESCU - DNS Exfiltration Using Nslookup App - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Excessive Usage of NSLOOKUP App - Rule", "ESCU - Get DNS Server History for a host - Rule", "ESCU - Get DNS traffic ratio - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Process Responsible For The DNS Traffic - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task"] description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and deny lists. -narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and deny lists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. +narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and deny lists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. [analytic_story://Emotet Malware DHS Report TA18-201A ] category = Malware @@ -472,10 +472,10 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 searches = ["ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get DNS Server History for a host - Rule", "ESCU - Get DNS traffic ratio - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Get Process Information For Port Activity - Rule", "ESCU - Get Process Responsible For The DNS Traffic - Rule", "ESCU - Investigate Successful Remote Desktop Authentications - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task", "ESCU - Investigate Successful Remote Desktop Authentications - Response Task"] description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. -narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ -These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ -In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ -Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. +narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ +These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ +In June of 2018, The Department of Homeland Security, together with the FBI and other U.S. government partners, issued Technical Alert (TA-18-149A) to advise the public about two variants of North Korean malware. One variant, dubbed "Joanap," is a multi-stage peer-to-peer botnet that allows North Korean state actors to exfiltrate data, download and execute secondary payloads, and initialize proxy communications. The other variant, "Brambul," is a Windows32 SMB worm that is dropped into a victim network. When executed, the malware attempts to spread laterally within a victim's local subnet, connecting via the SMB protocol and initiating brute-force password attacks. It reports details to the Hidden Cobra actors via email, so they can use the information for secondary remote operations.\ +Among other searches in this Analytic Story is a detection search that looks for the creation or deletion of hidden shares, such as, "adnim$," which the Hidden Cobra malware creates on the target system. Another looks for the creation of three malicious files associated with the malware. You can also use a search in this story to investigate activity that indicates that malware is sending email back to the attackers. [analytic_story://IcedID] category = Malware @@ -486,7 +486,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Teoderick Contreras" spec_version = 3 searches = ["ESCU - Account Discovery With Net App - Rule", "ESCU - CHCP Command Execution - Rule", "ESCU - Create Remote Thread In Shell Application - Rule", "ESCU - Drop IcedID License dat - Rule", "ESCU - Eventvwr UAC Bypass - Rule", "ESCU - FodHelper UAC Bypass - Rule", "ESCU - IcedID Exfiltrated Archived File Creation - Rule", "ESCU - Mshta spawning Rundll32 OR Regsvr32 Process - Rule", "ESCU - NLTest Domain Trust Discovery - Rule", "ESCU - Office Application Spawn Regsvr32 process - Rule", "ESCU - Office Application Spawn rundll32 process - Rule", "ESCU - Office Document Executing Macro Code - Rule", "ESCU - Office Product Spawning MSHTA - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Rundll32 Create Remote Thread To A Process - Rule", "ESCU - Rundll32 CreateRemoteThread In Browser - Rule", "ESCU - Rundll32 DNSQuery - Rule", "ESCU - Rundll32 Process Creating Exe Dll Files - Rule", "ESCU - Schedule Task with Rundll32 Command Trigger - Rule", "ESCU - Sqlite Module In Temp Folder - Rule", "ESCU - Suspicious IcedID Regsvr32 Cmdline - Rule", "ESCU - Suspicious IcedID Rundll32 Cmdline - Rule", "ESCU - Suspicious Rundll32 PluginInit - Rule", "ESCU - WinEvent Scheduled Task Created Within Public Path - Rule"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the IcedID banking trojan, including looking for file writes associated with its payload, process injection, shellcode execution and data collection. -narrative = IcedId banking trojan campaigns targeting banks and other vertical sectors.This malware is known in Microsoft Windows OS targetting browser such as firefox and chrom to steal banking information. It is also known to its unique payload downloaded in C2 where it can be a .png file that hides the core shellcode bot using steganography technique or gzip dat file that contains "license.dat" which is the actual core icedid bot. +narrative = IcedId banking trojan campaigns targeting banks and other vertical sectors.This malware is known in Microsoft Windows OS targetting browser such as firefox and chrom to steal banking information. It is also known to its unique payload downloaded in C2 where it can be a .png file that hides the core shellcode bot using steganography technique or gzip dat file that contains "license.dat" which is the actual core icedid bot. [analytic_story://Ingress Tool Transfer] category = Adversary Tactics @@ -555,8 +555,8 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect Renamed PSExec - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Get Process Information For Port Activity - Rule", "ESCU - Investigate Successful Remote Desktop Authentications - Rule", "ESCU - Kerberoasting spn request with RC4 encryption - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Investigate Successful Remote Desktop Authentications - Response Task"] description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. -narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ -Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ +narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ +Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ An adversary can use lateral movement for multiple purposes, including remote execution of tools, pivoting to additional systems, obtaining access to specific information or files, access to additional credentials, exfiltrating data, or delivering a secondary effect. Adversaries may use legitimate credentials alongside inherent network and operating-system functionality to remotely connect to other systems and remain under the radar of network defenders.\ If there is evidence of lateral movement, it is imperative for analysts to collect evidence of the associated offending hosts. For example, an attacker might leverage host A to gain access to host B. From there, the attacker may try to move laterally to host C. In this example, the analyst should gather as much information as possible from all three hosts. \ It is also important to collect authentication logs for each host, to ensure that the offending accounts are well-documented. Analysts should account for all processes to ensure that the attackers did not install unauthorized software. @@ -568,7 +568,7 @@ version = 5 references = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Any Powershell DownloadFile - Rule", "ESCU - Any Powershell DownloadString - Rule", "ESCU - Detect Empire with PowerShell Script Block Logging - Rule", "ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - PowerShell Domain Enumeration - Rule", "ESCU - PowerShell Loading DotNET into Memory via System Reflection Assembly - Rule", "ESCU - Powershell Creating Thread Mutex - Rule", "ESCU - Powershell Enable SMB1Protocol Feature - Rule", "ESCU - Powershell Execute COM Object - Rule", "ESCU - Powershell Fileless Process Injection via GetProcAddress - Rule", "ESCU - Powershell Fileless Script Contains Base64 Encoded Content - Rule", "ESCU - Powershell Processing Stream Of Data - Rule", "ESCU - Powershell Using memory As Backing Store - Rule", "ESCU - Recon AVProduct Through Pwh or WMI - Rule", "ESCU - Recon Using WMI Class - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unloading AMSI via Reflection - Rule", "ESCU - WMI Recon Running Process Or Services - Rule", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] +searches = ["ESCU - Any Powershell DownloadFile - Rule", "ESCU - Any Powershell DownloadString - Rule", "ESCU - Detect Empire with PowerShell Script Block Logging - Rule", "ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - PowerShell 4104 Hunting - Rule", "ESCU - PowerShell Domain Enumeration - Rule", "ESCU - PowerShell Loading DotNET into Memory via System Reflection Assembly - Rule", "ESCU - Powershell Creating Thread Mutex - Rule", "ESCU - Powershell Enable SMB1Protocol Feature - Rule", "ESCU - Powershell Execute COM Object - Rule", "ESCU - Powershell Fileless Process Injection via GetProcAddress - Rule", "ESCU - Powershell Fileless Script Contains Base64 Encoded Content - Rule", "ESCU - Powershell Processing Stream Of Data - Rule", "ESCU - Powershell Using memory As Backing Store - Rule", "ESCU - Recon AVProduct Through Pwh or WMI - Rule", "ESCU - Recon Using WMI Class - Rule", "ESCU - Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Unloading AMSI via Reflection - Rule", "ESCU - WMI Recon Running Process Or Services - Rule", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. narrative = The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope. \ The following factors may assist you in determining whether the event is malicious: \ @@ -576,7 +576,7 @@ The following factors may assist you in determining whether the event is malicio 1. Responsible party \ 1. Fully qualified domain names associated with the external IP address \ 1. Registration of fully qualified domain names associated with external IP address \ -Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you answer some questions surrounding the attacker and details related to the external system. In addition, there are various sources--such as VirusTotal&#151; that can provide some reputation information on the IP address or domain name, which can assist in determining whether the event is malicious. Finally, determining whether there are other events associated with the IP address may help connect data points or show other events that should be brought into scope. \ +Determining whether it is a dynamic domain frequently visited by others and/or how third parties categorize it can also help you answer some questions surrounding the attacker and details related to the external system. In addition, there are various sources--such as VirusTotal— that can provide some reputation information on the IP address or domain name, which can assist in determining whether the event is malicious. Finally, determining whether there are other events associated with the IP address may help connect data points or show other events that should be brought into scope. \ Gathering data on the system of interest can sometimes help you quickly determine whether something suspicious is happening. Some of these items include finding out who else may have recently logged into the system, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and whether there are any known vulnerabilities on the system. This information can often highlight other activity commonly seen in attack scenarios or give more information about how the system may have been targeted. \ Often, a simple inspection of the process name and path can tell you if the system has been compromised. For example, if `svchost.exe` is found running from a location other than `C:\Windows\System32`, it is likely something malicious designed to hide in plain sight when cursorily reviewing process names. Similarly, if the process itself seems legitimate, but the parent process is running from the temporary browser cache, that could be indicative of activity initiated via a compromised website a user visited. \ It can also be very helpful to examine various behaviors of the process of interest or the parent of the process of interest. For example, if it turns out the process of interest is malicious, it would be good to see if the parent to that process spawned other processes that might be worth further scrutiny. If a process is suspect, a review of the network connections made in and around the time of the event and/or whether the process spawned any child processes could be helpful, as well. \ @@ -680,8 +680,8 @@ spec_version = 3 searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. narrative = This story was created as a joint effort between iDefense and Splunk.\ -iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ -The MUDCARP techniques include the use of the compressed-folders module from Microsoft, zipfldr.dll, with RouteTheCall export to run the malicious process or command. After a successful reboot, the malware is made persistent by a manipulating `[HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Run]'help'='c:\\windows\\system32\\rundll32.exe c:\\windows\\system32\\zipfldr.dll,RouteTheCall c:\\programdata\\winapp.exe'`. Though this technique is not exclusive to MUDCARP, it has been spotted in the group's arsenal of advanced techniques seen in the wild.\ +iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ +The MUDCARP techniques include the use of the compressed-folders module from Microsoft, zipfldr.dll, with RouteTheCall export to run the malicious process or command. After a successful reboot, the malware is made persistent by a manipulating `[HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Run]'help'='c:\\windows\\system32\\rundll32.exe c:\\windows\\system32\\zipfldr.dll,RouteTheCall c:\\programdata\\winapp.exe'`. Though this technique is not exclusive to MUDCARP, it has been spotted in the group's arsenal of advanced techniques seen in the wild.\ This Analytic Story searches for evidence of tactics, techniques, and procedures (TTPs) that allow for the use of a endpoint detection-and-response (EDR) bypass technique to mask the true parent of a malicious process. It can also be set as a registry key for further sandbox evasion and to allow the malware to launch only after reboot.\ If behavioral searches included in this story yield positive hits, iDefense recommends conducting IOC searches for the following:\ \ @@ -778,7 +778,7 @@ spec_version = 3 searches = ["ESCU - Detect ARP Poisoning - Rule", "ESCU - Detect IPv6 Network Infrastructure Threats - Rule", "ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Detect Port Security Violation - Rule", "ESCU - Detect Rogue DHCP Server - Rule", "ESCU - Detect Software Download To Network Device - Rule", "ESCU - Detect Traffic Mirroring - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Notable History - Response Task"] description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. narrative = Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ -This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure&#151; effectively increasing the attack surface and accessing private services/data. +This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. [analytic_story://Ryuk Ransomware] category = Malware @@ -813,9 +813,9 @@ spec_version = 3 searches = ["ESCU - Attacker Tools On Endpoint - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect Renamed PSExec - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Get Backup Logs For Endpoint - Rule", "ESCU - Get History Of Email Sources - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Get Process Information For Port Activity - Rule", "ESCU - Investigate Successful Remote Desktop Authentications - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Get Backup Logs For Endpoint - Response Task", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Investigate Successful Remote Desktop Authentications - Response Task"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. narrative = The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ -Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ -SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ -In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ +Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ +SamSam attacks are different beasts. They have become progressively more targeted and skillful than typical ransomware attacks. First, malicious actors break into a victim's network, surveil it, then run the malware manually. The attacks are tailored to cause maximum damage and the threat actors usually demand amounts in the tens of thousands of dollars.\ +In a typical attack on one large healthcare organization in 2018, the company ended up paying a ransom of four Bitcoins, then worth $56,707. Reports showed that access to the company's files was restored within two hours of paying the sum.\ According to Sophos, SamSam previously leveraged RDP to gain access to targeted networks via brute force. SamSam is not spread automatically, like other malware. It requires skill because it forces the attacker to adapt their tactics to the individual environment. Next, the actors escalate their privileges to admin level. They scan the networks for worthy targets, using conventional tools, such as PsExec or PaExec, to deploy/execute, quickly encrypting files.\ This Analytic Story includes searches designed to help detect and investigate signs of the SamSam ransomware, such as the creation of fileswrites to system32, writes with tell-tale extensions, batch files written to system32, and evidence of brute-force attacks via RDP. @@ -840,12 +840,12 @@ spec_version = 3 searches = ["ESCU - Detect Outlook exe writing a zip file - Rule", "ESCU - Excel Spawning PowerShell - Rule", "ESCU - Excel Spawning Windows Script Host - Rule", "ESCU - Office Application Spawn rundll32 process - Rule", "ESCU - Office Document Creating Schedule Task - Rule", "ESCU - Office Document Executing Macro Code - Rule", "ESCU - Office Document Spawned Child Process To Download - Rule", "ESCU - Office Product Spawning BITSAdmin - Rule", "ESCU - Office Product Spawning CertUtil - Rule", "ESCU - Office Product Spawning MSHTA - Rule", "ESCU - Office Product Spawning Rundll32 with no DLL - Rule", "ESCU - Office Product Spawning Wmic - Rule", "ESCU - Process Creating LNK file in Suspicious Location - Rule", "ESCU - Winword Spawning Cmd - Rule", "ESCU - Winword Spawning PowerShell - Rule"] description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. narrative = Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ -As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ -While any kind of file may contain a malicious payload, some are more likely to be perceived as benign (and thus more often escape notice) by the average victim&#151;especially when the attacker sends an email that seems to be from one of their contacts. An example is Microsoft Office files. Most corporate users are familiar with documents with the following suffixes: .doc/.docx (MS Word), .xls/.xlsx (MS Excel), and .ppt/.pptx (MS PowerPoint), so they may click without a second thought, slashing a hole in their organizations' security. \ +As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ +While any kind of file may contain a malicious payload, some are more likely to be perceived as benign (and thus more often escape notice) by the average victim—especially when the attacker sends an email that seems to be from one of their contacts. An example is Microsoft Office files. Most corporate users are familiar with documents with the following suffixes: .doc/.docx (MS Word), .xls/.xlsx (MS Excel), and .ppt/.pptx (MS PowerPoint), so they may click without a second thought, slashing a hole in their organizations' security. \ Following is a typical series of events, according to an [article by Trend Micro](https://blog.trendmicro.com/trendlabs-security-intelligence/rising-trend-attackers-using-lnk-files-download-malware/):\ 1. Attacker sends a phishing email. Recipient downloads the attached file, which is typically a .docx or .zip file with an embedded .lnk file\ 1. The .lnk file executes a PowerShell script\ -1. Powershell executes a reverse shell, rendering the exploit successful </ol>As a side note, adversaries are likely to use a tool like Empire to craft and obfuscate payloads and their post-injection activities, such as [exfiltration, lateral movement, and persistence](https://github.com/EmpireProject/Empire).\ +1. Powershell executes a reverse shell, rendering the exploit successful As a side note, adversaries are likely to use a tool like Empire to craft and obfuscate payloads and their post-injection activities, such as [exfiltration, lateral movement, and persistence](https://github.com/EmpireProject/Empire).\ This Analytic Story focuses on detecting signs that a malicious payload has been injected into your environment. For example, one search detects outlook.exe writing a .zip file. Another looks for suspicious .lnk files launching processes. [analytic_story://Suspicious AWS Login Activities] @@ -869,7 +869,7 @@ spec_version = 3 searches = ["ESCU - AWS Investigate User Activities By ARN - Rule", "ESCU - AWS S3 Bucket details via bucketName - Rule", "ESCU - Detect New Open S3 Buckets over AWS CLI - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Get All AWS Activity From IP Address - Rule", "ESCU - Get Notable History - Rule", "ESCU - Investigate AWS activities via region name - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - AWS S3 Bucket details via bucketName - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Investigate AWS activities via region name - Response Task"] description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ -Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ +Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ Among things to look out for are S3 access from unfamiliar locations and by unfamiliar users. Some of the searches in this Analytic Story help you detect suspicious behavior and others help you investigate more deeply, when the situation warrants. [analytic_story://Suspicious AWS Traffic] @@ -882,7 +882,7 @@ spec_version = 3 searches = ["ESCU - AWS Investigate User Activities By ARN - Rule", "ESCU - AWS Network ACL Details from ID - Rule", "ESCU - AWS Network Interface details via resourceId - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get All AWS Activity From IP Address - Rule", "ESCU - Get DNS Server History for a host - Rule", "ESCU - Get DNS traffic ratio - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Process Info - Rule", "ESCU - Get Process Information For Port Activity - Rule", "ESCU - Get Process Responsible For The DNS Traffic - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - AWS Network ACL Details from ID - Response Task", "ESCU - AWS Network Interface details via resourceId - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task"] description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). narrative = A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ -Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ +Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ Attackers may abuse the AWS infrastructure with insecure VPCs so they can co-opt AWS resources for command-and-control nodes, data exfiltration, and more. Once an EC2 instance is compromised, an attacker may initiate outbound network connections for malicious reasons. Monitoring these network traffic behaviors is crucial for understanding the type of traffic flowing in and out of your network and to alert you to suspicious activities.\ The searches in this Analytic Story will monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors. @@ -919,7 +919,7 @@ spec_version = 3 searches = ["ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule", "ESCU - Cloud Provisioning Activity From Previously Unseen Country - Rule", "ESCU - Cloud Provisioning Activity From Previously Unseen IP Address - Rule", "ESCU - Cloud Provisioning Activity From Previously Unseen Region - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Notable History - Response Task"] description = Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment. narrative = Because most enterprise cloud infrastructure activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to add specific IPs to an allow list because they vary.\ -This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. +This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. [analytic_story://Suspicious Cloud User Activities] category = Cloud Security @@ -930,7 +930,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 searches = ["ESCU - AWS IAM AccessDenied Discovery Events - Rule", "ESCU - AWS Investigate User Activities By ARN - Rule", "ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule", "ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule", "ESCU - Cloud API Calls From Previously Unseen User Roles - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task"] description = Detect and investigate suspicious activities by users and roles in your cloud environments. -narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ +narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new instances and increased bandwidth usage. [analytic_story://Suspicious Command-Line Executions] @@ -941,7 +941,7 @@ references = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.micr maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] -description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. +description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. narrative = The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. [analytic_story://Suspicious Compiled HTML Activity] @@ -967,7 +967,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Exfiltration Using Nslookup App - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Excessive Usage of NSLOOKUP App - Rule", "ESCU - Get DNS Server History for a host - Rule", "ESCU - Get DNS traffic ratio - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Get Process Responsible For The DNS Traffic - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task"] description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. -narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. +narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. [analytic_story://Suspicious Emails] category = Adversary Tactics @@ -1051,7 +1051,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}] spec_version = 3 searches = ["ESCU - Detect Regsvr32 Application Control Bypass - Rule", "ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule"] description = Monitor and detect techniques used by attackers who leverage the regsvr32.exe process to execute malicious code. -narrative = One common adversary tactic is to bypass application control solutions via the regsvr32.exe process. This particular bypass was popularized with "SquiblyDoo" using the "scrobj.dll" dll to load .sct scriptlets. This technique is still widely used by adversaries to bypass detection and prevention controls. The file extension of the DLL is irrelevant (it may load a .txt file extension for example). The searches in this story help you detect and investigate suspicious activity that may indicate that an adversary is leveraging regsvr32.exe to execute malicious code. Validate execution Determine if regsvr32.exe executed. Validate the OriginalFileName of regsvr32.exe and further PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64, it should be highly suspect. Determine if script code was executed with regsvr32. Situational Awareness - The objective of this step is meant to identify suspicious behavioral indicators related to executed of Script code by regsvr32.exe. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application? Module loads. Is regsvr32 loading any suspicious .DLLs? Unsigned or signed from non-standard paths. Network connections. Any network connections? Review the reputation of the remote IP or domain. Retrieval of Script Code - confirm the executed script code is benign or malicious. +narrative = One common adversary tactic is to bypass application control solutions via the regsvr32.exe process. This particular bypass was popularized with "SquiblyDoo" using the "scrobj.dll" dll to load .sct scriptlets. This technique is still widely used by adversaries to bypass detection and prevention controls. The file extension of the DLL is irrelevant (it may load a .txt file extension for example). The searches in this story help you detect and investigate suspicious activity that may indicate that an adversary is leveraging regsvr32.exe to execute malicious code. Validate execution Determine if regsvr32.exe executed. Validate the OriginalFileName of regsvr32.exe and further PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64, it should be highly suspect. Determine if script code was executed with regsvr32. Situational Awareness - The objective of this step is meant to identify suspicious behavioral indicators related to executed of Script code by regsvr32.exe. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application? Module loads. Is regsvr32 loading any suspicious .DLLs? Unsigned or signed from non-standard paths. Network connections. Any network connections? Review the reputation of the remote IP or domain. Retrieval of Script Code - confirm the executed script code is benign or malicious. [analytic_story://Suspicious Rundll32 Activity] category = Adversary Tactics @@ -1085,7 +1085,7 @@ spec_version = 3 searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. narrative = Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ - The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ + The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. [analytic_story://Suspicious Zoom Child Processes] @@ -1156,7 +1156,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 searches = ["ESCU - Attacker Tools On Endpoint - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - WinRM Spawning a Process - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. -narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ +narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ In the event an unusual process is identified, it is imperative to better understand how that process was able to execute on the host, when it first executed, and whether other hosts are affected. This extra information may provide clues that can help the analyst further investigate any suspicious activity. @@ -1180,7 +1180,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "Shannon Davis"}] spec_version = 3 searches = ["ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule", "ESCU - Detect Windows DNS SIGRed via Zeek - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Notable History - Response Task"] description = Uncover activity consistent with CVE-2020-1350, or SIGRed. Discovered by Checkpoint researchers, this vulnerability affects Windows 2003 to 2019, and is triggered by a malicious DNS response (only affects DNS over TCP). An attacker can use the malicious payload to cause a buffer overflow on the vulnerable system, leading to compromise. The included searches in this Analytic Story are designed to identify the large response payload for SIG and KEY DNS records which can be used for the exploit. -narrative = When a client requests a DNS record for a particular domain, that request gets routed first through the client's locally configured DNS server, then to any DNS server(s) configured as forwarders, and then onto the target domain's own DNS server(s). If a attacker wanted to, they could host a malicious DNS server that responds to the initial request with a specially crafted large response (~65KB). This response would flow through to the client's local DNS server, which if not patched for CVE-2020-1350, would cause the buffer overflow. The detection searches in this Analytic Story use wire data to detect the malicious behavior. Searches for Splunk Stream and Zeek are included. The Splunk Stream search correlates across stream:dns and stream:tcp, while the Zeek search correlates across bro:dns:json and bro:conn:json. These correlations are required to pick up both the DNS record types (SIG and KEY) along with the payload size (>65KB). +narrative = When a client requests a DNS record for a particular domain, that request gets routed first through the client's locally configured DNS server, then to any DNS server(s) configured as forwarders, and then onto the target domain's own DNS server(s). If a attacker wanted to, they could host a malicious DNS server that responds to the initial request with a specially crafted large response (~65KB). This response would flow through to the client's local DNS server, which if not patched for CVE-2020-1350, would cause the buffer overflow. The detection searches in this Analytic Story use wire data to detect the malicious behavior. Searches for Splunk Stream and Zeek are included. The Splunk Stream search correlates across stream:dns and stream:tcp, while the Zeek search correlates across bro:dns:json and bro:conn:json. These correlations are required to pick up both the DNS record types (SIG and KEY) along with the payload size (>65KB). [analytic_story://Windows Defense Evasion Tactics] category = Adversary Tactics @@ -1191,7 +1191,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 searches = ["ESCU - Disable Registry Tool - Rule", "ESCU - Disable Show Hidden Files - Rule", "ESCU - Disable Windows Behavior Monitoring - Rule", "ESCU - Disable Windows SmartScreen Protection - Rule", "ESCU - Disabling CMD Application - Rule", "ESCU - Disabling ControlPanel - Rule", "ESCU - Disabling Firewall with Netsh - Rule", "ESCU - Disabling FolderOptions Windows Feature - Rule", "ESCU - Disabling NoRun Windows App - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Disabling SystemRestore In Registry - Rule", "ESCU - Disabling Task Manager - Rule", "ESCU - Eventvwr UAC Bypass - Rule", "ESCU - Excessive number of service control start as disabled - Rule", "ESCU - FodHelper UAC Bypass - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - NET Profiler UAC bypass - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - SLUI RunAs Elevated - Rule", "ESCU - SLUI Spawning a Process - Rule", "ESCU - Sdclt UAC Bypass - Rule", "ESCU - SilentCleanup UAC Bypass - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - UAC Bypass MMC Load Unsigned Dll - Rule", "ESCU - WSReset UAC Bypass - Rule", "ESCU - Windows DisableAntiSpyware Registry - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others -narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. +narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. [analytic_story://Windows File Extension and Association Abuse] category = Malware @@ -1204,8 +1204,8 @@ searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESC description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. narrative = Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ - Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ -Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ + Attackers take advantage of this expectation by obfuscating the true file extension. They can accomplish this in a couple of ways. One technique involves inserting multiple spaces in the file name before the extension to hide the extension from the GUI, obscuring the true nature of the file. Another approach involves prepending the real extension with a different one. This is especially effective when Windows is configured to "hide extensions for known file types." In this case, the real extension is not displayed, but the prepended one is, leading end users to believe the file is a different type than it actually is.\ +Changing the association between a file extension and an application can allow an attacker to execute arbitrary code. The technique typically involves changing the association for an often-launched file type to associate instead with a malicious program the attacker has dropped on the endpoint. When the end user launches a file that has been manipulated in this way, it will execute the attacker's malware. It will also execute the application the end user expected to run, cleverly obscuring the fact that something suspicious has occurred.\ Run the searches in this story to detect and investigate suspicious behavior that may indicate abuse or manipulation of Windows file extensions and/or associations. [analytic_story://Windows Log Manipulation] @@ -1240,7 +1240,7 @@ maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 searches = ["ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Get Notable History - Rule", "ESCU - Get Parent Process Info - Rule", "ESCU - Get Process Info - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task"] description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. -narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. +narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. [analytic_story://Windows Service Abuse] category = Malware @@ -1282,10 +1282,10 @@ providing_technologies = [] type = detection asset_type = AWS Instance confidence = medium -explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -1293,10 +1293,10 @@ providing_technologies = [] type = detection asset_type = AWS Instance confidence = medium -explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -1304,10 +1304,10 @@ providing_technologies = [] type = detection asset_type = AWS Instance confidence = medium -explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -1315,10 +1315,10 @@ providing_technologies = [] type = detection asset_type = AWS Instance confidence = medium -explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." This search is deprecated and have been translated to use the latest Change Datamodel. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1535"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -1359,7 +1359,7 @@ confidence = medium explanation = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} -known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. +known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. providing_technologies = [] [savedsearch://ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule] @@ -1509,7 +1509,7 @@ confidence = medium explanation = The search looks for AWS CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your AWS CloudTrail inputs. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. +known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. providing_technologies = [] [savedsearch://ESCU - AWS Network Access Control List Deleted - Rule] @@ -1519,7 +1519,7 @@ confidence = medium explanation = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the AWS CloudTrail logs to detect users deleting network ACLs. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that a user has legitimately deleted a network ACL. +known_false_positives = It's possible that a user has legitimately deleted a network ACL. providing_technologies = [] [savedsearch://ESCU - AWS SAML Access by Provider User and Principal - Rule] @@ -1716,7 +1716,7 @@ providing_technologies = [] type = detection asset_type = Amazon EKS Kubernetes cluster Pod confidence = medium -explanation = This search provides detection information on unauthenticated requests against Kubernetes' Pods API +explanation = This search provides detection information on unauthenticated requests against Kubernetes' Pods API how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. @@ -1777,7 +1777,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = Attempt To Add Certificate To Untrusted Store -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1553.004"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. providing_technologies = [] @@ -1797,7 +1797,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.002"], "nist": ["DE.CM"]} known_false_positives = None identified. providing_technologies = [] @@ -1826,7 +1826,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. +explanation = The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197", "T1105"]} known_false_positives = Limited false positives, however it may be required to filter based on parent process name or network connection. @@ -1839,7 +1839,7 @@ confidence = medium explanation = The search looks for a batch file (.bat) written to the Windows system directory tree. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1204.002"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. +known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. providing_technologies = [] [savedsearch://ESCU - CHCP Command Execution - Rule] @@ -1856,7 +1856,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. +explanation = This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1059.003", "T1543.003"]} known_false_positives = Unknown. It is possible filtering may be required to ensure fidelity. @@ -1886,7 +1886,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. +explanation = Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\`. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]} known_false_positives = Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection. @@ -1917,7 +1917,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. providing_technologies = [] @@ -1938,17 +1938,17 @@ asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ -This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ +This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} -known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. +known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. providing_technologies = [] [savedsearch://ESCU - Clop Common Exec Parameter - Rule] type = detection asset_type = confidence = medium -explanation = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. +explanation = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1204"]} known_false_positives = Operators can execute third party tools using these parameters. @@ -1979,9 +1979,9 @@ type = detection asset_type = Cloud Compute Instance confidence = medium explanation = This search looks for cloud compute instances created by users who have not created them before. -how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. +how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} -known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. +known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule] @@ -1991,7 +1991,7 @@ confidence = medium explanation = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro. annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. +known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule] @@ -2019,19 +2019,19 @@ type = detection asset_type = AWS Instance confidence = medium explanation = This search looks for cloud instances being modified by users who have not previously modified them. -how_to_implement = This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. +how_to_implement = This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} -known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. +known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. providing_technologies = [] [savedsearch://ESCU - Cloud Network Access Control List Deleted - Rule] type = detection asset_type = Instance confidence = medium -explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate +explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that a user has legitimately deleted a network ACL. +known_false_positives = It's possible that a user has legitimately deleted a network ACL. providing_technologies = [] [savedsearch://ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule] @@ -2041,7 +2041,7 @@ confidence = medium explanation = This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something. how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -2052,7 +2052,7 @@ confidence = medium explanation = This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something. how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -2063,7 +2063,7 @@ confidence = medium explanation = This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something. how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -2074,7 +2074,7 @@ confidence = medium explanation = This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something. how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = [] @@ -2095,7 +2095,7 @@ asset_type = Endpoint confidence = medium explanation = The search looks for file modifications with extensions commonly used by Ransomware how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ -This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ 1. \ 1. **Label:** File Extension, **Field:** file_extension\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` @@ -2110,7 +2110,7 @@ confidence = medium explanation = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. +known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. providing_technologies = [] [savedsearch://ESCU - Conti Common Exec parameter - Rule] @@ -2147,7 +2147,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. +explanation = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows. how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. annotations = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1569.002"]} known_false_positives = unknown @@ -2158,7 +2158,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for the creation of local administrator accounts using net.exe. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.001"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create admin accounts. providing_technologies = [] @@ -2168,7 +2168,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for the creation or deletion of hidden shares using net.exe. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.005"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. providing_technologies = [] @@ -2178,7 +2178,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} known_false_positives = Legitimate administrator usage of Vssadmin or Wmic will create false positives. providing_technologies = [] @@ -2208,7 +2208,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search detects credential dumping using copy command from a shadow copy. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} known_false_positives = unknown providing_technologies = [] @@ -2218,7 +2218,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search detects the creation of a symlink to a shadow copy. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} known_false_positives = unknown providing_technologies = [] @@ -2248,8 +2248,8 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. -how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ 1. **Label:** DNS Query Length, **Field:** query_length\ 1. \ @@ -2266,7 +2266,7 @@ confidence = medium explanation = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} -known_false_positives = It's possible there can be long domain names that are legitimate. +known_false_positives = It's possible there can be long domain names that are legitimate. providing_technologies = [] [savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] @@ -2284,9 +2284,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. -how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ +how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} @@ -2297,9 +2297,9 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ +explanation = The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\ Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\ -The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ +The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "dsquery.exe" and its parent process.\ DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\ The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\ In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used. @@ -2333,7 +2333,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. providing_technologies = [] @@ -2344,7 +2344,7 @@ asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ -This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ 1. **Label:** AWS User ARN, **Field:** userIdentity.arn\ 1. \ @@ -2358,8 +2358,8 @@ providing_technologies = [] type = detection asset_type = Infrastructure confidence = medium -explanation = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. -how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +explanation = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure. +how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely). providing_technologies = [] @@ -2369,15 +2369,15 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for successful AWS CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ -This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ +This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ 1. **Label:** First Time, **Field:** firstTime\ 1. \ 1. **Label:** Last Time, **Field:** lastTime\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} -known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. +known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by New User - Rule] @@ -2444,7 +2444,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. +explanation = The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip. how_to_implement = To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"]} known_false_positives = False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed. @@ -2509,7 +2509,7 @@ confidence = medium explanation = This search looks for reading lsass memory consistent with credential dumping. how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} -known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. +known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. providing_technologies = [] [savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule] @@ -2518,8 +2518,8 @@ asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. how_to_implement = You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1566.003"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} @@ -2544,12 +2544,12 @@ asset_type = Windows confidence = medium explanation = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. how_to_implement = You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ - **Splunk>Phantom Playbook Integration**\ -If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ + **Splunk>Phantom Playbook Integration**\ +If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.002"], "nist": ["PR.IP"]} -known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. +known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. providing_technologies = [] [savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule] @@ -2596,7 +2596,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +explanation = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed. @@ -2606,7 +2606,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed. @@ -2616,7 +2616,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed. @@ -2626,7 +2626,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. +explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed. @@ -2636,7 +2636,7 @@ providing_technologies = [] type = detection asset_type = Infrastructure confidence = medium -explanation = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. +explanation = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure. how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None currently known @@ -2647,7 +2647,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. -how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model +how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]} known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list. providing_technologies = [] @@ -2659,14 +2659,14 @@ confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses. how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} -known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. +known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. providing_technologies = [] [savedsearch://ESCU - Detect MSHTA Url in Command Line - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +explanation = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible legitimate applications may perform this behavior and will need to be filtered. @@ -2687,9 +2687,9 @@ type = detection asset_type = Windows confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective. -how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. +how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} -known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. +known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. providing_technologies = [] [savedsearch://ESCU - Detect Mimikatz With PowerShell Script Block Logging - Rule] @@ -2711,7 +2711,7 @@ confidence = medium explanation = This search looks for newly created accounts that have been elevated to local administrators. how_to_implement = You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732 annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1136.001"], "nist": ["PR.AC", "DE.CM"]} -known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives +known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives providing_technologies = [] [savedsearch://ESCU - Detect New Login Attempts to Routers - Rule] @@ -2719,7 +2719,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. -how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. +how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate router connections may appear as new connections providing_technologies = [] @@ -2731,7 +2731,7 @@ confidence = medium explanation = This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket. how_to_implement = This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -known_false_positives = While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. +known_false_positives = While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group. providing_technologies = [] [savedsearch://ESCU - Detect New Open S3 Buckets over AWS CLI - Rule] @@ -2741,7 +2741,7 @@ confidence = medium explanation = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket over the aws cli. how_to_implement = annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. +known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. providing_technologies = [] [savedsearch://ESCU - Detect New Open S3 buckets - Rule] @@ -2751,7 +2751,7 @@ confidence = medium explanation = This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket. how_to_implement = You must install the AWS App for Splunk. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. +known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. providing_technologies = [] [savedsearch://ESCU - Detect Outbound SMB Traffic - Rule] @@ -2761,7 +2761,7 @@ confidence = medium explanation = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1071.002"], "nist": ["DE.CM"]} -known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. +known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. providing_technologies = [] [savedsearch://ESCU - Detect Outlook exe writing a zip file - Rule] @@ -2779,7 +2779,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1574.009"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = unknown providing_technologies = [] @@ -2789,7 +2789,7 @@ type = detection asset_type = Infrastructure confidence = medium explanation = By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs. -how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Exploitation", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network. providing_technologies = [] @@ -2809,9 +2809,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine +known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine providing_technologies = [] [savedsearch://ESCU - Detect RClone Command-Line Usage - Rule] @@ -2898,11 +2898,11 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ -Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. +explanation = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \ +Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]} -known_false_positives = Limited false positives related to third party software registering .DLL's. +known_false_positives = Limited false positives related to third party software registering .DLL's. providing_technologies = [] [savedsearch://ESCU - Detect Renamed 7-Zip - Rule] @@ -2949,8 +2949,8 @@ providing_technologies = [] type = detection asset_type = Infrastructure confidence = medium -explanation = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). -how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. +explanation = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack). +how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices. annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface. providing_technologies = [] @@ -2959,7 +2959,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +explanation = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive. @@ -2969,7 +2969,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +explanation = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may use setupapi triggering a false positive. @@ -2979,7 +2979,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. +explanation = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive. @@ -2989,7 +2989,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. +explanation = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. @@ -3000,7 +3000,7 @@ type = detection asset_type = S3 Bucket confidence = medium explanation = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour providing_technologies = [] @@ -3029,7 +3029,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. +explanation = SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell. how_to_implement = To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1087.002", "T1087.001", "T1482", "T1069.002", "T1069.001"]} known_false_positives = False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed. @@ -3050,7 +3050,7 @@ type = detection asset_type = Infrastructure confidence = medium explanation = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images. -how_to_implement = This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. +how_to_implement = This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory. annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1542.005"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices. providing_technologies = [] @@ -3061,7 +3061,7 @@ asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ -This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ +This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ 1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\ 1. \ @@ -3096,7 +3096,7 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. providing_technologies = [] @@ -3106,7 +3106,7 @@ type = detection asset_type = S3 Bucket confidence = medium explanation = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. providing_technologies = [] @@ -3116,7 +3116,7 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. providing_technologies = [] @@ -3126,7 +3126,7 @@ type = detection asset_type = AWS Instance confidence = medium explanation = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. providing_technologies = [] @@ -3136,7 +3136,7 @@ type = detection asset_type = Infrastructure confidence = medium explanation = Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device. -how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. +how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring. annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1020.001"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search will return false positives for any legitimate traffic captures by network administrators. providing_technologies = [] @@ -3155,7 +3155,7 @@ providing_technologies = [] type = detection asset_type = Infrastructure confidence = medium -explanation = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. +explanation = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. @@ -3183,7 +3183,7 @@ All event subscriptions have three components \ Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription. how_to_implement = To successfully implement this search, you need to be ingesting logs with that provide WMI Event Subscription from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID 19, 20 and 21. Tune and filter known good to limit the volume. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1546.003"]} -known_false_positives = It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage. +known_false_positives = It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage. providing_technologies = [] [savedsearch://ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule] @@ -3191,7 +3191,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search detects SIGRed via Splunk Stream. -how_to_implement = You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. +how_to_implement = You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment. annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]} known_false_positives = unknown providing_technologies = [] @@ -3223,7 +3223,7 @@ confidence = medium explanation = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]} -known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. +known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. providing_technologies = [] [savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule] @@ -3231,8 +3231,8 @@ type = detection asset_type = Endpoint confidence = medium explanation = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. -how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ -This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ +how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ +This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ 1. **Label:** DNS Answer, **Field:** answer\ 1. \ @@ -3256,7 +3256,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. +explanation = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. @@ -3277,7 +3277,7 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in AWS CloudTrail" support search once to create a history of previously seen user roles. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in AWS CloudTrail" support search once to create a history of previously seen user roles. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. providing_technologies = [] @@ -3287,7 +3287,7 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen users in AWS CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in AWS CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen users in AWS CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in AWS CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. providing_technologies = [] @@ -3318,7 +3318,7 @@ asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for web connections to dynamic DNS providers. how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ -This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ +This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate. annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1071.001"], "nist": ["PR.IP", "DE.DP"]} known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. @@ -3331,7 +3331,7 @@ confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same. how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "PR.DS"]} -known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. +known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. providing_technologies = [] [savedsearch://ESCU - Detection of tools built by NirSoft - Rule] @@ -3339,7 +3339,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072"], "nist": ["PR.IP"]} known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. providing_technologies = [] @@ -3528,7 +3528,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. +explanation = This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1204.002"]} known_false_positives = unknown @@ -3539,7 +3539,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = Detect the usage of comsvcs.dll for dumping the lsass process. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]} known_false_positives = None identified. providing_technologies = [] @@ -3571,9 +3571,9 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} -known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. +known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule] @@ -3581,9 +3581,9 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for AWS CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel. annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. +known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] @@ -3591,7 +3591,7 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. providing_technologies = [] @@ -3601,7 +3601,7 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. providing_technologies = [] @@ -3611,19 +3611,19 @@ type = detection asset_type = AWS Instance confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]} -known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. +known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. providing_technologies = [] [savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. -how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ +explanation = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. +how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ **Splunk Phantom Playbook Integration**\ -If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. +If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time providing_technologies = [] @@ -3643,7 +3643,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. -how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. +how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. providing_technologies = [] @@ -3662,12 +3662,22 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. +explanation = This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device. how_to_implement = To successfully implement this search, you need to be ingesting logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and filter known instances of process like logonUI used in your environment. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1087"]} known_false_positives = unknown providing_technologies = [] +[savedsearch://ESCU - Esentutl SAM Copy - Rule] +type = detection +asset_type = +confidence = medium +explanation = The following analytic identifies the process - `esentutl.exe` - being used to capture credentials stored in ntds.dit or the SAM file on disk. During triage, review parallel processes and determine if legitimate activity. Upon determination of illegitimate activity, take further action to isolate and contain the threat. +how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. +annotations = {"kill_chain_phases": ["Privilege Escalation", "Lateral Movement"], "mitre_attack": ["T1003.002"]} +known_false_positives = False positives should be limited. Filter as needed. +providing_technologies = [] + [savedsearch://ESCU - Eventvwr UAC Bypass - Rule] type = detection asset_type = @@ -3783,7 +3793,7 @@ type = detection asset_type = confidence = medium explanation = This analytic will identify suspicious series of process executions. We have observed that post exploit framework tools like Koadic and Meterpreter will launch an excessive number of processes with distinct file paths from Windows\Temp to execute actions on objective. This behavior is extremely anomalous compared to typical application behaviors that use Windows\Temp. -how_to_implement = To successfully implement this search, you need to be ingesting logs with the full process path in the process field of CIM's Process data model. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used. +how_to_implement = To successfully implement this search, you need to be ingesting logs with the full process path in the process field of CIM's Process data model. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"]} known_false_positives = Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold. Filter as needed. providing_technologies = [] @@ -3793,7 +3803,7 @@ type = detection asset_type = confidence = medium explanation = This detection targets behaviors observed when threat actors have used sc.exe to modify services. We observed malware in a honey pot spawning numerous sc.exe processes in a short period of time, presumably to impair defenses, possibly to block others from compromising the same machine. This detection will alert when we see both an excessive number of sc.exe processes launched with specific commandline arguments to disable the start of certain services. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562.001"]} known_false_positives = Legitimate programs and administrators will execute sc.exe with the start disabled flag. It is possible, but unlikely from the telemetry of normal Windows operation we observed, that sc.exe will be called more than seven times in a short period of time. providing_technologies = [] @@ -3842,7 +3852,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. +explanation = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. @@ -3852,7 +3862,7 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. +explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring. how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified @@ -3885,7 +3895,7 @@ confidence = medium explanation = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. providing_technologies = [] [savedsearch://ESCU - First Time Seen Running Windows Service - Rule] @@ -3903,7 +3913,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"&#151;which creates a lookup file called `previously_seen_cmd_line_arguments.csv`&#151;a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001", "T1059.003"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name providing_technologies = [] @@ -3976,7 +3986,7 @@ providing_technologies = [] type = detection asset_type = GCP Kubernetes cluster confidence = medium -explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods +explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context. @@ -4012,6 +4022,16 @@ annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.00 known_false_positives = network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack. providing_technologies = [] +[savedsearch://ESCU - Github Commit Changes In Master - Rule] +type = detection +asset_type = +confidence = medium +explanation = This search is to detect a pushed or commit to master or main branch. This is to avoid unwanted modification to master without a review to the changes. Ideally in terms of devsecops the changes made in a branch and do a PR for review. of course in some cases admin of the project may did a changes directly to master branch +how_to_implement = To successfully implement this search, you need to be ingesting logs related to github logs having the fork, commit, push metadata that can be use to monitor the changes in a github project. +annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1199"]} +known_false_positives = admin can do changes directly to master branch +providing_technologies = [] + [savedsearch://ESCU - Gsuite Drive Share In External Email - Rule] type = detection asset_type = @@ -4022,6 +4042,26 @@ annotations = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1567.00 known_false_positives = network admin or normal user may share files to customer and external team. providing_technologies = [] +[savedsearch://ESCU - Gsuite Email Suspicious Subject With Attachment - Rule] +type = detection +asset_type = +confidence = medium +explanation = This search is to detect a gsuite email contains suspicious subject having known file type used in spear phishing. This technique is a common and effective entry vector of attacker to compromise a network by luring the user to click or execute the suspicious attachment send from external email account because of the effective social engineering of subject related to delivery, bank and so on. On the other hand this detection may catch a normal email traffic related to legitimate transaction so better to check the email sender, spelling and etc. avoid click link or opening the attachment if you are not expecting this type of e-mail. +how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} +known_false_positives = normal user or normal transaction may contain the subject and file type attachment that this detection try to search. +providing_technologies = [] + +[savedsearch://ESCU - Gsuite Email With Known Abuse Web Service Link - Rule] +type = detection +asset_type = +confidence = medium +explanation = This analytics is to detect a gmail containing a link that are known to be abused by malware or attacker like pastebin, telegram and discord to deliver malicious payload. This event can encounter some normal email traffic within organization and external email that normally using this application and services. +how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} +known_false_positives = normal email contains this link that are known application within the organization or network can be catched by this detection. +providing_technologies = [] + [savedsearch://ESCU - Gsuite Outbound Email With Attachment To External Domain - Rule] type = detection asset_type = @@ -4032,6 +4072,16 @@ annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1048.00 known_false_positives = network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack. providing_technologies = [] +[savedsearch://ESCU - Gsuite Suspicious Shared File Name - Rule] +type = detection +asset_type = +confidence = medium +explanation = This search is to detect a shared file in google drive with suspicious file name that are commonly used by spear phishing campaign. This technique is very popular to lure the user by running a malicious document or click a malicious link within the shared file that will redirected to malicious website. This detection can also catch some normal email communication between organization and its external customer. +how_to_implement = To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. +annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} +known_false_positives = normal user or normal transaction may contain the subject and file type attachment that this detection try to search +providing_technologies = [] + [savedsearch://ESCU - Hide User Account From Sign-In Screen - Rule] type = detection asset_type = @@ -4047,7 +4097,7 @@ type = detection asset_type = confidence = medium explanation = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1222.001"], "nist": ["DE.CM"]} known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. providing_technologies = [] @@ -4087,7 +4137,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. -how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. +how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. providing_technologies = [] @@ -4342,6 +4392,36 @@ annotations = {"kill_chain_phases": ["Lateral Movement"]} known_false_positives = Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets providing_technologies = [] +[savedsearch://ESCU - Kubernetes Nginx Ingress LFI - Rule] +type = detection +asset_type = Kubernetes +confidence = medium +explanation = This search uses the Kubernetes logs from a nginx ingress controller to detect local file inclusion attacks. +how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes. +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +known_false_positives = unknown +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Nginx Ingress RFI - Rule] +type = detection +asset_type = Kubernetes +confidence = medium +explanation = This search uses the Kubernetes logs from a nginx ingress controller to detect remote file inclusion attacks. +how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes. +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1212"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +known_false_positives = unknown +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Scanner Image Pulling - Rule] +type = detection +asset_type = Kubernetes +confidence = medium +explanation = This search uses the Kubernetes logs from Splunk Connect from Kubernetes to detect Kubernetes Security Scanner. +how_to_implement = You must ingest Kubernetes logs through Splunk Connect for Kubernetes. +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +known_false_positives = unknown +providing_technologies = [] + [savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule] type = detection asset_type = DNS Servers @@ -4376,10 +4456,10 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +explanation = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. +known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] @@ -4387,9 +4467,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = System administrators may use this option, but it's not common. +known_false_positives = System administrators may use this option, but it's not common. providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] @@ -4397,7 +4477,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. providing_technologies = [] @@ -4407,9 +4487,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. +known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] @@ -4417,7 +4497,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = These characters might be legitimately on the command-line, but it is not common. providing_technologies = [] @@ -4457,7 +4537,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. -how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]} known_false_positives = None at this time providing_technologies = [] @@ -4467,7 +4547,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. -how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time providing_technologies = [] @@ -4487,7 +4567,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. -how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time providing_technologies = [] @@ -4556,9 +4636,9 @@ explanation = The following analytic identifies one source endpoint failing to a The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\ This detection will only trigger on domain controllers, not on member servers or workstations.\ The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts. -how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled. +how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003"]} -known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. +known_false_positives = A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. providing_technologies = [] [savedsearch://ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule] @@ -4607,7 +4687,7 @@ This detection will only trigger on domain controllers, not on member servers or The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts. how_to_implement = To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1110.003"]} -known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. +known_false_positives = A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts. providing_technologies = [] [savedsearch://ESCU - Multiple Users Failing To Authenticate From Process - Rule] @@ -4681,7 +4761,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. -how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. +how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. annotations = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} known_false_positives = None identified providing_technologies = [] @@ -4691,9 +4771,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \ -ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ -This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \ +This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]} known_false_positives = Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives. providing_technologies = [] @@ -4832,7 +4912,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +explanation = this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1566.001"]} known_false_positives = unknown @@ -4998,6 +5078,16 @@ annotations = {"kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1048.00 known_false_positives = unknown providing_technologies = [] +[savedsearch://ESCU - PowerShell 4104 Hunting - Rule] +type = detection +asset_type = +confidence = medium +explanation = The following Hunting analytic assists with identifying suspicious PowerShell execution using Script Block Logging, or EventCode 4104. This analytic is not meant to be ran hourly, but occasionally to identify malicious or suspicious PowerShell. This analytic is a combination of work completed by Alex Teixeira and Splunk Threat Research Team. +how_to_implement = The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging. +annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"]} +known_false_positives = Limited false positives. May filter as needed. +providing_technologies = [] + [savedsearch://ESCU - PowerShell Domain Enumeration - Rule] type = detection asset_type = @@ -5026,7 +5116,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? +explanation = Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation? how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1197"]} known_false_positives = Limited false positives. It is possible administrators will utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent process or command-line arguments. @@ -5056,7 +5146,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. +explanation = This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system. how_to_implement = To successfully implement this search, you need to be ingesting logs with the powershell logs from your endpoints. make sure you enable needed registry to monitor this event. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1027.005"]} known_false_positives = network operator may enable or disable this windows feature. @@ -5143,7 +5233,7 @@ type = detection asset_type = confidence = medium explanation = The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ +Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began. how_to_implement = You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1547.012"]} @@ -5155,7 +5245,7 @@ type = detection asset_type = confidence = medium explanation = The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \ -Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ +Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \ The analytic is based on file path and failure to load the plug-in. \ During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events. how_to_implement = You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems. @@ -5178,7 +5268,7 @@ type = detection asset_type = confidence = medium explanation = This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"]} known_false_positives = unknown providing_technologies = [] @@ -5188,7 +5278,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = The following analytic identifies `WmiPrvSE.exe` spawning a process. This typically occurs when a process is instantiated from a local or remote process using `wmic.exe`. During triage, review parallel processes for suspicious behavior or commands executed. Review the process and command-line spawning from `wmiprvse.exe`. Contain and remediate the endpoint as necessary. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. providing_technologies = [] @@ -5220,7 +5310,7 @@ confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type. how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. +known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft. providing_technologies = [] [savedsearch://ESCU - Processes launching netsh - Rule] @@ -5237,8 +5327,8 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. -how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. +explanation = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. +how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified providing_technologies = [] @@ -5248,7 +5338,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as prohibited. -how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search <code>Add Prohibited Processes to Enterprise Security</code>. +how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified providing_technologies = [] @@ -5267,8 +5357,8 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. -how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. +explanation = The following analytic identifies cleartext protocols at risk of leaking sensitive information. Currently, this consists of legacy protocols such as telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21) sessions. While some of these protocols may be used over SSL, they typically are found on different assigned ports in those instances. +how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. For more accurate result it's better to limit destination to organization private and public IP range, like All_Traffic.dest IN(192.168.0.0/16,172.16.0.0/12,10.0.0.0/8, x.x.x.x/22) annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare. providing_technologies = [] @@ -5328,7 +5418,7 @@ type = detection asset_type = confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for command-line arguments used to hide a file or directory using the reg add command. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]} known_false_positives = None at the moment providing_technologies = [] @@ -5347,7 +5437,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. +explanation = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.012"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. @@ -5370,7 +5460,7 @@ confidence = medium explanation = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. +known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. providing_technologies = [] [savedsearch://ESCU - Remote Desktop Network Traffic - Rule] @@ -5378,7 +5468,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network. -how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. +how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. providing_technologies = [] @@ -5387,8 +5477,8 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. -how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. +explanation = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. +how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. providing_technologies = [] @@ -5398,9 +5488,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. +known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. providing_technologies = [] [savedsearch://ESCU - Remote Registry Key modifications - Rule] @@ -5408,9 +5498,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search monitors for remote modifications to registry keys. -how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. +how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. +known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. providing_technologies = [] [savedsearch://ESCU - Remote WMI Command Attempt - Rule] @@ -5418,7 +5508,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = The following analytic identifies usage of `wmic.exe` spawning a local or remote process, identified by the `node` switch. During triage, review parallel processes for additional commands executed. Look for any file modifications before and after `wmic.exe` execution. In addition, identify the remote endpoint and confirm execution or file modifications. Contain and isolate the endpoint as needed. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Administrators may use this legitimately to gather info from remote systems. Filter as needed. providing_technologies = [] @@ -5458,7 +5548,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process providing_technologies = [] @@ -5477,7 +5567,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. +explanation = This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process. how_to_implement = To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]} known_false_positives = unknown @@ -5487,7 +5577,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. +explanation = This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.011"]} known_false_positives = unknown @@ -5537,8 +5627,8 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. -how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." +explanation = The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords. +how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"]} known_false_positives = Natively, `dllhost.exe` will access the files. Every environment will have additional native processes that do as well. Filter by process_name. As an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`. providing_technologies = [] @@ -5578,8 +5668,8 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. -how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ -This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ +how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ +This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["DE.CM"]} @@ -5593,14 +5683,14 @@ confidence = medium explanation = This search looks for long URLs that have several SQL commands visible within them. how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1190"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} -known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. +known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. providing_technologies = [] [savedsearch://ESCU - Samsam Test File Write - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. +explanation = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = No false positives have been identified. @@ -5620,7 +5710,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +explanation = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' how_to_implement = To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"]} known_false_positives = unknown @@ -5630,7 +5720,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' +explanation = The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.' how_to_implement = To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1053"]} known_false_positives = unknown @@ -5641,7 +5731,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]} known_false_positives = Tasks should not be manually created via CLI, this is rarely done by admins as well providing_technologies = [] @@ -5651,7 +5741,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]} known_false_positives = No known false positives providing_technologies = [] @@ -5671,7 +5761,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. providing_technologies = [] @@ -5691,7 +5781,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for scripts launched via WMI. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. Filter as needed. providing_technologies = [] @@ -5740,10 +5830,10 @@ providing_technologies = [] type = detection asset_type = Endpoint confidence = medium -explanation = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. +explanation = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["DE.CM"]} -known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. +known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. providing_technologies = [] [savedsearch://ESCU - Shim Database File Creation - Rule] @@ -5761,7 +5851,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]} known_false_positives = None identified providing_technologies = [] @@ -5791,7 +5881,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for process names that consist only of a single letter. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. providing_technologies = [] @@ -5961,7 +6051,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). -how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. +how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566"], "nist": ["PR.IP"]} known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender. providing_technologies = [] @@ -5971,9 +6061,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for emails that have attachments with suspicious file extensions. -how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ +how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ **Splunk Phantom Playbook Integration**\ -If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. +If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566.001"], "nist": ["DE.AE", "PR.IP"]} known_false_positives = None identified providing_technologies = [] @@ -5995,7 +6085,7 @@ confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. The search looks for files created with names that have been linked to malicious activity. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. +known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. providing_technologies = [] [savedsearch://ESCU - Suspicious GPUpdate no Command Line Arguments - Rule] @@ -6012,7 +6102,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. +explanation = this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed regsvr32.exe may be used. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.010"]} known_false_positives = minimal. but network operator can use this application to load dll. @@ -6063,12 +6153,12 @@ type = detection asset_type = Endpoint confidence = medium explanation = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001"]} @@ -6080,12 +6170,12 @@ type = detection asset_type = Endpoint confidence = medium explanation = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\ -- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ -- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \ +- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \ Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further. how_to_implement = OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct. annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001"]} @@ -6107,9 +6197,9 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]} -known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. +known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. providing_technologies = [] [savedsearch://ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule] @@ -6117,7 +6207,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]} known_false_positives = Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues. providing_technologies = [] @@ -6229,14 +6319,14 @@ confidence = medium explanation = The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild. how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. +known_false_positives = Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on. providing_technologies = [] [savedsearch://ESCU - Suspicious mshta child process - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. +explanation = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. @@ -6257,7 +6347,7 @@ type = detection asset_type = confidence = medium explanation = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. providing_technologies = [] @@ -6266,7 +6356,7 @@ providing_technologies = [] type = detection asset_type = Windows confidence = medium -explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. +explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search detects writes to the 'System Volume Information' folder by something other than the System process. how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]} known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. @@ -6309,7 +6399,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. -how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. +how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.001"], "nist": ["DE.AE"]} known_false_positives = None at this time providing_technologies = [] @@ -6349,7 +6439,7 @@ type = detection asset_type = Endpoint confidence = medium explanation = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} known_false_positives = None identified providing_technologies = [] @@ -6359,7 +6449,7 @@ type = detection asset_type = confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search looks for applications on the endpoint that you have marked as uncommon. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified providing_technologies = [] @@ -6368,7 +6458,7 @@ providing_technologies = [] type = detection asset_type = confidence = medium -explanation = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. +explanation = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]} known_false_positives = Unknown. Tune out child processes as needed to limit volume of false positives. @@ -6389,7 +6479,7 @@ type = detection asset_type = confidence = medium explanation = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]} known_false_positives = providing_technologies = [] @@ -6399,7 +6489,7 @@ type = detection asset_type = confidence = medium explanation = The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \ -This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ +This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \ During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block. how_to_implement = To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell. annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1562"]} @@ -6441,7 +6531,7 @@ type = detection asset_type = confidence = medium explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. -how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. +how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. providing_technologies = [] @@ -6546,9 +6636,9 @@ type = detection asset_type = Account confidence = medium explanation = WARNING, this detection has been marked deprecated by the Splunk Threat Research team, this means that it will no longer be maintained or supported. If you have any questions feel free to email us at: research@splunk.com. This search is used to identify the creation of multiple user accounts using the same email domain name. -how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. +how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} -known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment&#151;improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. +known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. providing_technologies = [] [savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule] @@ -6576,7 +6666,7 @@ type = detection asset_type = Web Server confidence = medium explanation = This search looks for suspicious processes on all systems labeled as web servers. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. providing_technologies = [] @@ -7042,7 +7132,7 @@ latest_time_offset = 0 [savedsearch://ESCU - Get Parent Process Info - Response Task] type = investigation explanation = none -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 diff --git a/dist/escu/lookups/local_file_inclusion_paths.csv b/dist/escu/lookups/local_file_inclusion_paths.csv new file mode 100644 index 0000000000..f0a0de38a5 --- /dev/null +++ b/dist/escu/lookups/local_file_inclusion_paths.csv @@ -0,0 +1,1009 @@ +local_file_inclusion_paths, lfi_path +*/apache2/logs/access.log*, yes +*/apache2/logs/error.log*, yes +*/apache/conf/httpd.conf*, yes +*/apache/logs/access.log*, yes +*/apache/logs/error.log*, yes +*/apache/php/php.ini*, yes +*/apachephpphp.ini*, yes +*/bin/php.ini*, yes +*/boot/grub/grub.cfg*, yes +*/boot/grub/menu.lst*, yes +*/etc/adduser.conf*, yes +*/etc/alias*, yes +*/etc/apache22/conf/httpd.conf*, yes +*/etc/apache22/httpd.conf*, yes +*/etc/apache2/apache2.conf*, yes +*/etc/apache2/apache.conf*, yes +*/etc/apache2/conf/httpd.conf*, yes +*/etc/apache2/default-server.conf*, yes +*/etc/apache2/envvars*, yes +*/etc/apache2/httpd2.conf*, yes +*/etc/apache2/httpd.conf*, yes +*/etc/apache2/mods-available/autoindex.conf*, yes +*/etc/apache2/mods-available/deflate.conf*, yes +*/etc/apache2/mods-available/dir.conf*, yes +*/etc/apache2/mods-available/mem_cache.conf*, yes +*/etc/apache2/mods-available/mime.conf*, yes +*/etc/apache2/mods-available/proxy.conf*, yes +*/etc/apache2/mods-available/setenvif.conf*, yes +*/etc/apache2/mods-available/ssl.conf*, yes +*/etc/apache2/mods-enabled/alias.conf*, yes +*/etc/apache2/mods-enabled/deflate.conf*, yes +*/etc/apache2/mods-enabled/dir.conf*, yes +*/etc/apache2/mods-enabled/mime.conf*, yes +*/etc/apache2/mods-enabled/negotiation.conf*, yes +*/etc/apache2/mods-enabled/php5.conf*, yes +*/etc/apache2/mods-enabled/status.conf*, yes +*/etc/apache2/ports.conf*, yes +*/etc/apache2/sites-available/default*, yes +*/etc/apache2/sites-available/default-ssl*, yes +*/etc/apache2/sites-enabled/000-default*, yes +*/etc/apache2/sites-enabled/default*, yes +*/etc/apache2/ssl-global.conf*, yes +*/etc/apache/access.conf*, yes +*/etc/apache/apache.conf*, yes +*/etc/apache/conf/httpd.conf*, yes +*/etc/apache/default-server.conf*, yes +*/etc/apache/httpd.conf*, yes +*/etc/apt/apt.conf*, yes +*/etc/avahi/avahi-daemon.conf*, yes +*/etc/bash.bashrc*, yes +*/etc/bluetooth/input.conf*, yes +*/etc/bluetooth/main.conf*, yes +*/etc/bluetooth/network.conf*, yes +*/etc/bluetooth/rfcomm.conf*, yes +*/etc/ca-certificates.conf*, yes +*/etc/ca-certificates.conf.dpkg-old*, yes +*/etc/casper.conf*, yes +*/etc/chkrootkit.conf*, yes +*/etc/chrootUsers*, yes +*/etc/clamav/clamd.conf*, yes +*/etc/clamav/freshclam.conf*, yes +*/etc/crontab*, yes +*/etc/crypttab*, yes +*/etc/cups/acroread.conf*, yes +*/etc/cups/cupsd.conf*, yes +*/etc/cups/cupsd.conf.default*, yes +*/etc/cups/pdftops.conf*, yes +*/etc/cups/printers.conf*, yes +*/etc/cvs-cron.conf*, yes +*/etc/cvs-pserver.conf*, yes +*/etc/debconf.conf*, yes +*/etc/debian_version*, yes +*/etc/default/grub*, yes +*/etc/deluser.conf*, yes +*/etc/dhcp3/dhclient.conf*, yes +*/etc/dhcp3/dhcpd.conf*, yes +*/etc/dhcp/dhclient.conf*, yes +*/etc/dns2tcpd.conf*, yes +*/etc/e2fsck.conf*, yes +*/etc/esound/esd.conf*, yes +*/etc/etter.conf*, yes +*/etc/exports*, yes +*/etc/fedora-release*, yes +*/etc/firewall.rules*, yes +*/etc/foremost.conf*, yes +*/etc/fstab*, yes +*/etc/ftpchroot*, yes +*/etc/ftphosts*, yes +*/etc/ftpusers*, yes +*/etc/fuse.conf*, yes +*/etc/group*, yes +*/etc/group-*, yes +*/etc/hdparm.conf*, yes +*/etc/host.conf*, yes +*/etc/hostname*, yes +*/etc/hosts*, yes +*/etc/hosts.allow*, yes +*/etc/hosts.deny*, yes +*/etc/http/conf/httpd.conf*, yes +*/etc/httpd/apache2.conf*, yes +*/etc/httpd/apache.conf*, yes +*/etc/httpd.conf*, yes +*/etc/httpd/conf*, yes +*/etc/httpd/conf/apache2.conf*, yes +*/etc/httpd/conf/apache.conf*, yes +*/etc/httpd/conf.d*, yes +*/etc/httpd/conf/httpd.conf*, yes +*/etc/httpd/extra/httpd-ssl.conf*, yes +*/etc/httpd/httpd.conf*, yes +*/etc/httpd/logs/acces.log*, yes +*/etc/httpd/logs/acces_log*, yes +*/etc/httpd/logs/access.log*, yes +*/etc/httpd/logs/access_log*, yes +*/etc/httpd/logs/error.log*, yes +*/etc/httpd/logs/error_log*, yes +*/etc/httpd/mod_php.conf*, yes +*/etc/httpd/php.ini*, yes +*/etc/http/httpd.conf*, yes +*/etc/inetd.conf*, yes +*/etc/init.d*, yes +*/etc/inittab*, yes +*/etc/ipfw.conf*, yes +*/etc/ipfw.rules*, yes +*/etc/issue*, yes +*/etc/issue.net*, yes +*/etc/kbd/config*, yes +*/etc/kernel-img.conf*, yes +*/etc/kernel-pkg.conf*, yes +*/etc/ldap/ldap.conf*, yes +*/etc/ld.so.conf*, yes +*/etc/lighttpd/lighthttpd.conf*, yes +*/etc/login.defs*, yes +*/etc/logrotate.conf*, yes +*/etc/ltrace.conf*, yes +*/etc/mail/sendmail.conf*, yes +*/etc/mandrake-release*, yes +*/etc/manpath.config*, yes +*/etc/miredo.conf*, yes +*/etc/miredo/miredo.conf*, yes +*/etc/miredo/miredo-server.conf*, yes +*/etc/miredo-server.conf*, yes +*/etc/modules*, yes +*/etc/mono/config*, yes +*/etc/motd*, yes +*/etc/mtab*, yes +*/etc/mtools.conf*, yes +*/etc/muddleftpd.com*, yes +*/etc/muddleftpd/muddleftpd.conf*, yes +*/etc/muddleftpd/muddleftpd.passwd*, yes +*/etc/muddleftpd/mudlog*, yes +*/etc/muddleftpd/mudlogd.conf*, yes +*/etc/muddleftpd/passwd*, yes +*/etc/my.cnf*, yes +*/etc/mysql/my.cnf*, yes +*/etc/networks*, yes +*/etc/nginx/nginx.conf*, yes +*/etc/openldap/ldap.conf*, yes +*/etc/os-release*, yes +*/etc/osxhttpd/osxhttpd.conf*, yes +*/etc/pam.conf*, yes +*/etc/passwd*, yes +*/etc/passwd-*, yes +*/etc/passwd~*, yes +*/etc/password.master*, yes +*/etc/php4/apache2/php.ini*, yes +*/etc/php4/apache/php.ini*, yes +*/etc/php4/cgi/php.ini*, yes +*/etc/php5/apache2/php.ini*, yes +*/etc/php5/apache/php.ini*, yes +*/etc/php5/cgi/php.ini*, yes +*/etc/php/apache2/php.ini*, yes +*/etc/php/apache/php.ini*, yes +*/etc/php/cgi/php.ini*, yes +*/etc/php.ini*, yes +*/etc/phpmyadmin/config.inc.php*, yes +*/etc/php/php4/php.ini*, yes +*/etc/php/php.ini*, yes +*/etc/postgresql/pg_hba.conf*, yes +*/etc/postgresql/postgresql.conf*, yes +*/etc/profile*, yes +*/etc/proftp.conf*, yes +*/etc/proftpd/modules.conf*, yes +*/etc/protpd/proftpd.conf*, yes +*/etc/pulse/client.conf*, yes +*/etc/pure-ftpd.conf*, yes +*/etc/pureftpd.passwd*, yes +*/etc/pureftpd.pdb*, yes +*/etc/pure-ftpd/pure-ftpd.conf*, yes +*/etc/pure-ftpd/pure-ftpd.pdb*, yes +*/etc/pure-ftpd/pureftpd.pdb*, yes +*/etc/rc.conf*, yes +*/etc/redhat-release*, yes +*/etc/resolv.conf*, yes +*/etc/samba/dhcp.conf*, yes +*/etc/samba/netlogon*, yes +*/etc/samba/private/smbpasswd*, yes +*/etc/samba/samba.conf*, yes +*/etc/samba/smb.conf*, yes +*/etc/samba/smb.conf.user*, yes +*/etc/samba/smbpasswd*, yes +*/etc/samba/smbusers*, yes +*/etc/security/access.conf*, yes +*/etc/security/environ*, yes +*/etc/security/failedlogin*, yes +*/etc/security/group*, yes +*/etc/security/group.conf*, yes +*/etc/security/lastlog*, yes +*/etc/security/limits*, yes +*/etc/security/limits.conf*, yes +*/etc/security/namespace.conf*, yes +*/etc/security/opasswd*, yes +*/etc/security/pam_env.conf*, yes +*/etc/security/passwd*, yes +*/etc/security/passwd*, yes +*/etc/security/sepermit.conf*, yes +*/etc/security/time.conf*, yes +*/etc/security/user*, yes +*/etc/sensors3.conf*, yes +*/etc/sensors.conf*, yes +*/etc/shadow*, yes +*/etc/shadow-*, yes +*/etc/shadow~*, yes +*/etc/slackware-release*, yes +*/etc/smb.conf*, yes +*/etc/smbpasswd*, yes +*/etc/smi.conf*, yes +*/etc/squirrelmail/apache.conf*, yes +*/etc/squirrelmail/config/config.php*, yes +*/etc/squirrelmail/config_default.php*, yes +*/etc/squirrelmail/config_local.php*, yes +*/etc/squirrelmail/config.php*, yes +*/etc/squirrelmail/default_pref*, yes +*/etc/squirrelmail/filters_setup.php*, yes +*/etc/squirrelmail/index.php*, yes +*/etc/squirrelmail/sqspell_config.php*, yes +*/etc/ssh/sshd_config*, yes +*/etc/sso/sso_config.ini*, yes +*/etc/stunnel/stunnel.conf*, yes +*/etc/sudoers*, yes +*/etc/SUSE-release*, yes +*/etc/sysconfig/network-scripts/ifcfg-eth0*, yes +*/etc/sysctl.conf*, yes +*/etc/syslog.conf*, yes +*/etc/timezone*, yes +*/etc/tinyproxy/tinyproxy.conf*, yes +*/etc/tor/tor-tsocks.conf*, yes +*/etc/tsocks.conf*, yes +*/etc/updatedb.conf*, yes +*/etc/updatedb.conf.BeforeVMwareToolsInstall*, yes +*/etc/utmp*, yes +*/etc/vhcs2/proftpd/proftpd.conf*, yes +*/etc/vmware-tools/config*, yes +*/etc/vmware-tools/tpvmlp.conf*, yes +*/etc/vmware-tools/vmware-tools-libraries.conf*, yes +*/etc/vsftpd.chroot_list*, yes +*/etc/vsftpd.conf*, yes +*/etc/vsftpd/vsftpd.conf*, yes +*/etc/webmin/miniserv.conf*, yes +*/etc/webmin/miniserv.users*, yes +*/etc/wicd/dhclient.conf.template.default*, yes +*/etc/wicd/manager-settings.conf*, yes +*/etc/wicd/wired-settings.conf*, yes +*/etc/wicd/wireless-settings.conf*, yes +*/etc/wu-ftpd/ftpaccess*, yes +*/etc/wu-ftpd/ftphosts*, yes +*/etc/wu-ftpd/ftpusers*, yes +*/etc/X11/xorg.conf*, yes +*/etc/X11/xorg.conf.BeforeVMwareToolsInstall*, yes +*/etc/X11/xorg.conf.orig*, yes +*/etc/X11/xorg.conf-vesa*, yes +*/etc/X11/xorg.conf-vmware*, yes +*/home2/bin/stable/apache/php.ini*, yes +*/home2binstableapachephp.ini*, yes +*/home/bin/stable/apache/php.ini*, yes +*/homebinstableapachephp.ini*, yes +*/home/postgres/data/pg_hba.conf*, yes +*/home/postgres/data/pg_ident.conf*, yes +*/home/postgres/data/PG_VERSION*, yes +*/home/postgres/data/postgresql.conf*, yes +*/home/user/lighttpd/lighttpd.conf*, yes +*/http/httpd.conf*, yes +*/[JBOSS]/server/default/conf/jboss-minimal.xml*, yes +*/[JBOSS]/server/default/conf/jboss-service.xml*, yes +*/[JBOSS]/server/default/conf/jndi.properties*, yes +*/[JBOSS]/server/default/conf/log4j.xml*, yes +*/[JBOSS]/server/default/conf/login-config.xml*, yes +*/[JBOSS]/server/default/conf/server.log.properties*, yes +*/[JBOSS]/server/default/conf/standardjaws.xml*, yes +*/[JBOSS]/server/default/conf/standardjboss.xml*, yes +*/[JBOSS]/server/default/deploy/jboss-logging.xml*, yes +*/[JBOSS]/server/default/log/boot.log*, yes +*/[JBOSS]/server/default/log/server.log*, yes +*/Library/WebServer/Documents/default.htm*, yes +*/Library/WebServer/Documents/default.html*, yes +*/Library/WebServer/Documents/default.php*, yes +*/Library/WebServer/Documents/.htaccess*, yes +*/Library/WebServer/Documents/index.htm*, yes +*/Library/WebServer/Documents/index.html*, yes +*/Library/WebServer/Documents/index.php*, yes +*/logs/access.log*, yes +*/logs/access_log*, yes +*/logs/error.log*, yes +*/logs/error_log*, yes +*/logs/pure-ftpd.log*, yes +*/logs/security_debug_log*, yes +*/logs/security_log*, yes +*/mysql/bin/my.ini*, yes +*/MySQL/data/{HOST}.err*, yes +*/MySQL/data/mysql-bin.index*, yes +*/MySQL/data/mysql-bin.log*, yes +*/MySQL/data/mysql.err*, yes +*/MySQL/data/mysql.log*, yes +*/MySQL/my.cnf*, yes +*/MySQL/my.ini*, yes +*/NetServer/bin/stable/apache/php.ini*, yes +*/NetServerbinstableapachephp.ini*, yes +*/opt/apache22/conf/httpd.conf*, yes +*/opt/apache2/apache2.conf*, yes +*/opt/apache2/apache.conf*, yes +*/opt/apache2/conf/apache2.conf*, yes +*/opt/apache2/conf/apache.conf*, yes +*/opt/apache2/conf/httpd.conf*, yes +*/opt/apache/apache2.conf*, yes +*/opt/apache/apache.conf*, yes +*/opt/apache/conf/apache2.conf*, yes +*/opt/apache/conf/apache.conf*, yes +*/opt/apache/conf/httpd.conf*, yes +*/opt/httpd/apache2.conf*, yes +*/opt/httpd/apache.conf*, yes +*/opt/httpd/conf/apache2.conf*, yes +*/opt/httpd/conf/apache.conf*, yes +*/opt/[JBOSS]/server/default/conf/jboss-minimal.xml*, yes +*/opt/[JBOSS]/server/default/conf/jboss-service.xml*, yes +*/opt/[JBOSS]/server/default/conf/jndi.properties*, yes +*/opt/[JBOSS]/server/default/conf/log4j.xml*, yes +*/opt/[JBOSS]/server/default/conf/login-config.xml*, yes +*/opt/[JBOSS]/server/default/conf/server.log.properties*, yes +*/opt/[JBOSS]/server/default/conf/standardjaws.xml*, yes +*/opt/[JBOSS]/server/default/conf/standardjboss.xml*, yes +*/opt/[JBOSS]/server/default/deploy/jboss-logging.xml*, yes 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+*/var/lp/logs/requests*, yes +*/var/mail/root*, yes +*/var/mysql-bin.index*, yes +*/var/mysql.log*, yes +*/var/nm2/postgresql.conf*, yes +*/var/postgresql/db/postgresql.conf*, yes +*/var/postgresql/log/postgresql.log*, yes +*/var/saf/_log*, yes +*/var/saf/port/log*, yes +*/var/spool/cron/crontabs/root*, yes +*/var/spool/cron/crontabs/root*, yes +*/var/www/conf*, yes +*/var/www/conf/httpd.conf*, yes +*/var/www/html/squirrelmail/config/config.php*, yes +*/var/www/.lighttpdpassword*, yes +*/var/www/logs/access.log*, yes +*/var/www/logs/access_log*, yes +*/var/www/logs/error.log*, yes +*/var/www/logs/error_log*, yes +*/var/www/squirrelmail/config/config.php*, yes +*/Volumes/Macintosh_HD1/opt/apache2/conf/httpd.conf*, yes +*/Volumes/Macintosh_HD1/opt/apache/conf/httpd.conf*, yes +*/Volumes/Macintosh_HD1/opt/httpd/conf/httpd.conf*, yes +*/Volumes/Macintosh_HD1/usr/local/php4/httpd.conf.php*, yes +*/Volumes/Macintosh_HD1/usr/local/php5/httpd.conf.php*, yes +*/Volumes/Macintosh_HD1/usr/local/php/httpd.conf.php*, yes +*/Volumes/Macintosh_HD1/usr/local/php/lib/php.ini*, yes +*/Volumes/webBackup/opt/apache2/conf/httpd.conf*, yes +*/Volumes/webBackup/private/etc/httpd/httpd.conf*, yes +*/Volumes/webBackup/private/etc/httpd/httpd.conf.default*, yes +*/wamp/bin/apache/apache2.2.21/conf/httpd.conf*, yes +*/wamp/bin/apache/apache2.2.21/logs/access.log*, yes +*/wamp/bin/apache/apache2.2.21/logs/error.log*, yes +*/wamp/bin/apache/apache2.2.21/wampserver.conf*, yes +*/wamp/bin/apache/apache2.2.22/conf/httpd.conf*, yes +*/wamp/bin/apache/apache2.2.22/conf/wampserver.conf*, yes +*/wamp/bin/apache/apache2.2.22/logs/access.log*, yes +*/wamp/bin/apache/apache2.2.22/logs/error.log*, yes +*/wamp/bin/apache/apache2.2.22/wampserver.conf*, yes +*/wamp/bin/mysql/mysql5.5.16/data/mysql-bin.index*, yes +*/wamp/bin/mysql/mysql5.5.16/my.ini*, yes +*/wamp/bin/mysql/mysql5.5.16/wampserver.conf*, yes +*/wamp/bin/mysql/mysql5.5.24/data/mysql-bin.index*, yes +*/wamp/bin/mysql/mysql5.5.24/my.ini*, yes +*/wamp/bin/mysql/mysql5.5.24/wampserver.conf*, yes +*/wamp/logs/access.log*, yes +*/wamp/logs/apache_error.log*, yes +*/wamp/logs/genquery.log*, yes +*/wamp/logs/mysql.log*, yes +*/wamp/logs/slowquery.log*, yes +*/web/conf/php.ini*, yes +*/WINDOWS/php.ini*, yes +*/WINDOWSphp.ini*, yes +*/WINDOWS/system32/logfiles/MSFTPSVC*, yes +*/WINDOWS/system32/logfiles/MSFTPSVC1*, yes +*/WINDOWS/system32/logfiles/MSFTPSVC2*, yes +*/WINDOWS/system32/logfiles/SMTPSVC*, yes +*/WINDOWS/system32/logfiles/SMTPSVC1*, yes +*/WINDOWS/system32/logfiles/SMTPSVC2*, yes +*/WINDOWS/system32/logfiles/SMTPSVC3*, yes +*/WINDOWS/system32/logfiles/SMTPSVC4*, yes +*/WINDOWS/system32/logfiles/SMTPSVC5*, yes +*/WINDOWS/system32/logfiles/W3SVC1/inetsvn1.log*, yes +*/WINDOWS/system32/logfiles/W3SVC2/inetsvn1.log*, yes +*/WINDOWS/system32/logfiles/W3SVC3/inetsvn1.log*, yes +*/WINDOWS/system32/logfiles/W3SVC/inetsvn1.log*, yes +*/WINNT/php.ini*, yes +*/WINNTphp.ini*, yes +*/WINNT/system32/logfiles/MSFTPSVC*, yes +*/WINNT/system32/logfiles/MSFTPSVC1*, yes +*/WINNT/system32/logfiles/MSFTPSVC2*, yes +*/WINNT/system32/logfiles/SMTPSVC*, yes +*/WINNT/system32/logfiles/SMTPSVC1*, yes +*/WINNT/system32/logfiles/SMTPSVC2*, yes +*/WINNT/system32/logfiles/SMTPSVC3*, yes +*/WINNT/system32/logfiles/SMTPSVC4*, yes +*/WINNT/system32/logfiles/SMTPSVC5*, yes +*/WINNT/system32/logfiles/W3SVC1/inetsvn1.log*, yes +*/WINNT/system32/logfiles/W3SVC2/inetsvn1.log*, yes +*/WINNT/system32/logfiles/W3SVC3/inetsvn1.log*, yes +*/WINNT/system32/logfiles/W3SVC/inetsvn1.log*, yes +*/www/apache/conf/httpd.conf*, yes +*/www/conf/httpd.conf*, yes +*/www/logs/freebsddiary-access_log*, yes +*/www/logs/freebsddiary-error.log*, yes +*/www/logs/proftpd.system.log*, yes +*/xampp/apache/bin/php.ini*, yes +*/xamppapachebinphp.ini*, yes +*/xampp/apache/conf/httpd.conf*, yes +*/xampp/apache/logs/access.log*, yes +*/xampp/apache/logs/error.log*, yes +*/xampp/FileZillaFTP/FileZilla Server.xml*, yes +*/xampp/htdocs/aca.txt*, yes +*/xampp/htdocs/admin.php*, yes +*/xampp/htdocs/leer.txt*, yes +*/xampp/MercuryMail/mercury.ini*, yes +*/xampp/mysql/data/{HOST}.err*, yes +*/xampp/mysql/data/mysql-bin.index*, yes +*/xampp/mysql/data/mysql.err*, yes +*/xampp/phpMyAdmin/config.inc.php*, yes +*/xampp/php/php.ini*, yes +*/xampp/sendmail/sendmail.ini*, yes +*/xampp/sendmail/sendmail.log*, yes +*/xampp/webalizer/webalizer.conf*, yes +*/proc/self/fd/0*, yes +*/proc/self/fd/1*, yes +*/proc/self/fd/2*, yes +*/proc/self/fd/3*, yes +*/proc/self/fd/4*, yes +*/proc/self/fd/5*, yes +*/proc/self/fd/6*, yes +*/proc/self/fd/7*, yes +*/proc/self/fd/8*, yes +*/proc/self/fd/9*, yes +*/proc/self/fd/10*, yes +*/proc/self/fd/11*, yes +*/proc/self/fd/12*, yes +*/proc/self/fd/13*, yes +*/proc/self/fd/14*, yes +*/proc/self/fd/15*, yes +*/proc/self/fd/16*, yes +*/proc/self/fd/17*, yes +*/proc/self/fd/18*, yes +*/proc/self/fd/19*, yes +*/proc/self/fd/20*, yes +*/proc/self/fd/21*, yes +*/proc/self/fd/22*, yes +*/proc/self/fd/23*, yes +*/proc/self/fd/24*, yes +*/proc/self/fd/25*, yes +*/proc/self/fd/26*, yes +*/proc/self/fd/27*, yes +*/proc/self/fd/28*, yes +*/proc/self/fd/29*, yes +*/proc/self/fd/30*, yes +*/proc/self/fd/31*, yes +*/proc/self/fd/32*, yes +*/proc/self/fd/33*, yes +*/proc/self/fd/34*, yes +*/proc/self/fd/35*, yes +*/proc/self/fd/36*, yes +*/proc/self/fd/37*, yes +*/proc/self/fd/38*, yes +*/proc/self/fd/39*, yes +*/proc/self/fd/40*, yes +*/proc/self/fd/41*, yes +*/proc/self/fd/42*, yes +*/proc/self/fd/43*, yes +*/proc/self/fd/44*, yes +*/proc/self/fd/45*, yes +*/proc/self/fd/46*, yes +*/proc/self/fd/47*, yes +*/proc/self/fd/48*, yes +*/proc/self/fd/49*, yes +*/proc/self/fd/50*, yes +*/proc/self/fd/51*, yes +*/proc/self/fd/52*, yes +*/proc/self/fd/53*, yes +*/proc/self/fd/54*, yes +*/proc/self/fd/55*, yes +*/proc/self/fd/56*, yes +*/proc/self/fd/57*, yes +*/proc/self/fd/58*, yes +*/proc/self/fd/59*, yes +*/proc/self/fd/60*, yes +*/proc/self/fd/61*, yes +*/proc/self/fd/62*, yes +*/proc/self/fd/63*, yes +*/proc/self/fd/64*, yes +*/proc/self/fd/65*, yes +*/proc/self/fd/66*, yes +*/proc/self/fd/67*, yes +*/proc/self/fd/68*, yes +*/proc/self/fd/69*, yes +*/proc/self/fd/70*, yes +*/proc/self/fd/71*, yes +*/proc/self/fd/72*, yes +*/proc/self/fd/73*, yes +*/proc/self/fd/74*, yes +*/proc/self/fd/75*, yes +*/proc/self/fd/76*, yes +*/proc/self/fd/77*, yes +*/proc/self/fd/78*, yes +*/proc/self/fd/79*, yes +*/proc/self/fd/80*, yes +*/proc/self/fd/81*, yes +*/proc/self/fd/82*, yes +*/proc/self/fd/83*, yes +*/proc/self/fd/84*, yes +*/proc/self/fd/85*, yes +*/proc/self/fd/86*, yes +*/proc/self/fd/87*, yes +*/proc/self/fd/88*, yes +*/proc/self/fd/89*, yes +*/proc/self/fd/90*, yes +*/proc/self/fd/91*, yes +*/proc/self/fd/92*, yes +*/proc/self/fd/93*, yes +*/proc/self/fd/94*, yes +*/proc/self/fd/95*, yes +*/proc/self/fd/96*, yes +*/proc/self/fd/97*, yes +*/proc/self/fd/98*, yes +*/proc/self/fd/99*, yes +*/proc/self/fd/100*, yes \ No newline at end of file diff --git a/dist/escu/lookups/mitre_enrichment.csv b/dist/escu/lookups/mitre_enrichment.csv index 0717cbc6ba..fee94d3941 100644 --- a/dist/escu/lookups/mitre_enrichment.csv +++ b/dist/escu/lookups/mitre_enrichment.csv @@ -1,440 +1,564 @@ -mitre_id,technique,tactics,groups -T1205.001,Port Knocking,Defense Evasion|Persistence|Command And Control,no -T1564.006,Run Virtual Instance,Defense Evasion,no -T1564.005,Hidden File System,Defense Evasion,Strider|Equation -T1556.003,Pluggable Authentication Modules,Credential Access|Defense Evasion,no -T1574.012,COR_PROFILER,Persistence|Privilege Escalation|Defense Evasion,Blue Mockingbird -T1562.007,Disable or Modify Cloud Firewall,Defense Evasion,no -T1098.004,SSH Authorized Keys,Persistence,no -T1480.001,Environmental Keying,Defense Evasion,APT41|Equation -T1059.007,JavaScript/JScript,Execution,APT32|FIN7|Cobalt Group|Molerats|TA505|Silence|Leafminer -T1578.004,Revert Cloud Instance,Defense Evasion,no -T1578.003,Delete Cloud Instance,Defense Evasion,no -T1578.001,Create Snapshot,Defense Evasion,no -T1578.002,Create Cloud Instance,Defense Evasion,no -T1127.001,MSBuild,Defense Evasion,Frankenstein -T1027.005,Indicator Removal from Tools,Defense Evasion,Soft Cell|TEMP.Veles|Patchwork|APT3|Turla|OilRig|Deep Panda -T1562.006,Indicator Blocking,Defense Evasion,no -T1573.002,Asymmetric Cryptography,Command And Control,Tropic Trooper|Cobalt Group|OilRig|FIN8|FIN6 -T1573.001,Symmetric Cryptography,Command And Control,Frankenstein|Inception|APT28|APT33|BRONZE BUTLER|Stealth Falcon|Lazarus Group -T1573,Encrypted Channel,Command And Control,Tropic Trooper -T1027.004,Compile After Delivery,Defense Evasion,Gamaredon Group|Rocke|MuddyWater -T1574.004,Dylib Hijacking,Persistence|Privilege Escalation|Defense Evasion,no -T1546.015,Component Object Model Hijacking,Privilege Escalation|Persistence,APT28 -T1071.004,DNS,Command And Control,APT39|Tropic Trooper|OilRig|Ke3chang|Cobalt Group|APT18|APT41|FIN7 -T1071.003,Mail Protocols,Command And Control,APT32|SilverTerrier|APT28 -T1071.002,File Transfer Protocols,Command And Control,APT41|SilverTerrier|Machete|Honeybee -T1071.001,Web Protocols,Command And Control,Sandworm Team|TA505|Rocke|APT39|Tropic Trooper|MuddyWater|Wizard Spider|Inception|APT41|SilverTerrier|Machete|APT28|WIRTE|APT33|FIN4|Night Dragon|APT18|APT38|Cobalt Group|APT19|Threat Group-3390|Rancor|Orangeworm|APT37|Ke3chang|Dark Caracal|Turla|Lazarus Group|BRONZE BUTLER|APT32|OilRig|Magic Hound|Gamaredon Group|Stealth Falcon -T1572,Protocol Tunneling,Command And Control,OilRig|Cobalt Group|FIN6 -T1048.003,Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol,Exfiltration,APT32|APT33|Thrip|FIN8|OilRig|Lazarus Group -T1048.002,Exfiltration Over Asymmetric Encrypted Non-C2 Protocol,Exfiltration,no -T1048.001,Exfiltration Over Symmetric Encrypted Non-C2 Protocol,Exfiltration,no -T1001.003,Protocol Impersonation,Command And Control,Lazarus Group -T1001.002,Steganography,Command And Control,Axiom -T1001.001,Junk Data,Command And Control,APT28 -T1132.002,Non-Standard Encoding,Command And Control,no -T1132.001,Standard Encoding,Command And Control,Sandworm Team|Tropic Trooper|MuddyWater|APT33|APT19|Lazarus Group|BRONZE BUTLER|Patchwork -T1090.004,Domain Fronting,Command And Control,APT29 -T1090.003,Multi-hop Proxy,Command And Control,Inception|FIN4|APT29 -T1090.002,External Proxy,Command And Control,APT39|Silence|Soft Cell|MuddyWater|APT3|FIN5|Lazarus Group|menuPass|APT28 -T1090.001,Internal Proxy,Command And Control,APT39|Strider -T1102.003,One-Way Communication,Command And Control,Leviathan -T1102.002,Bidirectional Communication,Command And Control,Sandworm Team|APT39|APT12|Turla|FIN7|APT37|Magic Hound|Carbanak -T1102.001,Dead Drop Resolver,Command And Control,Rocke|APT41|BRONZE BUTLER|RTM|Patchwork -T1571,Non-Standard Port,Command And Control,Sandworm Team|Rocke|DarkVishnya|Silence|APT-C-36|Magic Hound|APT33|APT32|TEMP.Veles|Lazarus Group|FIN7 -T1074.002,Remote Data Staging,Collection,Threat Group-3390|menuPass|FIN6|Night Dragon|FIN8 -T1074.001,Local Data Staging,Collection,Machete|Soft Cell|TEMP.Veles|Patchwork|Dragonfly 2.0|Honeybee|Leviathan|APT3|FIN5|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT28 -T1078.004,Cloud Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,APT33 -T1564.004,NTFS File Attributes,Defense Evasion,APT32 -T1564.003,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound -T1078.003,Local Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,Tropic Trooper|FIN10|Stolen Pencil|APT32 -T1078.002,Domain Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,TA505|APT3|Threat Group-1314 -T1078.001,Default Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,no -T1564.002,Hidden Users,Defense Evasion,no -T1574.006,LD_PRELOAD,Persistence|Privilege Escalation|Defense Evasion,Rocke -T1574.002,DLL Side-Loading,Persistence|Privilege Escalation|Defense Evasion,BRONZE BUTLER|Naikon|APT41|Soft Cell|Tropic Trooper|Patchwork|APT19|APT32|APT3|menuPass|Threat Group-3390 -T1574.001,DLL Search Order Hijacking,Persistence|Privilege Escalation|Defense Evasion,Whitefly|RTM|Threat Group-3390|menuPass -T1574.008,Path Interception by Search Order Hijacking,Persistence|Privilege Escalation|Defense Evasion,no -T1574.007,Path Interception by PATH Environment Variable,Persistence|Privilege Escalation|Defense Evasion,no -T1574.009,Path Interception by Unquoted Path,Persistence|Privilege Escalation|Defense Evasion,no -T1574.011,Services Registry Permissions Weakness,Persistence|Privilege Escalation|Defense Evasion,no -T1574.005,Executable Installer File Permissions Weakness,Persistence|Privilege Escalation|Defense Evasion,no -T1574.010,Services File Permissions Weakness,Persistence|Privilege Escalation|Defense Evasion,no -T1574,Hijack Execution Flow,Persistence|Privilege Escalation|Defense Evasion,no -T1069.001,Local Groups,Discovery,Turla|OilRig|admin@338 -T1570,Lateral Tool Transfer,Lateral Movement,APT32|Wizard Spider|Turla|FIN10 -T1568.003,DNS Calculation,Command And Control,APT12 -T1204.002,Malicious File,Execution,Magic Hound|Windshift|APT33|Sandworm Team|Naikon|Whitefly|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Wizard Spider|Mofang|Frankenstein|RTM|Inception|BlackTech|APT-C-36|Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|APT19|Dragonfly 2.0|BRONZE BUTLER|Cobalt Group|DarkHydrus|Gorgon Group|Patchwork|OilRig|Dark Caracal|MuddyWater|Lazarus Group|FIN7|APT32|Rancor|APT37|FIN8|APT28|Elderwood|TA459|APT29|Leviathan|menuPass|PLATINUM -T1204.001,Malicious Link,Execution,Patchwork|Windshift|APT32|Molerats|Mofang|BlackTech|TA505|OilRig|Machete|Leviathan|FIN8|FIN4|Elderwood|Dragonfly 2.0|Cobalt Group|APT39|Night Dragon|APT33|Turla -T1195.003,Compromise Hardware Supply Chain,Initial Access,no -T1195.002,Compromise Software Supply Chain,Initial Access,Sandworm Team|APT41 -T1195.001,Compromise Software Dependencies and Development Tools,Initial Access,no -T1568.001,Fast Flux DNS,Command And Control,TA505 -T1052.001,Exfiltration over USB,Exfiltration,Tropic Trooper -T1569.002,Service Execution,Execution,Blue Mockingbird|APT39|APT41|Silence|FIN6|APT32|Honeybee|Ke3chang -T1569.001,Launchctl,Execution,no -T1569,System Services,Execution,no -T1568.002,Domain Generation Algorithms,Command And Control,APT41 -T1568,Dynamic Resolution,Command And Control,no -T1011.001,Exfiltration Over Bluetooth,Exfiltration,no -T1567.002,Exfiltration to Cloud Storage,Exfiltration,Leviathan|Turla -T1567.001,Exfiltration to Code Repository,Exfiltration,no -T1059.006,Python,Execution,Rocke|BRONZE BUTLER|APT39|Dragonfly 2.0|Machete -T1059.005,Visual Basic,Execution,APT33|Sandworm Team|Gamaredon Group|Sharpshooter|Molerats|Frankenstein|Inception|APT-C-36|Rancor|Patchwork|MuddyWater|Honeybee|FIN7|APT37|BRONZE BUTLER|APT32|Turla|TA505|Silence|WIRTE|FIN4|Cobalt Group|Gorgon Group|Leviathan|TA459|Magic Hound -T1059.004,Unix Shell,Execution,Rocke|APT41 -T1059.003,Windows Command Shell,Execution,TA505|Blue Mockingbird|Tropic Trooper|Frankenstein|OilRig|Lazarus Group|Honeybee|Cobalt Group|FIN7|APT41|Soft Cell|Turla|Silence|APT32|APT39|Darkhotel|MuddyWater|APT18|APT38|Dark Caracal|Gorgon Group|Dragonfly 2.0|Rancor|Ke3chang|APT37|Leviathan|FIN8|APT28|Magic Hound|Sowbug|BRONZE BUTLER|FIN10|Threat Group-3390|menuPass|Gamaredon Group|Suckfly|Patchwork|Threat Group-1314|APT3|admin@338|APT1 -T1059.002,AppleScript,Execution,no -T1059.001,PowerShell,Execution,Blue Mockingbird|APT39|DarkVishnya|Molerats|Wizard Spider|Frankenstein|Inception|Silence|APT41|Kimsuky|Soft Cell|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|APT19|DarkHydrus|APT28|Thrip|Gorgon Group|Cobalt Group|Dragonfly 2.0|Leviathan|TA459|FIN8|MuddyWater|Magic Hound|OilRig|BRONZE BUTLER|CopyKittens|APT32|FIN7|FIN10|Threat Group-3390|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda -T1567,Exfiltration Over Web Service,Exfiltration,no -T1497.003,Time Based Evasion,Defense Evasion|Discovery,no -T1497.002,User Activity Based Checks,Defense Evasion|Discovery,FIN7 -T1497.001,System Checks,Defense Evasion|Discovery,Frankenstein -T1498.002,Reflection Amplification,Impact,no -T1498.001,Direct Network Flood,Impact,no -T1566.003,Spearphishing via Service,Initial Access,Magic Hound|Windshift|FIN6|OilRig|Dark Caracal -T1566.002,Spearphishing Link,Initial Access,Windshift|Molerats|Mofang|BlackTech|Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|Turla|APT28|Cobalt Group|Dragonfly 2.0|OilRig|APT33|Elderwood|Leviathan|Magic Hound|Patchwork|APT29|FIN8 -T1566.001,Spearphishing Attachment,Initial Access,Magic Hound|Windshift|APT33|Sandworm Team|Naikon|Gamaredon Group|Sharpshooter|Molerats|Mofang|Wizard Spider|RTM|Frankenstein|Inception|BlackTech|APT-C-36|APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Turla|Gorgon Group|Rancor|DarkHydrus|Cobalt Group|FIN7|OilRig|Lazarus Group|APT19|Dragonfly 2.0|BRONZE BUTLER|APT32|FIN8|MuddyWater|APT28|TA459|Leviathan|Patchwork|PLATINUM|Elderwood|APT29|APT37|menuPass -T1566,Phishing,Initial Access,no -T1565.003,Runtime Data Manipulation,Impact,APT38 -T1565.002,Transmitted Data Manipulation,Impact,APT38 -T1565.001,Stored Data Manipulation,Impact,FIN4|APT38 -T1565,Data Manipulation,Impact,no -T1564.001,Hidden Files and Directories,Defense Evasion,Rocke|APT32|Tropic Trooper|APT28|Lazarus Group -T1564,Hide Artifacts,Defense Evasion,no -T1563.002,RDP Hijacking,Lateral Movement,no -T1563.001,SSH Hijacking,Lateral Movement,no -T1563,Remote Service Session Hijacking,Lateral Movement,no -T1518.001,Security Software Discovery,Discovery,Turla|Rocke|Frankenstein|The White Company|Cobalt Group|Darkhotel|MuddyWater|Tropic Trooper|FIN8|Patchwork|Naikon -T1069.003,Cloud Groups,Discovery,no -T1069.002,Domain Groups,Discovery,Turla|Wizard Spider|Inception|OilRig|FIN6|Dragonfly 2.0|Ke3chang -T1087.004,Cloud Account,Discovery,no -T1087.003,Email Account,Discovery,Sandworm Team|TA505 -T1087.002,Domain Account,Discovery,Turla|Sandworm Team|Dragonfly 2.0|OilRig|BRONZE BUTLER|menuPass|FIN6|Poseidon Group|Ke3chang -T1087.001,Local Account,Discovery,Turla|Poseidon Group|OilRig|Ke3chang|APT32|APT1|Threat Group-3390|APT3|admin@338 -T1553.004,Install Root Certificate,Defense Evasion,no -T1562.004,Disable or Modify System Firewall,Defense Evasion,Rocke|Lazarus Group|Kimsuky|Dragonfly 2.0|Carbanak -T1562.003,HISTCONTROL,Defense Evasion,no -T1562.002,Disable Windows Event Logging,Defense Evasion,Threat Group-3390 -T1562.001,Disable or Modify Tools,Defense Evasion,Gamaredon Group|BRONZE BUTLER|Rocke|Kimsuky|Turla|Night Dragon|Gorgon Group|Lazarus Group|Putter Panda -T1562,Impair Defenses,Defense Evasion,no -T1003.004,LSA Secrets,Credential Access,OilRig|MuddyWater|menuPass|Leafminer|Ke3chang|Dragonfly 2.0|APT33|Threat Group-3390 -T1003.005,Cached Domain Credentials,Credential Access,OilRig|MuddyWater|Leafminer|APT33 -T1561.002,Disk Structure Wipe,Impact,Sandworm Team|Lazarus Group|APT38|APT37 -T1561.001,Disk Content Wipe,Impact,Lazarus Group -T1561,Disk Wipe,Impact,no -T1560.003,Archive via Custom Method,Collection,Lazarus Group|Kimsuky|CopyKittens|FIN6 -T1560.002,Archive via Library,Collection,Lazarus Group|Threat Group-3390 -T1560.001,Archive via Utility,Collection,APT41|Soft Cell|Turla|Gallmaker|APT33|APT39|MuddyWater|Magic Hound|FIN8|BRONZE BUTLER|CopyKittens|APT3|Sowbug|menuPass|APT1|Ke3chang -T1560,Archive Collected Data,Collection,menuPass|APT32|Honeybee|Patchwork|APT28|Dragonfly 2.0|FIN6|Lazarus Group|Ke3chang -T1499.004,Application or System Exploitation,Impact,no -T1499.003,Application Exhaustion Flood,Impact,no -T1499.002,Service Exhaustion Flood,Impact,no -T1499.001,OS Exhaustion Flood,Impact,no -T1491.002,External Defacement,Impact,no -T1491.001,Internal Defacement,Impact,Lazarus Group -T1114.003,Email Forwarding Rule,Collection,no -T1114.002,Remote Email Collection,Collection,APT1|FIN4|APT28|Dragonfly 2.0|Ke3chang|Leafminer -T1114.001,Local Email Collection,Collection,Magic Hound|APT1 -T1134.005,SID-History Injection,Defense Evasion|Privilege Escalation,no -T1134.004,Parent PID Spoofing,Defense Evasion|Privilege Escalation,no -T1134.003,Make and Impersonate Token,Defense Evasion|Privilege Escalation,no -T1134.002,Create Process with Token,Defense Evasion|Privilege Escalation,Turla|Lazarus Group -T1134.001,Token Impersonation/Theft,Defense Evasion|Privilege Escalation,APT28 -T1213.002,Sharepoint,Collection,Ke3chang|APT28 -T1213.001,Confluence,Collection,no -T1555.003,Credentials from Web Browsers,Credential Access,Magic Hound|Sandworm Team|Inception|Stealth Falcon|OilRig|Leafminer|APT33|APT3|Kimsuky|TA505|Stolen Pencil|MuddyWater|APT37|Patchwork|Molerats -T1555.002,Securityd Memory,Credential Access,no -T1555.001,Keychain,Credential Access,no -T1559.002,Dynamic Data Exchange,Execution,Sharpshooter|TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7 -T1559.001,Component Object Model,Execution,Gamaredon Group|MuddyWater -T1559,Inter-Process Communication,Execution,no -T1558.002,Silver Ticket,Credential Access,no -T1558.001,Golden Ticket,Credential Access,Ke3chang -T1558,Steal or Forge Kerberos Tickets,Credential Access,no -T1557.001,LLMNR/NBT-NS Poisoning and SMB Relay,Credential Access|Collection,no -T1557,Man-in-the-Middle,Credential Access|Collection,no -T1556.002,Password Filter DLL,Credential Access|Defense Evasion,Strider -T1556.001,Domain Controller Authentication,Credential Access|Defense Evasion,no -T1556,Modify Authentication Process,Credential Access|Defense Evasion,no -T1056.004,Credential API Hooking,Collection|Credential Access,PLATINUM -T1056.003,Web Portal Capture,Collection|Credential Access,no -T1056.002,GUI Input Capture,Collection|Credential Access,FIN4 -T1056.001,Keylogging,Collection|Credential Access,APT32|Sandworm Team|APT39|APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|Ke3chang|OilRig|PLATINUM|Sowbug|Magic Hound|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28 -T1555,Credentials from Password Stores,Credential Access,APT39|OilRig|MuddyWater|Leafminer|APT33|Turla|Stealth Falcon -T1552.005,Cloud Instance Metadata API,Credential Access,no -T1003.008,/etc/passwd and /etc/shadow,Credential Access,no -T1003.007,Proc Filesystem,Credential Access,no -T1003.006,DCSync,Credential Access,no -T1558.003,Kerberoasting,Credential Access,no -T1552.006,Group Policy Preferences,Credential Access,APT33 -T1003.003,NTDS,Credential Access,FIN6|Dragonfly 2.0 -T1003.002,Security Account Manager,Credential Access,Threat Group-3390|Ke3chang|Soft Cell|Night Dragon|Dragonfly 2.0|menuPass -T1003.001,LSASS Memory,Credential Access,Sandworm Team|Whitefly|Blue Mockingbird|Silence|Threat Group-3390|Leviathan|APT41|Soft Cell|TEMP.Veles|APT33|APT39|Stolen Pencil|APT32|Lazarus Group|Leafminer|Magic Hound|MuddyWater|PLATINUM|FIN8|BRONZE BUTLER|OilRig|FIN6|APT3|APT28|APT1|Ke3chang|Cleaver -T1110.004,Credential Stuffing,Credential Access,no -T1110.003,Password Spraying,Credential Access,APT33|Leafminer|Lazarus Group -T1110.002,Password Cracking,Credential Access,APT41|Dragonfly 2.0|APT3 -T1110.001,Password Guessing,Credential Access,no -T1021.006,Windows Remote Management,Lateral Movement,Threat Group-3390 -T1021.005,VNC,Lateral Movement,GCMAN -T1021.004,SSH,Lateral Movement,Rocke|TEMP.Veles|Leviathan|APT39|OilRig|menuPass|GCMAN -T1021.003,Distributed Component Object Model,Lateral Movement,no -T1021.002,SMB/Windows Admin Shares,Lateral Movement,Blue Mockingbird|APT39|APT32|Orangeworm|FIN8|APT3|Lazarus Group|Threat Group-1314|Turla|Deep Panda|Ke3chang -T1021.001,Remote Desktop Protocol,Lateral Movement,Blue Mockingbird|Wizard Spider|Silence|APT41|TEMP.Veles|Leviathan|APT39|Stolen Pencil|Cobalt Group|Dragonfly 2.0|FIN8|APT3|OilRig|menuPass|FIN10|Patchwork|FIN6|Lazarus Group|APT1|Axiom -T1554,Compromise Client Software Binary,Persistence,no -T1036.006,Space after Filename,Defense Evasion,no -T1036.005,Match Legitimate Name or Location,Defense Evasion,Rocke|Sandworm Team|APT39|Blue Mockingbird|Whitefly|Tropic Trooper|Silence|APT41|menuPass|TEMP.Veles|MuddyWater|BRONZE BUTLER|Sowbug|APT32|Patchwork|Poseidon Group|admin@338|Carbanak|APT1 -T1036.004,Masquerade Task or Service,Defense Evasion,Wizard Spider|APT-C-36|Carbanak|APT32|FIN6|FIN7 -T1036.003,Rename System Utilities,Defense Evasion,menuPass|APT32|Soft Cell|PLATINUM -T1036.002,Right-to-Left Override,Defense Evasion,BRONZE BUTLER|BlackTech|Ke3chang|Scarlet Mimic -T1036.001,Invalid Code Signature,Defense Evasion,Windshift -T1553.003,SIP and Trust Provider Hijacking,Defense Evasion,no -T1553.002,Code Signing,Defense Evasion,Patchwork|Silence|APT41|FIN6|TA505|FIN7|Honeybee|Leviathan|APT37|CopyKittens|Winnti Group|Suckfly|Molerats|Darkhotel -T1553.001,Gatekeeper Bypass,Defense Evasion,no -T1553,Subvert Trust Controls,Defense Evasion,no -T1027.003,Steganography,Defense Evasion,BRONZE BUTLER|Tropic Trooper|MuddyWater|APT37 -T1027.002,Software Packing,Defense Evasion,TA505|Rocke|Soft Cell|The White Company|APT39|APT38|Dark Caracal|Elderwood|APT3|Patchwork|APT29|Night Dragon -T1027.001,Binary Padding,Defense Evasion,Gamaredon Group|Patchwork|APT32|Leviathan|BRONZE BUTLER|Moafee -T1222.002,Linux and Mac File and Directory Permissions Modification,Defense Evasion,Rocke|APT32 -T1222.001,Windows File and Directory Permissions Modification,Defense Evasion,no -T1552.004,Private Keys,Credential Access,Rocke -T1552.003,Bash History,Credential Access,no -T1552.002,Credentials in Registry,Credential Access,APT32 -T1552.001,Credentials In Files,Credential Access,Leafminer|APT33|OilRig|TA505|Stolen Pencil|MuddyWater|APT3 -T1552,Unsecured Credentials,Credential Access,no -T1216.001,PubPrn,Defense Evasion,APT32 -T1070.006,Timestomp,Defense Evasion,Rocke|TEMP.Veles|APT32|Lazarus Group|APT28 -T1070.005,Network Share Connection Removal,Defense Evasion,Threat Group-3390 -T1070.004,File Deletion,Defense Evasion,Sandworm Team|Rocke|Tropic Trooper|Gamaredon Group|Wizard Spider|APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Patchwork|Honeybee|Cobalt Group|Dragonfly 2.0|menuPass|FIN8|OilRig|FIN5|BRONZE BUTLER|Magic Hound|APT3|FIN10|APT28|Threat Group-3390|Group5|Lazarus Group|APT18|APT29 -T1070.003,Clear Command History,Defense Evasion,APT41 -T1550.004,Web Session Cookie,Defense Evasion|Lateral Movement,no -T1550.001,Application Access Token,Defense Evasion|Lateral Movement,APT28 -T1550.003,Pass the Ticket,Defense Evasion|Lateral Movement,APT32|BRONZE BUTLER|APT29 -T1550.002,Pass the Hash,Defense Evasion|Lateral Movement,Soft Cell|APT32|Night Dragon|APT28|APT1 -T1550,Use Alternate Authentication Material,Defense Evasion|Lateral Movement,no -T1548.004,Elevated Execution with Prompt,Privilege Escalation|Defense Evasion,no -T1548.003,Sudo and Sudo Caching,Privilege Escalation|Defense Evasion,no -T1548.002,Bypass User Access Control,Privilege Escalation|Defense Evasion,APT37|MuddyWater|Honeybee|Cobalt Group|Threat Group-3390|BRONZE BUTLER|Patchwork|APT29 -T1548.001,Setuid and Setgid,Privilege Escalation|Defense Evasion,no -T1548,Abuse Elevation Control Mechanism,Privilege Escalation|Defense Evasion,no -T1136.003,Cloud Account,Persistence,no -T1070.002,Clear Linux or Mac System Logs,Defense Evasion,Rocke -T1070.001,Clear Windows Event Logs,Defense Evasion,APT41|APT38|Dragonfly 2.0|APT32|FIN8|FIN5|APT28 -T1136.002,Domain Account,Persistence,Soft Cell -T1136.001,Local Account,Persistence,APT39|APT41|Dragonfly 2.0|Leafminer|APT3 -T1547.011,Plist Modification,Persistence|Privilege Escalation,no -T1547.010,Port Monitors,Persistence|Privilege Escalation,no -T1547.009,Shortcut Modification,Persistence|Privilege Escalation,APT39|Darkhotel|APT29|Gorgon Group|Dragonfly 2.0|Leviathan|Lazarus Group -T1547.008,LSASS Driver,Persistence|Privilege Escalation,no -T1547.007,Re-opened Applications,Persistence|Privilege Escalation,no -T1547.006,Kernel Modules and Extensions,Persistence|Privilege Escalation,no -T1547.005,Security Support Provider,Persistence|Privilege Escalation,no -T1547.004,Winlogon Helper DLL,Persistence|Privilege Escalation,Tropic Trooper|Turla -T1547.003,Time Providers,Persistence|Privilege Escalation,no -T1546.014,Emond,Privilege Escalation|Persistence,no -T1546.013,PowerShell Profile,Privilege Escalation|Persistence,Turla -T1546.012,Image File Execution Options Injection,Privilege Escalation|Persistence,TEMP.Veles -T1218.008,Odbcconf,Defense Evasion,Cobalt Group -T1546.011,Application Shimming,Privilege Escalation|Persistence,FIN7 -T1547.002,Authentication Package,Persistence|Privilege Escalation,no -T1546.010,AppInit DLLs,Privilege Escalation|Persistence,no -T1546.009,AppCert DLLs,Privilege Escalation|Persistence,Honeybee -T1218.007,Msiexec,Defense Evasion,TA505|Rancor -T1546.008,Accessibility Features,Privilege Escalation|Persistence,APT41|APT3|APT29|Deep Panda|Axiom -T1546.007,Netsh Helper DLL,Privilege Escalation|Persistence,no -T1546.006,LC_LOAD_DYLIB Addition,Privilege Escalation|Persistence,no -T1546.005,Trap,Privilege Escalation|Persistence,no -T1546.004,.bash_profile and .bashrc,Privilege Escalation|Persistence,no -T1546.003,Windows Management Instrumentation Event Subscription,Privilege Escalation|Persistence,APT33|Blue Mockingbird|Turla|Leviathan|APT29 -T1546.002,Screensaver,Privilege Escalation|Persistence,no -T1546.001,Change Default File Association,Privilege Escalation|Persistence,Kimsuky -T1547.001,Registry Run Keys / Startup Folder,Persistence|Privilege Escalation,Rocke|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Silence|RTM|Inception|APT41|Machete|Kimsuky|APT33|APT39|APT32|APT18|Turla|Dark Caracal|Cobalt Group|Honeybee|Threat Group-3390|Dragonfly 2.0|Gorgon Group|Ke3chang|APT19|Leviathan|MuddyWater|APT37|BRONZE BUTLER|Magic Hound|APT3|FIN10|FIN7|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel -T1218.002,Control Panel,Defense Evasion,no -T1218.010,Regsvr32,Defense Evasion,Blue Mockingbird|Inception|WIRTE|Cobalt Group|APT19|Leviathan|APT32|Deep Panda -T1218.009,Regsvcs/Regasm,Defense Evasion,no -T1218.005,Mshta,Defense Evasion,Inception|Kimsuky|APT32|MuddyWater|FIN7 -T1218.004,InstallUtil,Defense Evasion,no -T1218.001,Compiled HTML File,Defense Evasion,APT41|Silence|Lazarus Group|Dark Caracal|OilRig -T1218.003,CMSTP,Defense Evasion,Cobalt Group|MuddyWater -T1218.011,Rundll32,Defense Evasion,APT32|Sandworm Team|Blue Mockingbird|TA505|MuddyWater|APT29|APT19|CopyKittens|APT3|Carbanak|APT28 -T1547,Boot or Logon Autostart Execution,Persistence|Privilege Escalation,no -T1546,Event Triggered Execution,Privilege Escalation|Persistence,no -T1098.003,Add Office 365 Global Administrator Role,Persistence,no -T1098.002,Exchange Email Delegate Permissions,Persistence,Magic Hound -T1098.001,Additional Azure Service Principal Credentials,Persistence,no -T1543.004,Launch Daemon,Persistence|Privilege Escalation,no -T1543.003,Windows Service,Persistence|Privilege Escalation,Blue Mockingbird|DarkVishnya|Wizard Spider|APT32|APT41|Kimsuky|Tropic Trooper|Cobalt Group|Ke3chang|Honeybee|FIN7|Threat Group-3390|APT19|APT3|Lazarus Group|Carbanak -T1543.002,Systemd Service,Persistence|Privilege Escalation,Rocke -T1543.001,Launch Agent,Persistence|Privilege Escalation,no -T1037.005,Startup Items,Persistence|Privilege Escalation,no -T1037.004,Rc.common,Persistence|Privilege Escalation,no -T1055.012,Process Hollowing,Defense Evasion|Privilege Escalation,Threat Group-3390|menuPass|Gorgon Group|Patchwork -T1055.013,Process Doppelgänging,Defense Evasion|Privilege Escalation,Leafminer -T1055.011,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no -T1055.014,VDSO Hijacking,Defense Evasion|Privilege Escalation,no -T1055.009,Proc Memory,Defense Evasion|Privilege Escalation,no -T1055.008,Ptrace System Calls,Defense Evasion|Privilege Escalation,no -T1055.005,Thread Local Storage,Defense Evasion|Privilege Escalation,no -T1055.004,Asynchronous Procedure Call,Defense Evasion|Privilege Escalation,no -T1055.003,Thread Execution Hijacking,Defense Evasion|Privilege Escalation,no -T1055.002,Portable Executable Injection,Defense Evasion|Privilege Escalation,Rocke|Gorgon Group -T1055.001,Dynamic-link Library Injection,Defense Evasion|Privilege Escalation,TA505|Turla|Tropic Trooper|Lazarus Group|Putter Panda -T1037.003,Network Logon Script,Persistence|Privilege Escalation,no -T1543,Create or Modify System Process,Persistence|Privilege Escalation,no -T1037.002,Logon Script (Mac),Persistence|Privilege Escalation,no -T1037.001,Logon Script (Windows),Persistence|Privilege Escalation,Cobalt Group|APT28 -T1542.003,Bootkit,Persistence|Defense Evasion,APT41|Lazarus Group|APT28 -T1542.002,Component Firmware,Persistence|Defense Evasion,Equation -T1542.001,System Firmware,Persistence|Defense Evasion,no -T1505.003,Web Shell,Persistence,Tropic Trooper|Soft Cell|Threat Group-3390|TEMP.Veles|Leviathan|APT39|Dragonfly 2.0|APT32|OilRig|Deep Panda -T1505.002,Transport Agent,Persistence,no -T1505.001,SQL Stored Procedures,Persistence,no -T1053.003,Cron,Execution|Persistence|Privilege Escalation,Rocke -T1053.004,Launchd,Execution|Persistence|Privilege Escalation,no -T1053.001,At (Linux),Execution|Persistence|Privilege Escalation,no -T1053.005,Scheduled Task,Execution|Persistence|Privilege Escalation,Gamaredon Group|Blue Mockingbird|MuddyWater|Wizard Spider|Frankenstein|APT-C-36|BRONZE BUTLER|APT41|Machete|Soft Cell|Silence|TEMP.Veles|APT33|APT39|Dragonfly 2.0|Patchwork|OilRig|Rancor|Cobalt Group|FIN8|menuPass|FIN10|APT32|FIN7|Stealth Falcon|FIN6|APT3|APT29 -T1053.002,At (Windows),Execution|Persistence|Privilege Escalation,BRONZE BUTLER|Threat Group-3390|APT18 -T1542,Pre-OS Boot,Defense Evasion|Persistence,no -T1137.001,Office Template Macros,Persistence,MuddyWater -T1137.004,Outlook Home Page,Persistence,OilRig -T1137.003,Outlook Forms,Persistence,no -T1137.005,Outlook Rules,Persistence,no -T1137.006,Add-ins,Persistence,Naikon -T1137.002,Office Test,Persistence,APT28 -T1531,Account Access Removal,Impact,no -T1539,Steal Web Session Cookie,Credential Access,no -T1529,System Shutdown/Reboot,Impact,Lazarus Group|APT38|APT37 -T1518,Software Discovery,Discovery,BRONZE BUTLER|Tropic Trooper|Inception -T1534,Internal Spearphishing,Lateral Movement,Gamaredon Group -T1528,Steal Application Access Token,Credential Access,APT28 -T1535,Unused/Unsupported Cloud Regions,Defense Evasion,no -T1525,Implant Container Image,Persistence,no -T1538,Cloud Service Dashboard,Discovery,no -T1530,Data from Cloud Storage Object,Collection,no -T1578,Modify Cloud Compute Infrastructure,Defense Evasion,no -T1537,Transfer Data to Cloud Account,Exfiltration,no -T1526,Cloud Service Discovery,Discovery,no -T1505,Server Software Component,Persistence,no -T1499,Endpoint Denial of Service,Impact,no -T1497,Virtualization/Sandbox Evasion,Defense Evasion|Discovery,no -T1498,Network Denial of Service,Impact,no -T1496,Resource Hijacking,Impact,Blue Mockingbird|Rocke|APT41|Lazarus Group -T1495,Firmware Corruption,Impact,no -T1491,Defacement,Impact,no -T1490,Inhibit System Recovery,Impact,no -T1489,Service Stop,Impact,Lazarus Group -T1486,Data Encrypted for Impact,Impact,APT41|TA505|APT38 -T1485,Data Destruction,Impact,Sandworm Team|Lazarus Group|APT38 -T1484,Group Policy Modification,Defense Evasion|Privilege Escalation,no -T1482,Domain Trust Discovery,Discovery,Wizard Spider -T1480,Execution Guardrails,Defense Evasion,no -T1222,File and Directory Permissions Modification,Defense Evasion,no -T1221,Template Injection,Defense Evasion,Gamaredon Group|Frankenstein|Inception|APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus -T1220,XSL Script Processing,Defense Evasion,Cobalt Group -T1197,BITS Jobs,Defense Evasion|Persistence,Patchwork|APT41|Leviathan -T1217,Browser Bookmark Discovery,Discovery,no -T1213,Data from Information Repositories,Collection,Turla -T1189,Drive-by Compromise,Initial Access,Turla|Windshift|RTM|Darkhotel|APT38|Dragonfly 2.0|BRONZE BUTLER|Leafminer|Dark Caracal|APT19|APT32|Lazarus Group|Threat Group-3390|Elderwood|APT37|Patchwork|PLATINUM -T1203,Exploitation for Client Execution,Execution,Sandworm Team|MuddyWater|Frankenstein|Inception|BlackTech|APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|Lazarus Group|BRONZE BUTLER|Cobalt Group|APT37|Patchwork|Leviathan|Elderwood|TA459|APT29 -T1212,Exploitation for Credential Access,Credential Access,no -T1211,Exploitation for Defense Evasion,Defense Evasion,APT28 -T1190,Exploit Public-Facing Application,Initial Access,Blue Mockingbird|Rocke|APT39|BlackTech|APT41|Soft Cell|Night Dragon|Axiom -T1210,Exploitation of Remote Services,Lateral Movement,Threat Group-3390|APT28 -T1202,Indirect Command Execution,Defense Evasion,no -T1200,Hardware Additions,Initial Access,DarkVishnya -T1201,Password Policy Discovery,Discovery,Turla|OilRig -T1219,Remote Access Software,Command And Control,Sandworm Team|DarkVishnya|RTM|Kimsuky|Night Dragon|Thrip|Cobalt Group|Carbanak -T1207,Rogue Domain Controller,Defense Evasion,no -T1199,Trusted Relationship,Initial Access,APT28|menuPass -T1218,Signed Binary Proxy Execution,Defense Evasion,no -T1204,User Execution,Execution,no -T1216,Signed Script Proxy Execution,Defense Evasion,no -T1195,Supply Chain Compromise,Initial Access,Elderwood -T1205,Traffic Signaling,Defense Evasion|Persistence|Command And Control,no -T1176,Browser Extensions,Persistence,Kimsuky|Stolen Pencil -T1175,Component Object Model and Distributed COM,Lateral Movement|Execution,no -T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0 -T1185,Man in the Browser,Collection,no -T1134,Access Token Manipulation,Defense Evasion|Privilege Escalation,Blue Mockingbird -T1136,Create Account,Persistence,no -T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Rocke|Sandworm Team|Gamaredon Group|Molerats|Frankenstein|Turla|WIRTE|Darkhotel|Tropic Trooper|menuPass|Honeybee|Threat Group-3390|APT19|Gorgon Group|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER -T1149,LC_MAIN Hijacking,Defense Evasion,no -T1135,Network Share Discovery,Discovery,APT32|APT39|DarkVishnya|APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug -T1137,Office Application Startup,Persistence,Gamaredon Group|APT32 -T1153,Source,Execution,no -T1133,External Remote Services,Persistence|Initial Access,Sandworm Team|APT41|Soft Cell|TEMP.Veles|Night Dragon|OilRig|Dragonfly 2.0|Ke3chang|FIN5|Threat Group-3390|APT18 -T1132,Data Encoding,Command And Control,no -T1129,Shared Modules,Execution,no -T1127,Trusted Developer Utilities Proxy Execution,Defense Evasion,no -T1125,Video Capture,Collection,Silence|FIN7 -T1124,System Time Discovery,Discovery,The White Company|Lazarus Group|BRONZE BUTLER|Turla -T1123,Audio Capture,Collection,APT37 -T1120,Peripheral Device Discovery,Discovery,Turla|APT37|Gamaredon Group|Equation|APT28 -T1119,Automated Collection,Collection,Tropic Trooper|Frankenstein|APT1|APT28|Patchwork|OilRig|FIN5|Threat Group-3390|FIN6 -T1115,Clipboard Data,Collection,APT39|APT38 -T1114,Email Collection,Collection,no -T1113,Screen Capture,Collection,Gamaredon Group|APT39|Silence|MuddyWater|Dragonfly 2.0|OilRig|Dark Caracal|FIN7|BRONZE BUTLER|Magic Hound|Group5|APT28 -T1112,Modify Registry,Defense Evasion,Gamaredon Group|Blue Mockingbird|Wizard Spider|Silence|APT41|Turla|APT32|APT38|Dragonfly 2.0|APT19|Threat Group-3390|Honeybee|Patchwork|Gorgon Group|FIN8 -T1111,Two-Factor Authentication Interception,Credential Access,no -T1110,Brute Force,Credential Access,DarkVishnya|APT39|OilRig|FIN5|Turla -T1108,Redundant Access,Defense Evasion|Persistence,no -T1106,Native API,Execution,Gamaredon Group|Tropic Trooper|Sharpshooter|Turla|Silence|Gorgon Group|APT37 -T1105,Ingress Tool Transfer,Command And Control,Sandworm Team|Whitefly|Rocke|APT39|Tropic Trooper|Sharpshooter|Molerats|Frankenstein|Silence|APT-C-36|APT41|Soft Cell|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Rancor|Cobalt Group|Turla|Gorgon Group|OilRig|Dragonfly 2.0|APT37|FIN8|PLATINUM|Leviathan|Elderwood|Magic Hound|APT3|APT32|BRONZE BUTLER|menuPass|FIN7|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28 -T1104,Multi-Stage Channels,Command And Control,APT41|MuddyWater|APT3 -T1102,Web Service,Command And Control,Gamaredon Group|Rocke|Inception|FIN6 -T1098,Account Manipulation,Persistence,APT3|Dragonfly 2.0|Lazarus Group -T1095,Non-Application Layer Protocol,Command And Control,APT29|PLATINUM|APT3 -T1092,Communication Through Removable Media,Command And Control,APT28 -T1091,Replication Through Removable Media,Lateral Movement|Initial Access,Tropic Trooper|Darkhotel|APT28 -T1090,Proxy,Command And Control,Sandworm Team|Blue Mockingbird|Wizard Spider|APT41|Turla -T1087,Account Discovery,Discovery,no -T1083,File and Directory Discovery,Discovery,Gamaredon Group|Tropic Trooper|Inception|APT41|Kimsuky|APT32|MuddyWater|APT18|Leafminer|Honeybee|Dark Caracal|Dragonfly 2.0|Magic Hound|Sowbug|BRONZE BUTLER|APT3|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang -T1082,System Information Discovery,Discovery,Rocke|Sandworm Team|Blue Mockingbird|Tropic Trooper|Frankenstein|Inception|Kimsuky|Darkhotel|MuddyWater|APT18|Honeybee|APT19|APT37|APT32|Magic Hound|OilRig|APT3|Sowbug|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang -T1080,Taint Shared Content,Lateral Movement,BRONZE BUTLER|Darkhotel -T1078,Valid Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,Sandworm Team|Wizard Spider|Silence|APT41|Soft Cell|TEMP.Veles|APT39|FIN4|Night Dragon|Dragonfly 2.0|FIN8|Leviathan|APT33|OilRig|FIN5|menuPass|APT28|FIN10|Suckfly|FIN6|Threat Group-3390|APT18|PittyTiger|Carbanak -T1074,Data Staged,Collection,Wizard Spider -T1072,Software Deployment Tools,Execution|Lateral Movement,Silence|APT32|Threat Group-1314 -T1071,Application Layer Protocol,Command And Control,Rocke|Magic Hound|Dragonfly 2.0 -T1070,Indicator Removal on Host,Defense Evasion,no -T1069,Permission Groups Discovery,Discovery,TA505|APT3 -T1068,Exploitation for Privilege Escalation,Privilege Escalation,Whitefly|APT33|Cobalt Group|PLATINUM|FIN8|APT32|Threat Group-3390|FIN6|APT28 -T1064,Scripting,Defense Evasion|Execution,no -T1062,Hypervisor,Persistence,no -T1061,Graphical User Interface,Execution,no -T1059,Command and Scripting Interpreter,Execution,APT32|Molerats|Whitefly|Dragonfly 2.0|APT19|FIN7|OilRig|FIN5|Stealth Falcon|FIN6|Ke3chang -T1057,Process Discovery,Discovery,Rocke|Frankenstein|Inception|Darkhotel|MuddyWater|APT1|APT38|Tropic Trooper|APT37|Honeybee|OilRig|APT3|Magic Hound|APT28|Winnti Group|Stealth Falcon|Poseidon Group|Lazarus Group|Molerats|Turla|Deep Panda|Ke3chang -T1056,Input Capture,Collection|Credential Access,no -T1055,Process Injection,Defense Evasion|Privilege Escalation,APT32|Sharpshooter|Silence|APT41|Kimsuky|Turla|Cobalt Group|APT37|Honeybee|PLATINUM -T1053,Scheduled Task/Job,Execution|Persistence|Privilege Escalation,no -T1052,Exfiltration Over Physical Medium,Exfiltration,no -T1051,Shared Webroot,Lateral Movement,no -T1049,System Network Connections Discovery,Discovery,Tropic Trooper|APT41|APT38|Soft Cell|APT32|APT1|OilRig|APT3|menuPass|Threat Group-3390|Poseidon Group|admin@338|Turla|Ke3chang -T1048,Exfiltration Over Alternative Protocol,Exfiltration,no -T1047,Windows Management Instrumentation,Execution,Blue Mockingbird|Wizard Spider|Frankenstein|APT41|FIN6|Soft Cell|APT32|MuddyWater|OilRig|Threat Group-3390|FIN8|Leviathan|menuPass|Stealth Falcon|Lazarus Group|APT29|Deep Panda -T1046,Network Service Scanning,Discovery,Rocke|DarkVishnya|APT41|Tropic Trooper|APT39|APT32|Leafminer|OilRig|Cobalt Group|menuPass|Suckfly|FIN6|Threat Group-3390 -T1043,Commonly Used Port,Command And Control,Machete|OilRig|APT28|TEMP.Veles|Night Dragon|APT29|APT18|APT19|Dragonfly 2.0|FIN7|FIN8|APT37|Magic Hound|APT3|Lazarus Group|Threat Group-3390 -T1041,Exfiltration Over C2 Channel,Exfiltration,Sandworm Team|MuddyWater|Wizard Spider|Frankenstein|Kimsuky|Soft Cell|APT32|APT3|Gamaredon Group|Stealth Falcon|Lazarus Group|Ke3chang -T1040,Network Sniffing,Credential Access|Discovery,Sandworm Team|DarkVishnya|APT33|Stolen Pencil|APT28 -T1039,Data from Network Shared Drive,Collection,Sowbug|BRONZE BUTLER|menuPass -T1037,Boot or Logon Initialization Scripts,Persistence|Privilege Escalation,Rocke -T1036,Masquerading,Defense Evasion,Windshift|APT32|BRONZE BUTLER|menuPass|Dragonfly 2.0 -T1034,Path Interception,Persistence|Privilege Escalation,no -T1033,System Owner/User Discovery,Discovery,Frankenstein|APT41|Soft Cell|Tropic Trooper|APT39|MuddyWater|APT32|APT37|APT19|Dragonfly 2.0|OilRig|Magic Hound|FIN10|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|APT3 -T1030,Data Transfer Size Limits,Exfiltration,Threat Group-3390 -T1029,Scheduled Transfer,Exfiltration,no -T1027,Obfuscated Files or Information,Defense Evasion,Gamaredon Group|Rocke|Sandworm Team|Blue Mockingbird|Whitefly|Molerats|Wizard Spider|Mofang|Frankenstein|Inception|APT-C-36|APT41|Machete|Soft Cell|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|Cobalt Group|Patchwork|Leafminer|APT37|Threat Group-3390|Honeybee|Dark Caracal|menuPass|APT19|BlackOasis|FIN8|Leviathan|Elderwood|MuddyWater|FIN7|Magic Hound|OilRig|APT3|APT32|Group5|Dust Storm|Lazarus Group|Putter Panda|APT28 -T1026,Multiband Communication,Command And Control,Lazarus Group -T1025,Data from Removable Media,Collection,Machete|Turla|Gamaredon Group|APT28 -T1021,Remote Services,Lateral Movement,no -T1020,Automated Exfiltration,Exfiltration,Tropic Trooper|Frankenstein|Honeybee -T1018,Remote System Discovery,Discovery,Sandworm Team|Rocke|Wizard Spider|Silence|Soft Cell|APT39|APT32|Deep Panda|Threat Group-3390|Dragonfly 2.0|Leafminer|Ke3chang|FIN8|APT3|FIN5|BRONZE BUTLER|menuPass|FIN6|Turla -T1016,System Network Configuration Discovery,Discovery,Sandworm Team|Tropic Trooper|Frankenstein|APT41|Soft Cell|APT32|Darkhotel|MuddyWater|APT1|APT19|Dragonfly 2.0|Magic Hound|OilRig|menuPass|Threat Group-3390|Stealth Falcon|Lazarus Group|APT3|Naikon|admin@338|Turla|Ke3chang -T1014,Rootkit,Defense Evasion,Rocke|APT41|APT28|Winnti Group -T1012,Query Registry,Discovery,APT32|Dragonfly 2.0|Threat Group-3390|OilRig|Stealth Falcon|Lazarus Group|Turla -T1011,Exfiltration Over Other Network Medium,Exfiltration,no -T1010,Application Window Discovery,Discovery,Lazarus Group -T1008,Fallback Channels,Command And Control,APT41|OilRig|Lazarus Group -T1007,System Service Discovery,Discovery,BRONZE BUTLER|APT1|OilRig|Poseidon Group|admin@338|Turla|Ke3chang -T1006,Direct Volume Access,Defense Evasion,no -T1005,Data from Local System,Collection,Gamaredon Group|APT39|Frankenstein|Inception|Kimsuky|Soft Cell|Turla|menuPass|Dark Caracal|Dragonfly 2.0|Honeybee|APT37|APT28|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang -T1003,OS Credential Dumping,Credential Access,APT39|Frankenstein|APT32|APT28|Leviathan|Sowbug|Suckfly|Poseidon Group|Axiom -T1001,Data Obfuscation,Command And Control,Axiom +"mitre_id","technique","tactics","groups" +"T1553.006","Code Signing Policy Modification","Defense Evasion","Turla|APT39" +"T1614","System Location Discovery","Discovery","no" +"T1613","Container and Resource Discovery","Discovery","no" +"T1552.007","Container API","Credential Access","no" +"T1612","Build Image on Host","Defense Evasion","no" +"T1611","Escape to Host","Privilege Escalation","no" +"T1204.003","Malicious Image","Execution","no" +"T1053.007","Container Orchestration Job","Execution|Persistence|Privilege Escalation","no" +"T1610","Deploy Container","Defense Evasion|Execution","no" +"T1609","Container Administration Command","Execution","no" +"T1608.005","Link Target","Resource Development","Silent Librarian" +"T1608.004","Drive-by Target","Resource Development","APT32|Threat Group-3390" +"T1608.003","Install Digital Certificate","Resource Development","no" +"T1608.002","Upload Tool","Resource Development","Threat Group-3390" +"T1608.001","Upload Malware","Resource Development","APT32" +"T1608","Stage Capabilities","Resource Development","no" +"T1016.001","Internet Connection Discovery","Discovery","APT29|UNC2452|Turla" +"T1553.005","Mark-of-the-Web Bypass","Defense Evasion","TA505" +"T1555.005","Password Managers","Credential Access","Fox Kitten|Operation Wocao" +"T1484.002","Domain Trust Modification","Defense Evasion|Privilege Escalation","APT29|UNC2452" +"T1484.001","Group Policy Modification","Defense Evasion|Privilege Escalation","Indrik Spider" +"T1547.014","Active Setup","Persistence|Privilege Escalation","no" +"T1606.002","SAML Tokens","Credential Access","APT29|UNC2452" +"T1606.001","Web Cookies","Credential Access","APT29|UNC2452" +"T1606","Forge Web Credentials","Credential Access","no" +"T1555.004","Windows Credential Manager","Credential Access","Stealth Falcon|OilRig|Turla" +"T1059.008","Network Device CLI","Execution","no" +"T1602.002","Network Device Configuration Dump","Collection","no" +"T1542.005","TFTP Boot","Defense Evasion|Persistence","no" +"T1542.004","ROMMONkit","Defense Evasion|Persistence","no" +"T1602.001","SNMP (MIB Dump)","Collection","no" +"T1602","Data from Configuration Repository","Collection","no" +"T1601.002","Downgrade System Image","Defense Evasion","no" +"T1601.001","Patch System Image","Defense Evasion","no" +"T1601","Modify System Image","Defense Evasion","no" +"T1600.002","Disable Crypto Hardware","Defense Evasion","no" +"T1600.001","Reduce Key Space","Defense Evasion","no" +"T1600","Weaken Encryption","Defense Evasion","no" +"T1556.004","Network Device Authentication","Credential Access|Defense Evasion|Persistence","no" +"T1599.001","Network Address Translation Traversal","Defense Evasion","no" +"T1599","Network Boundary Bridging","Defense Evasion","no" +"T1020.001","Traffic Duplication","Exfiltration","no" +"T1557.002","ARP Cache Poisoning","Credential Access|Collection","Cleaver" +"T1588.006","Vulnerabilities","Resource Development","Sandworm Team" +"T1053.006","Systemd Timers","Execution|Persistence|Privilege Escalation","no" +"T1562.008","Disable Cloud Logs","Defense Evasion","no" +"T1547.012","Print Processors","Persistence|Privilege Escalation","no" +"T1598.003","Spearphishing Link","Reconnaissance","Silent Librarian|Sidewinder|Sandworm Team|APT32|Kimsuky" +"T1598.002","Spearphishing Attachment","Reconnaissance","Sidewinder" +"T1598.001","Spearphishing Service","Reconnaissance","no" +"T1598","Phishing for Information","Reconnaissance","ZIRCONIUM|APT28" +"T1597.002","Purchase Technical Data","Reconnaissance","no" +"T1597.001","Threat Intel Vendors","Reconnaissance","no" +"T1597","Search Closed Sources","Reconnaissance","no" +"T1596.005","Scan Databases","Reconnaissance","no" +"T1596.004","CDNs","Reconnaissance","no" +"T1596.003","Digital Certificates","Reconnaissance","no" +"T1596.001","DNS/Passive DNS","Reconnaissance","no" +"T1596.002","WHOIS","Reconnaissance","no" +"T1596","Search Open Technical Databases","Reconnaissance","no" +"T1595.002","Vulnerability Scanning","Reconnaissance","Volatile Cedar|APT28|Sandworm Team" +"T1595.001","Scanning IP Blocks","Reconnaissance","no" +"T1595","Active Scanning","Reconnaissance","no" +"T1594","Search Victim-Owned Websites","Reconnaissance","Silent Librarian|Sandworm Team" +"T1593.002","Search Engines","Reconnaissance","no" +"T1593.001","Social Media","Reconnaissance","no" +"T1593","Search Open Websites/Domains","Reconnaissance","Sandworm Team" +"T1592.004","Client Configurations","Reconnaissance","HAFNIUM" +"T1592.003","Firmware","Reconnaissance","no" +"T1592.002","Software","Reconnaissance","Sandworm Team" +"T1592.001","Hardware","Reconnaissance","no" +"T1592","Gather Victim Host Information","Reconnaissance","no" +"T1591.004","Identify Roles","Reconnaissance","no" +"T1591.003","Identify Business Tempo","Reconnaissance","no" +"T1591.001","Determine Physical Locations","Reconnaissance","no" +"T1591.002","Business Relationships","Reconnaissance","Sandworm Team" +"T1591","Gather Victim Org Information","Reconnaissance","no" +"T1590.006","Network Security Appliances","Reconnaissance","no" +"T1590.005","IP Addresses","Reconnaissance","HAFNIUM" +"T1590.004","Network Topology","Reconnaissance","no" +"T1590.003","Network Trust Dependencies","Reconnaissance","no" +"T1590.002","DNS","Reconnaissance","no" +"T1590.001","Domain Properties","Reconnaissance","Sandworm Team" +"T1590","Gather Victim Network Information","Reconnaissance","HAFNIUM" +"T1589.003","Employee Names","Reconnaissance","Silent Librarian|Sandworm Team" +"T1589.002","Email Addresses","Reconnaissance","TA551|MuddyWater|HAFNIUM|APT32|Silent Librarian|Sandworm Team" +"T1589.001","Credentials","Reconnaissance","APT28|Magic Hound|Chimera" +"T1589","Gather Victim Identity Information","Reconnaissance","APT32" +"T1588.005","Exploits","Resource Development","no" +"T1588.004","Digital Certificates","Resource Development","Lazarus Group|Silent Librarian" +"T1588.003","Code Signing Certificates","Resource Development","Wizard Spider" +"T1588.002","Tool","Resource Development","MuddyWater|Silent Librarian|GALLIUM|Sandworm Team" +"T1588.001","Malware","Resource Development","Turla|APT1" +"T1588","Obtain Capabilities","Resource Development","no" +"T1587.004","Exploits","Resource Development","no" +"T1587.003","Digital Certificates","Resource Development","APT29|PROMETHIUM" +"T1587.002","Code Signing Certificates","Resource Development","PROMETHIUM|Patchwork" +"T1587.001","Malware","Resource Development","APT29|Lazarus Group|UNC2452|Sandworm Team|Turla|FIN7|Night Dragon|Cleaver" +"T1587","Develop Capabilities","Resource Development","Kimsuky" +"T1586.002","Email Accounts","Resource Development","Magic Hound|Kimsuky" +"T1586.001","Social Media Accounts","Resource Development","no" +"T1586","Compromise Accounts","Resource Development","no" +"T1585.002","Email Accounts","Resource Development","Magic Hound|Silent Librarian|Sandworm Team|APT1" +"T1585.001","Social Media Accounts","Resource Development","Fox Kitten|Sandworm Team|APT32|Cleaver" +"T1585","Establish Accounts","Resource Development","Fox Kitten|APT17" +"T1584.006","Web Services","Resource Development","Turla" +"T1584.005","Botnet","Resource Development","no" +"T1584.004","Server","Resource Development","Indrik Spider|Turla|APT16" +"T1584.003","Virtual Private Server","Resource Development","Turla" +"T1584.002","DNS Server","Resource Development","no" +"T1584.001","Domains","Resource Development","APT29|UNC2452|APT1" +"T1583.006","Web Services","Resource Development","ZIRCONIUM|MuddyWater|HAFNIUM|Lazarus Group|Turla|APT32|APT17|APT29" +"T1583.005","Botnet","Resource Development","no" +"T1583.004","Server","Resource Development","GALLIUM|Sandworm Team" +"T1583.003","Virtual Private Server","Resource Development","HAFNIUM|TEMP.Veles" +"T1583.002","DNS Server","Resource Development","no" +"T1584","Compromise Infrastructure","Resource Development","no" +"T1583.001","Domains","Resource Development","APT29|Mustang Panda|ZIRCONIUM|UNC2452|Lazarus Group|Silent Librarian|menuPass|Sandworm Team|APT32|Kimsuky|APT1|APT28" +"T1583","Acquire Infrastructure","Resource Development","no" +"T1564.007","VBA Stomping","Defense Evasion","no" +"T1558.004","AS-REP Roasting","Credential Access","no" +"T1580","Cloud Infrastructure Discovery","Discovery","no" +"T1218.012","Verclsid","Defense Evasion","no" +"T1205.001","Port Knocking","Defense Evasion|Persistence|Command And Control","PROMETHIUM" +"T1564.006","Run Virtual Instance","Defense Evasion","no" +"T1564.005","Hidden File System","Defense Evasion","Strider|Equation" +"T1556.003","Pluggable Authentication Modules","Credential Access|Defense Evasion|Persistence","no" +"T1574.012","COR_PROFILER","Persistence|Privilege Escalation|Defense Evasion","Blue Mockingbird" +"T1562.007","Disable or Modify Cloud Firewall","Defense Evasion","no" +"T1098.004","SSH Authorized Keys","Persistence","no" +"T1480.001","Environmental Keying","Defense Evasion","APT41|Equation" +"T1059.007","JavaScript","Execution","MuddyWater|Turla|Higaisa|Sidewinder|Evilnum|Kimsuky|FIN6|APT32|FIN7|Cobalt Group|Molerats|TA505|Silence|Leafminer" +"T1578.004","Revert Cloud Instance","Defense Evasion","no" +"T1578.003","Delete Cloud Instance","Defense Evasion","no" +"T1578.001","Create Snapshot","Defense Evasion","no" +"T1578.002","Create Cloud Instance","Defense Evasion","no" +"T1127.001","MSBuild","Defense Evasion","Frankenstein" +"T1027.005","Indicator Removal from Tools","Defense Evasion","Operation Wocao|GALLIUM|TEMP.Veles|Patchwork|APT3|Turla|OilRig|Deep Panda" +"T1562.006","Indicator Blocking","Defense Evasion","no" +"T1573.002","Asymmetric Cryptography","Command And Control","Operation Wocao|Tropic Trooper|Cobalt Group|OilRig|FIN8|FIN6" +"T1573.001","Symmetric Cryptography","Command And Control","Mustang Panda|Darkhotel|ZIRCONIUM|Higaisa|Frankenstein|Inception|APT28|APT33|BRONZE BUTLER|Stealth Falcon|Lazarus Group" +"T1573","Encrypted Channel","Command And Control","Tropic Trooper" +"T1027.004","Compile After Delivery","Defense Evasion","Gamaredon Group|Rocke|MuddyWater" +"T1574.004","Dylib Hijacking","Persistence|Privilege Escalation|Defense Evasion","no" +"T1546.015","Component Object Model Hijacking","Privilege Escalation|Persistence","APT28" +"T1071.004","DNS","Command And Control","Chimera|APT39|Tropic Trooper|OilRig|Ke3chang|Cobalt Group|APT18|APT41|FIN7" +"T1071.003","Mail Protocols","Command And Control","Turla|Kimsuky|APT32|SilverTerrier|APT28" +"T1071.002","File Transfer Protocols","Command And Control","Kimsuky|APT41|SilverTerrier|Honeybee" +"T1071.001","Web Protocols","Command And Control","APT29|Mustang Panda|Windshift|TA551|Higaisa|HAFNIUM|Sidewinder|Chimera|UNC2452|Sandworm Team|TA505|Rocke|APT39|Tropic Trooper|MuddyWater|Wizard Spider|Inception|APT41|SilverTerrier|APT28|WIRTE|APT33|FIN4|Night Dragon|APT18|APT38|APT19|Cobalt Group|Rancor|Orangeworm|Threat Group-3390|Ke3chang|Turla|APT37|Dark Caracal|Lazarus Group|BRONZE BUTLER|APT32|Magic Hound|OilRig|Gamaredon Group|Stealth Falcon" +"T1572","Protocol Tunneling","Command And Control","Chimera|Fox Kitten|OilRig|Cobalt Group|FIN6" +"T1048.003","Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol","Exfiltration","Wizard Spider|FIN6|APT32|APT33|Thrip|FIN8|OilRig|Lazarus Group" +"T1048.002","Exfiltration Over Asymmetric Encrypted Non-C2 Protocol","Exfiltration","APT29|UNC2452" +"T1048.001","Exfiltration Over Symmetric Encrypted Non-C2 Protocol","Exfiltration","no" +"T1001.003","Protocol Impersonation","Command And Control","Higaisa|Lazarus Group" +"T1001.002","Steganography","Command And Control","APT29|Axiom" +"T1001.001","Junk Data","Command And Control","APT28" +"T1132.002","Non-Standard Encoding","Command And Control","no" +"T1132.001","Standard Encoding","Command And Control","HAFNIUM|TA551|Sandworm Team|Tropic Trooper|MuddyWater|APT33|APT19|Lazarus Group|BRONZE BUTLER|Patchwork" +"T1090.004","Domain Fronting","Command And Control","APT29" +"T1090.003","Multi-hop Proxy","Command And Control","APT28|Operation Wocao|Inception|FIN4|APT29" +"T1090.002","External Proxy","Command And Control","APT39|Silence|GALLIUM|MuddyWater|APT3|FIN5|Lazarus Group|menuPass|APT28" +"T1090.001","Internal Proxy","Command And Control","APT29|Higaisa|UNC2452|Operation Wocao|APT39|Strider" +"T1102.003","One-Way Communication","Command And Control","Leviathan" +"T1102.002","Bidirectional Communication","Command And Control","ZIRCONIUM|MuddyWater|APT28|APT29|Sandworm Team|APT39|APT12|FIN7|Turla|APT37|Magic Hound|Carbanak" +"T1102.001","Dead Drop Resolver","Command And Control","Rocke|APT41|BRONZE BUTLER|RTM|Patchwork" +"T1571","Non-Standard Port","Command And Control","Sandworm Team|Rocke|DarkVishnya|Silence|APT-C-36|Magic Hound|APT33|APT32|TEMP.Veles|Lazarus Group|FIN7" +"T1074.002","Remote Data Staging","Collection","APT29|Chimera|UNC2452|Threat Group-3390|menuPass|FIN6|Night Dragon|FIN8" +"T1074.001","Local Data Staging","Collection","Mustang Panda|Sidewinder|Chimera|Kimsuky|APT39|Operation Wocao|GALLIUM|TEMP.Veles|Honeybee|Patchwork|Dragonfly 2.0|Leviathan|APT3|FIN5|menuPass|Lazarus Group|Threat Group-3390|APT28" +"T1078.004","Cloud Accounts","Defense Evasion|Persistence|Privilege Escalation|Initial Access","APT33" +"T1564.004","NTFS File Attributes","Defense Evasion","APT32" +"T1564.003","Hidden Window","Defense Evasion","Higaisa|Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound" +"T1078.003","Local Accounts","Defense Evasion|Persistence|Privilege Escalation|Initial Access","HAFNIUM|Turla|Operation Wocao|PROMETHIUM|Tropic Trooper|FIN10|Stolen Pencil|APT32" +"T1078.002","Domain Accounts","Defense Evasion|Persistence|Privilege Escalation|Initial Access","Indrik Spider|Chimera|Operation Wocao|Sandworm Team|Wizard Spider|APT29|TA505|APT3|Threat Group-1314" +"T1078.001","Default Accounts","Defense Evasion|Persistence|Privilege Escalation|Initial Access","no" +"T1564.002","Hidden Users","Defense Evasion","no" +"T1574.006","Dynamic Linker Hijacking","Persistence|Privilege Escalation|Defense Evasion","APT41|Rocke" +"T1574.002","DLL Side-Loading","Persistence|Privilege Escalation|Defense Evasion","Mustang Panda|Higaisa|BlackTech|Sidewinder|Chimera|BRONZE BUTLER|Naikon|APT41|GALLIUM|Tropic Trooper|Patchwork|APT19|APT32|APT3|menuPass|Threat Group-3390" +"T1574.001","DLL Search Order Hijacking","Persistence|Privilege Escalation|Defense Evasion","Evilnum|APT41|Whitefly|RTM|Threat Group-3390|menuPass" +"T1574.008","Path Interception by Search Order Hijacking","Persistence|Privilege Escalation|Defense Evasion","no" +"T1574.007","Path Interception by PATH Environment Variable","Persistence|Privilege Escalation|Defense Evasion","no" +"T1574.009","Path Interception by Unquoted Path","Persistence|Privilege Escalation|Defense Evasion","no" +"T1574.011","Services Registry Permissions Weakness","Persistence|Privilege Escalation|Defense Evasion","no" +"T1574.005","Executable Installer File Permissions Weakness","Persistence|Privilege Escalation|Defense Evasion","no" +"T1574.010","Services File Permissions Weakness","Persistence|Privilege Escalation|Defense Evasion","no" +"T1574","Hijack Execution Flow","Persistence|Privilege Escalation|Defense Evasion","no" +"T1069.001","Local Groups","Discovery","Chimera|Operation Wocao|Turla|OilRig|admin@338" +"T1570","Lateral Tool Transfer","Lateral Movement","Chimera|GALLIUM|Operation Wocao|APT32|Wizard Spider|Turla|FIN10" +"T1568.003","DNS Calculation","Command And Control","APT12" +"T1204.002","Malicious File","Execution","Ajax Security Team|Mustang Panda|TA551|Higaisa|Sidewinder|Kimsuky|FIN6|PROMETHIUM|APT30|Windshift|APT33|Sandworm Team|Naikon|Whitefly|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Wizard Spider|Mofang|Frankenstein|RTM|Inception|BlackTech|APT-C-36|Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|BRONZE BUTLER|FIN7|Dragonfly 2.0|APT19|Dark Caracal|Cobalt Group|Gorgon Group|Patchwork|MuddyWater|DarkHydrus|OilRig|APT32|Rancor|Lazarus Group|APT29|APT28|APT37|FIN8|Elderwood|menuPass|PLATINUM|TA459|Leviathan" +"T1204.001","Malicious Link","Execution","APT28|APT29|Mustang Panda|Sidewinder|ZIRCONIUM|MuddyWater|Evilnum|Sandworm Team|Wizard Spider|Patchwork|Windshift|APT32|Molerats|Mofang|BlackTech|TA505|OilRig|Machete|Leviathan|FIN8|FIN4|Elderwood|Dragonfly 2.0|Cobalt Group|APT39|Night Dragon|APT33|Turla" +"T1195.003","Compromise Hardware Supply Chain","Initial Access","no" +"T1195.002","Compromise Software Supply Chain","Initial Access","APT29|UNC2452|Cobalt Group|GOLD SOUTHFIELD|Dragonfly|Sandworm Team|APT41" +"T1195.001","Compromise Software Dependencies and Development Tools","Initial Access","no" +"T1568.001","Fast Flux DNS","Command And Control","menuPass|TA505" +"T1052.001","Exfiltration over USB","Exfiltration","Mustang Panda|Tropic Trooper" +"T1569.002","Service Execution","Execution","Chimera|Operation Wocao|Wizard Spider|Blue Mockingbird|APT39|APT41|Silence|FIN6|APT32|Honeybee|Ke3chang" +"T1569.001","Launchctl","Execution","no" +"T1569","System Services","Execution","no" +"T1568.002","Domain Generation Algorithms","Command And Control","TA551|APT41" +"T1568","Dynamic Resolution","Command And Control","APT29|UNC2452" +"T1011.001","Exfiltration Over Bluetooth","Exfiltration","no" +"T1567.002","Exfiltration to Cloud Storage","Exfiltration","ZIRCONIUM|HAFNIUM|Chimera|Leviathan|Turla" +"T1567.001","Exfiltration to Code Repository","Exfiltration","no" +"T1059.006","Python","Execution","ZIRCONIUM|MuddyWater|Turla|Operation Wocao|Kimsuky|APT29|Rocke|BRONZE BUTLER|APT39|Dragonfly 2.0|Machete" +"T1059.005","Visual Basic","Execution","Mustang Panda|Windshift|Higaisa|Sidewinder|APT39|Machete|Operation Wocao|Kimsuky|Lazarus Group|APT33|Sandworm Team|Gamaredon Group|Sharpshooter|Molerats|Frankenstein|Inception|APT-C-36|Rancor|Patchwork|MuddyWater|Honeybee|FIN7|APT37|BRONZE BUTLER|APT32|Turla|TA505|Silence|WIRTE|FIN4|Cobalt Group|Gorgon Group|Leviathan|TA459|Magic Hound" +"T1059.004","Unix Shell","Execution","Rocke|APT41" +"T1059.003","Windows Command Shell","Execution","APT29|Mustang Panda|ZIRCONIUM|TA551|Higaisa|Indrik Spider|Chimera|UNC2452|Fox Kitten|Machete|Operation Wocao|Wizard Spider|FIN6|TA505|Blue Mockingbird|Tropic Trooper|Frankenstein|OilRig|Lazarus Group|Honeybee|Cobalt Group|FIN7|APT41|GALLIUM|Turla|Silence|APT32|Darkhotel|MuddyWater|APT18|APT38|Gorgon Group|Dark Caracal|Rancor|Ke3chang|Dragonfly 2.0|Leviathan|APT37|FIN8|APT28|Magic Hound|Sowbug|BRONZE BUTLER|FIN10|menuPass|Threat Group-3390|Gamaredon Group|Patchwork|Suckfly|Threat Group-1314|APT3|admin@338|APT1" +"T1059.002","AppleScript","Execution","no" +"T1059.001","PowerShell","Execution","Mustang Panda|Indrik Spider|HAFNIUM|Sidewinder|UNC2452|Fox Kitten|GOLD SOUTHFIELD|Sandworm Team|Operation Wocao|Lazarus Group|Chimera|Blue Mockingbird|APT39|DarkVishnya|Molerats|Wizard Spider|Frankenstein|Inception|Silence|APT41|Kimsuky|GALLIUM|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|APT19|Dragonfly 2.0|APT28|Thrip|Cobalt Group|DarkHydrus|Gorgon Group|Leviathan|TA459|MuddyWater|FIN8|Magic Hound|CopyKittens|OilRig|BRONZE BUTLER|FIN10|Threat Group-3390|APT32|FIN7|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda" +"T1567","Exfiltration Over Web Service","Exfiltration","APT28" +"T1497.003","Time Based Evasion","Defense Evasion|Discovery","no" +"T1497.002","User Activity Based Checks","Defense Evasion|Discovery","Darkhotel|FIN7" +"T1497.001","System Checks","Defense Evasion|Discovery","Darkhotel|Evilnum|Frankenstein" +"T1498.002","Reflection Amplification","Impact","no" +"T1498.001","Direct Network Flood","Impact","no" +"T1566.003","Spearphishing via Service","Initial Access","Ajax Security Team|Lazarus Group|Magic Hound|Windshift|FIN6|OilRig|Dark Caracal" +"T1566.002","Spearphishing Link","Initial Access","Mustang Panda|ZIRCONIUM|MuddyWater|Sidewinder|Evilnum|Sandworm Team|Wizard Spider|APT1|Windshift|Molerats|Mofang|BlackTech|Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|Cobalt Group|Turla|APT28|Dragonfly 2.0|OilRig|APT33|APT29|Leviathan|Elderwood|FIN8|Patchwork|Magic Hound" +"T1566.001","Spearphishing Attachment","Initial Access","Ajax Security Team|Mustang Panda|TA551|Higaisa|Sidewinder|APT1|FIN6|APT30|Windshift|APT33|Sandworm Team|Naikon|Gamaredon Group|Sharpshooter|Molerats|Mofang|Wizard Spider|RTM|Frankenstein|Inception|BlackTech|APT-C-36|APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Gorgon Group|Rancor|DarkHydrus|Cobalt Group|FIN7|APT19|Lazarus Group|OilRig|APT32|BRONZE BUTLER|Dragonfly 2.0|MuddyWater|APT28|FIN8|TA459|Elderwood|APT29|Leviathan|Patchwork|APT37|menuPass|PLATINUM" +"T1566","Phishing","Initial Access","GOLD SOUTHFIELD|Dragonfly" +"T1565.003","Runtime Data Manipulation","Impact","APT38" +"T1565.002","Transmitted Data Manipulation","Impact","APT38" +"T1565.001","Stored Data Manipulation","Impact","FIN4|APT38" +"T1565","Data Manipulation","Impact","no" +"T1564.001","Hidden Files and Directories","Defense Evasion","Mustang Panda|Rocke|APT32|Tropic Trooper|Lazarus Group|APT28" +"T1564","Hide Artifacts","Defense Evasion","no" +"T1563.002","RDP Hijacking","Lateral Movement","no" +"T1563.001","SSH Hijacking","Lateral Movement","no" +"T1563","Remote Service Session Hijacking","Lateral Movement","no" +"T1518.001","Security Software Discovery","Discovery","Windshift|Sidewinder|Operation Wocao|Wizard Spider|Turla|Rocke|Frankenstein|The White Company|Cobalt Group|Darkhotel|MuddyWater|Tropic Trooper|FIN8|Patchwork|Naikon" +"T1069.003","Cloud Groups","Discovery","no" +"T1069.002","Domain Groups","Discovery","Turla|Inception|OilRig|Dragonfly 2.0|Ke3chang" +"T1087.004","Cloud Account","Discovery","no" +"T1087.003","Email Account","Discovery","Sandworm Team|TA505" +"T1087.002","Domain Account","Discovery","MuddyWater|Fox Kitten|Operation Wocao|Wizard Spider|Chimera|Turla|Sandworm Team|Dragonfly 2.0|BRONZE BUTLER|OilRig|menuPass|FIN6|Poseidon Group|Ke3chang" +"T1087.001","Local Account","Discovery","Chimera|Fox Kitten|Turla|Poseidon Group|OilRig|Ke3chang|APT32|APT1|Threat Group-3390|APT3|admin@338" +"T1553.004","Install Root Certificate","Defense Evasion","no" +"T1562.004","Disable or Modify System Firewall","Defense Evasion","APT29|UNC2452|Operation Wocao|Rocke|Lazarus Group|Kimsuky|Dragonfly 2.0|Carbanak" +"T1562.003","Impair Command History Logging","Defense Evasion","no" +"T1562.002","Disable Windows Event Logging","Defense Evasion","APT29|UNC2452|Threat Group-3390" +"T1562.001","Disable or Modify Tools","Defense Evasion","APT29|MuddyWater|UNC2452|Wizard Spider|FIN6|Gamaredon Group|BRONZE BUTLER|Rocke|Kimsuky|Turla|Night Dragon|Gorgon Group|Lazarus Group|Putter Panda" +"T1562","Impair Defenses","Defense Evasion","no" +"T1003.004","LSA Secrets","Credential Access","OilRig|MuddyWater|menuPass|Leafminer|Ke3chang|Dragonfly 2.0|APT33|Threat Group-3390" +"T1003.005","Cached Domain Credentials","Credential Access","OilRig|MuddyWater|Leafminer|APT33" +"T1561.002","Disk Structure Wipe","Impact","Sandworm Team|Lazarus Group|APT38|APT37" +"T1561.001","Disk Content Wipe","Impact","Lazarus Group" +"T1561","Disk Wipe","Impact","no" +"T1560.003","Archive via Custom Method","Collection","Mustang Panda|Lazarus Group|Kimsuky|CopyKittens|FIN6" +"T1560.002","Archive via Library","Collection","Lazarus Group|Threat Group-3390" +"T1560.001","Archive via Utility","Collection","APT29|Mustang Panda|HAFNIUM|UNC2452|Fox Kitten|Operation Wocao|Chimera|APT41|GALLIUM|Turla|Gallmaker|APT33|APT39|MuddyWater|Magic Hound|FIN8|BRONZE BUTLER|CopyKittens|Sowbug|APT3|menuPass|APT1|Ke3chang" +"T1560","Archive Collected Data","Collection","menuPass|APT32|Honeybee|Patchwork|APT28|Dragonfly 2.0|FIN6|Lazarus Group|Ke3chang" +"T1499.004","Application or System Exploitation","Impact","no" +"T1499.003","Application Exhaustion Flood","Impact","no" +"T1499.002","Service Exhaustion Flood","Impact","no" +"T1499.001","OS Exhaustion Flood","Impact","no" +"T1491.002","External Defacement","Impact","Sandworm Team" +"T1491.001","Internal Defacement","Impact","Lazarus Group" +"T1114.003","Email Forwarding Rule","Collection","Silent Librarian|Kimsuky" +"T1114.002","Remote Email Collection","Collection","APT29|HAFNIUM|Chimera|UNC2452|APT1|FIN4|Dragonfly 2.0|APT28|Leafminer|Ke3chang" +"T1114.001","Local Email Collection","Collection","Chimera|Magic Hound|APT1" +"T1134.005","SID-History Injection","Defense Evasion|Privilege Escalation","no" +"T1134.004","Parent PID Spoofing","Defense Evasion|Privilege Escalation","no" +"T1134.003","Make and Impersonate Token","Defense Evasion|Privilege Escalation","no" +"T1134.002","Create Process with Token","Defense Evasion|Privilege Escalation","Turla|Lazarus Group" +"T1134.001","Token Impersonation/Theft","Defense Evasion|Privilege Escalation","APT28" +"T1213.002","Sharepoint","Collection","Chimera|Ke3chang|APT28" +"T1213.001","Confluence","Collection","no" +"T1555.003","Credentials from Web Browsers","Credential Access","Ajax Security Team|ZIRCONIUM|FIN6|Sandworm Team|Inception|Stealth Falcon|OilRig|Leafminer|APT33|APT3|Kimsuky|TA505|Stolen Pencil|MuddyWater|APT37|Patchwork|Molerats" +"T1555.002","Securityd Memory","Credential Access","no" +"T1555.001","Keychain","Credential Access","no" +"T1559.002","Dynamic Data Exchange","Execution","Sidewinder|Sharpshooter|TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7" +"T1559.001","Component Object Model","Execution","Gamaredon Group|MuddyWater" +"T1559","Inter-Process Communication","Execution","no" +"T1558.002","Silver Ticket","Credential Access","no" +"T1558.001","Golden Ticket","Credential Access","Ke3chang" +"T1558","Steal or Forge Kerberos Tickets","Credential Access","no" +"T1557.001","LLMNR/NBT-NS Poisoning and SMB Relay","Credential Access|Collection","Wizard Spider" +"T1557","Man-in-the-Middle","Credential Access|Collection","Kimsuky" +"T1556.002","Password Filter DLL","Credential Access|Defense Evasion|Persistence","Strider" +"T1556.001","Domain Controller Authentication","Credential Access|Defense Evasion|Persistence","Chimera" +"T1556","Modify Authentication Process","Credential Access|Defense Evasion|Persistence","no" +"T1056.004","Credential API Hooking","Collection|Credential Access","PLATINUM" +"T1056.003","Web Portal Capture","Collection|Credential Access","no" +"T1056.002","GUI Input Capture","Collection|Credential Access","FIN4" +"T1056.001","Keylogging","Collection|Credential Access","Ajax Security Team|Operation Wocao|APT32|Sandworm Team|APT39|APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|OilRig|Ke3chang|PLATINUM|Sowbug|Magic Hound|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28" +"T1555","Credentials from Password Stores","Credential Access","APT29|Evilnum|UNC2452|FIN6|APT39|OilRig|MuddyWater|Leafminer|APT33|Stealth Falcon" +"T1552.005","Cloud Instance Metadata API","Credential Access","no" +"T1003.008","/etc/passwd and /etc/shadow","Credential Access","no" +"T1003.007","Proc Filesystem","Credential Access","no" +"T1003.006","DCSync","Credential Access","APT29|UNC2452|Operation Wocao" +"T1558.003","Kerberoasting","Credential Access","APT29|UNC2452|Operation Wocao|Wizard Spider" +"T1552.006","Group Policy Preferences","Credential Access","APT33" +"T1003.003","NTDS","Credential Access","Mustang Panda|HAFNIUM|Fox Kitten|menuPass|Wizard Spider|Chimera|FIN6|Dragonfly 2.0" +"T1003.002","Security Account Manager","Credential Access","Wizard Spider|Threat Group-3390|Ke3chang|GALLIUM|Night Dragon|Dragonfly 2.0|menuPass" +"T1003.001","LSASS Memory","Credential Access","HAFNIUM|Fox Kitten|Operation Wocao|Kimsuky|Sandworm Team|Whitefly|Blue Mockingbird|Silence|Threat Group-3390|Leviathan|APT41|GALLIUM|TEMP.Veles|APT33|APT39|Stolen Pencil|APT32|Leafminer|Magic Hound|Lazarus Group|MuddyWater|PLATINUM|FIN8|OilRig|BRONZE BUTLER|FIN6|APT3|APT28|APT1|Ke3chang|Cleaver" +"T1110.004","Credential Stuffing","Credential Access","Chimera" +"T1110.003","Password Spraying","Credential Access","Silent Librarian|Chimera|APT28|APT33|Leafminer|Lazarus Group" +"T1110.002","Password Cracking","Credential Access","FIN6|APT41|Dragonfly 2.0|APT3" +"T1110.001","Password Guessing","Credential Access","APT28" +"T1021.006","Windows Remote Management","Lateral Movement","APT29|UNC2452|Chimera|Wizard Spider|Threat Group-3390" +"T1021.005","VNC","Lateral Movement","Fox Kitten|GCMAN" +"T1021.004","SSH","Lateral Movement","Fox Kitten|Rocke|TEMP.Veles|Leviathan|APT39|OilRig|menuPass|GCMAN" +"T1021.003","Distributed Component Object Model","Lateral Movement","no" +"T1021.002","SMB/Windows Admin Shares","Lateral Movement","Fox Kitten|APT41|Operation Wocao|Wizard Spider|Chimera|Blue Mockingbird|APT39|APT32|Orangeworm|FIN8|APT3|Lazarus Group|Threat Group-1314|Turla|Deep Panda|Ke3chang" +"T1021.001","Remote Desktop Protocol","Lateral Movement","Fox Kitten|Chimera|Blue Mockingbird|Wizard Spider|Silence|APT41|TEMP.Veles|Leviathan|APT39|Stolen Pencil|Cobalt Group|Dragonfly 2.0|FIN8|APT3|OilRig|FIN10|menuPass|Patchwork|FIN6|Lazarus Group|APT1|Axiom" +"T1554","Compromise Client Software Binary","Persistence","no" +"T1036.006","Space after Filename","Defense Evasion","no" +"T1036.005","Match Legitimate Name or Location","Defense Evasion","APT29|Mustang Panda|Sidewinder|Darkhotel|Lazarus Group|Indrik Spider|UNC2452|Fox Kitten|Machete|Chimera|PROMETHIUM|Rocke|Sandworm Team|APT39|Blue Mockingbird|Whitefly|Tropic Trooper|Silence|APT41|menuPass|TEMP.Veles|MuddyWater|BRONZE BUTLER|Sowbug|APT32|Patchwork|Poseidon Group|admin@338|Carbanak|APT1" +"T1036.004","Masquerade Task or Service","Defense Evasion","ZIRCONIUM|APT29|Higaisa|UNC2452|Fox Kitten|Kimsuky|Lazarus Group|PROMETHIUM|Wizard Spider|APT-C-36|Carbanak|APT32|FIN6|FIN7" +"T1036.003","Rename System Utilities","Defense Evasion","menuPass|APT32|GALLIUM" +"T1036.002","Right-to-Left Override","Defense Evasion","BRONZE BUTLER|BlackTech|Ke3chang|Scarlet Mimic" +"T1036.001","Invalid Code Signature","Defense Evasion","Windshift|APT37" +"T1553.003","SIP and Trust Provider Hijacking","Defense Evasion","no" +"T1553.002","Code Signing","Defense Evasion","APT29|GALLIUM|UNC2452|Wizard Spider|Kimsuky|PROMETHIUM|Patchwork|Silence|APT41|FIN6|TA505|FIN7|Honeybee|Leviathan|CopyKittens|Winnti Group|Suckfly|Molerats|Darkhotel" +"T1553.001","Gatekeeper Bypass","Defense Evasion","no" +"T1553","Subvert Trust Controls","Defense Evasion","no" +"T1027.003","Steganography","Defense Evasion","TA551|BRONZE BUTLER|Tropic Trooper|MuddyWater|APT37" +"T1027.002","Software Packing","Defense Evasion","ZIRCONIUM|Lazarus Group|TA505|Rocke|GALLIUM|The White Company|APT39|APT38|Dark Caracal|Elderwood|APT3|Patchwork|APT29|Night Dragon" +"T1027.001","Binary Padding","Defense Evasion","Mustang Panda|Higaisa|Gamaredon Group|Patchwork|APT32|Leviathan|BRONZE BUTLER|Moafee" +"T1222.002","Linux and Mac File and Directory Permissions Modification","Defense Evasion","Rocke|APT32" +"T1222.001","Windows File and Directory Permissions Modification","Defense Evasion","Wizard Spider" +"T1552.004","Private Keys","Credential Access","APT29|UNC2452|Operation Wocao|Rocke" +"T1552.003","Bash History","Credential Access","no" +"T1552.002","Credentials in Registry","Credential Access","APT32" +"T1552.001","Credentials In Files","Credential Access","Fox Kitten|Leafminer|APT33|OilRig|TA505|Stolen Pencil|MuddyWater|APT3" +"T1552","Unsecured Credentials","Credential Access","no" +"T1216.001","PubPrn","Defense Evasion","APT32" +"T1070.006","Timestomp","Defense Evasion","APT29|UNC2452|Chimera|Kimsuky|Rocke|TEMP.Veles|APT32|Lazarus Group|APT28" +"T1070.005","Network Share Connection Removal","Defense Evasion","Threat Group-3390" +"T1070.004","File Deletion","Defense Evasion","APT39|Mustang Panda|Chimera|Evilnum|UNC2452|Operation Wocao|FIN6|Sandworm Team|Rocke|Tropic Trooper|Gamaredon Group|Wizard Spider|APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Patchwork|Honeybee|Cobalt Group|Dragonfly 2.0|menuPass|FIN8|OilRig|FIN5|BRONZE BUTLER|Magic Hound|APT3|Threat Group-3390|FIN10|APT28|Group5|Lazarus Group|APT18|APT29" +"T1070.003","Clear Command History","Defense Evasion","APT41" +"T1550.004","Web Session Cookie","Defense Evasion|Lateral Movement","APT29|UNC2452" +"T1550.001","Application Access Token","Defense Evasion|Lateral Movement","APT28" +"T1550.003","Pass the Ticket","Defense Evasion|Lateral Movement","APT32|BRONZE BUTLER|APT29" +"T1550.002","Pass the Hash","Defense Evasion|Lateral Movement","Chimera|Kimsuky|GALLIUM|APT32|Night Dragon|APT28|APT1" +"T1550","Use Alternate Authentication Material","Defense Evasion|Lateral Movement","APT29|UNC2452" +"T1548.004","Elevated Execution with Prompt","Privilege Escalation|Defense Evasion","no" +"T1548.003","Sudo and Sudo Caching","Privilege Escalation|Defense Evasion","no" +"T1548.002","Bypass User Account Control","Privilege Escalation|Defense Evasion","Evilnum|APT37|MuddyWater|Honeybee|Cobalt Group|Threat Group-3390|BRONZE BUTLER|Patchwork|APT29" +"T1548.001","Setuid and Setgid","Privilege Escalation|Defense Evasion","no" +"T1548","Abuse Elevation Control Mechanism","Privilege Escalation|Defense Evasion","no" +"T1136.003","Cloud Account","Persistence","no" +"T1070.002","Clear Linux or Mac System Logs","Defense Evasion","Rocke" +"T1070.001","Clear Windows Event Logs","Defense Evasion","Chimera|Operation Wocao|APT41|APT38|Dragonfly 2.0|APT32|FIN8|FIN5|APT28" +"T1136.002","Domain Account","Persistence","HAFNIUM|GALLIUM" +"T1136.001","Local Account","Persistence","Fox Kitten|APT39|APT41|Dragonfly 2.0|Leafminer|APT3" +"T1547.011","Plist Modification","Persistence|Privilege Escalation","no" +"T1547.010","Port Monitors","Persistence|Privilege Escalation","no" +"T1547.009","Shortcut Modification","Persistence|Privilege Escalation","APT39|Darkhotel|APT29|Gorgon Group|Dragonfly 2.0|Lazarus Group|Leviathan" +"T1547.008","LSASS Driver","Persistence|Privilege Escalation","no" +"T1547.007","Re-opened Applications","Persistence|Privilege Escalation","no" +"T1547.006","Kernel Modules and Extensions","Persistence|Privilege Escalation","no" +"T1547.005","Security Support Provider","Persistence|Privilege Escalation","Lazarus Group" +"T1547.004","Winlogon Helper DLL","Persistence|Privilege Escalation","Wizard Spider|Tropic Trooper|Turla" +"T1547.003","Time Providers","Persistence|Privilege Escalation","no" +"T1546.014","Emond","Privilege Escalation|Persistence","no" +"T1546.013","PowerShell Profile","Privilege Escalation|Persistence","Turla" +"T1546.012","Image File Execution Options Injection","Privilege Escalation|Persistence","TEMP.Veles" +"T1218.008","Odbcconf","Defense Evasion","Cobalt Group" +"T1546.011","Application Shimming","Privilege Escalation|Persistence","FIN7" +"T1547.002","Authentication Package","Persistence|Privilege Escalation","no" +"T1546.010","AppInit DLLs","Privilege Escalation|Persistence","APT39" +"T1546.009","AppCert DLLs","Privilege Escalation|Persistence","Honeybee" +"T1218.007","Msiexec","Defense Evasion","ZIRCONIUM|Molerats|Machete|TA505|Rancor" +"T1546.008","Accessibility Features","Privilege Escalation|Persistence","Fox Kitten|APT41|APT3|APT29|Deep Panda|Axiom" +"T1546.007","Netsh Helper DLL","Privilege Escalation|Persistence","no" +"T1546.006","LC_LOAD_DYLIB Addition","Privilege Escalation|Persistence","no" +"T1546.005","Trap","Privilege Escalation|Persistence","no" +"T1546.004","Unix Shell Configuration Modification","Privilege Escalation|Persistence","no" +"T1546.003","Windows Management Instrumentation Event Subscription","Privilege Escalation|Persistence","Mustang Panda|UNC2452|APT33|Blue Mockingbird|Turla|Leviathan|APT29" +"T1546.002","Screensaver","Privilege Escalation|Persistence","no" +"T1546.001","Change Default File Association","Privilege Escalation|Persistence","Kimsuky" +"T1547.001","Registry Run Keys / Startup Folder","Persistence|Privilege Escalation","Windshift|Mustang Panda|ZIRCONIUM|Higaisa|Sidewinder|APT28|Wizard Spider|PROMETHIUM|Rocke|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Silence|RTM|Inception|APT41|Kimsuky|APT33|APT39|APT32|APT18|Turla|APT19|Honeybee|Dark Caracal|Threat Group-3390|Cobalt Group|Ke3chang|Gorgon Group|Dragonfly 2.0|APT37|MuddyWater|Leviathan|APT3|BRONZE BUTLER|Magic Hound|FIN7|FIN10|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel" +"T1218.002","Control Panel","Defense Evasion","no" +"T1218.010","Regsvr32","Defense Evasion","TA551|Blue Mockingbird|Inception|WIRTE|APT19|Cobalt Group|Leviathan|APT32|Deep Panda" +"T1218.009","Regsvcs/Regasm","Defense Evasion","no" +"T1218.005","Mshta","Defense Evasion","Mustang Panda|TA551|Sidewinder|Lazarus Group|Inception|Kimsuky|APT32|MuddyWater|FIN7" +"T1218.004","InstallUtil","Defense Evasion","Mustang Panda|menuPass" +"T1218.001","Compiled HTML File","Defense Evasion","APT41|Silence|Lazarus Group|Dark Caracal|OilRig" +"T1218.003","CMSTP","Defense Evasion","Cobalt Group|MuddyWater" +"T1218.011","Rundll32","Defense Evasion","HAFNIUM|TA551|UNC2452|APT41|Gamaredon Group|APT32|Sandworm Team|Blue Mockingbird|TA505|MuddyWater|APT29|APT19|CopyKittens|APT3|Carbanak|APT28" +"T1547","Boot or Logon Autostart Execution","Persistence|Privilege Escalation","no" +"T1546","Event Triggered Execution","Privilege Escalation|Persistence","no" +"T1098.003","Add Office 365 Global Administrator Role","Persistence","no" +"T1098.002","Exchange Email Delegate Permissions","Persistence","APT29|UNC2452|Magic Hound" +"T1098.001","Additional Cloud Credentials","Persistence","APT29|UNC2452" +"T1543.004","Launch Daemon","Persistence|Privilege Escalation","no" +"T1543.003","Windows Service","Persistence|Privilege Escalation","PROMETHIUM|Blue Mockingbird|DarkVishnya|Wizard Spider|APT32|APT41|Kimsuky|Tropic Trooper|Cobalt Group|Ke3chang|FIN7|APT19|Honeybee|Threat Group-3390|APT3|Lazarus Group|Carbanak" +"T1543.002","Systemd Service","Persistence|Privilege Escalation","Rocke" +"T1543.001","Launch Agent","Persistence|Privilege Escalation","no" +"T1037.005","Startup Items","Persistence|Privilege Escalation","no" +"T1037.004","RC Scripts","Persistence|Privilege Escalation","no" +"T1055.012","Process Hollowing","Defense Evasion|Privilege Escalation","Threat Group-3390|Gorgon Group|menuPass|Patchwork" +"T1055.013","Process Doppelgänging","Defense Evasion|Privilege Escalation","Leafminer" +"T1055.011","Extra Window Memory Injection","Defense Evasion|Privilege Escalation","no" +"T1055.014","VDSO Hijacking","Defense Evasion|Privilege Escalation","no" +"T1055.009","Proc Memory","Defense Evasion|Privilege Escalation","no" +"T1055.008","Ptrace System Calls","Defense Evasion|Privilege Escalation","no" +"T1055.005","Thread Local Storage","Defense Evasion|Privilege Escalation","no" +"T1055.004","Asynchronous Procedure Call","Defense Evasion|Privilege Escalation","no" +"T1055.003","Thread Execution Hijacking","Defense Evasion|Privilege Escalation","no" +"T1055.002","Portable Executable Injection","Defense Evasion|Privilege Escalation","Rocke|Gorgon Group" +"T1055.001","Dynamic-link Library Injection","Defense Evasion|Privilege Escalation","Wizard Spider|TA505|Turla|Tropic Trooper|Lazarus Group|Putter Panda" +"T1037.003","Network Logon Script","Persistence|Privilege Escalation","no" +"T1543","Create or Modify System Process","Persistence|Privilege Escalation","no" +"T1037.002","Logon Script (Mac)","Persistence|Privilege Escalation","no" +"T1037.001","Logon Script (Windows)","Persistence|Privilege Escalation","Cobalt Group|APT28" +"T1542.003","Bootkit","Persistence|Defense Evasion","APT41|Lazarus Group|APT28" +"T1542.002","Component Firmware","Persistence|Defense Evasion","Equation" +"T1542.001","System Firmware","Persistence|Defense Evasion","no" +"T1505.003","Web Shell","Persistence","Sandworm Team|HAFNIUM|Volatile Cedar|Fox Kitten|Operation Wocao|Kimsuky|Tropic Trooper|GALLIUM|Threat Group-3390|TEMP.Veles|Leviathan|APT39|Dragonfly 2.0|APT32|OilRig|Deep Panda" +"T1505.002","Transport Agent","Persistence","no" +"T1505.001","SQL Stored Procedures","Persistence","no" +"T1053.003","Cron","Execution|Persistence|Privilege Escalation","Rocke" +"T1053.004","Launchd","Execution|Persistence|Privilege Escalation","no" +"T1053.001","At (Linux)","Execution|Persistence|Privilege Escalation","no" +"T1053.005","Scheduled Task","Execution|Persistence|Privilege Escalation","Mustang Panda|Higaisa|UNC2452|Fox Kitten|Molerats|Machete|Operation Wocao|Chimera|Gamaredon Group|Blue Mockingbird|MuddyWater|Wizard Spider|Frankenstein|APT-C-36|BRONZE BUTLER|APT41|GALLIUM|Silence|TEMP.Veles|APT33|APT39|Cobalt Group|Rancor|Dragonfly 2.0|OilRig|Patchwork|FIN8|menuPass|FIN10|FIN7|APT32|Stealth Falcon|FIN6|APT3|APT29" +"T1053.002","At (Windows)","Execution|Persistence|Privilege Escalation","BRONZE BUTLER|Threat Group-3390|APT18" +"T1542","Pre-OS Boot","Defense Evasion|Persistence","no" +"T1137.001","Office Template Macros","Persistence","MuddyWater" +"T1137.004","Outlook Home Page","Persistence","OilRig" +"T1137.003","Outlook Forms","Persistence","no" +"T1137.005","Outlook Rules","Persistence","no" +"T1137.006","Add-ins","Persistence","Naikon" +"T1137.002","Office Test","Persistence","APT28" +"T1531","Account Access Removal","Impact","no" +"T1539","Steal Web Session Cookie","Credential Access","Evilnum" +"T1529","System Shutdown/Reboot","Impact","Lazarus Group|APT38|APT37" +"T1518","Software Discovery","Discovery","Mustang Panda|Windshift|MuddyWater|Windigo|Sidewinder|Operation Wocao|BRONZE BUTLER|Tropic Trooper|Inception" +"T1547.013","XDG Autostart Entries","Persistence|Privilege Escalation","no" +"T1534","Internal Spearphishing","Lateral Movement","Gamaredon Group" +"T1528","Steal Application Access Token","Credential Access","APT28" +"T1535","Unused/Unsupported Cloud Regions","Defense Evasion","no" +"T1525","Implant Internal Image","Persistence","no" +"T1538","Cloud Service Dashboard","Discovery","no" +"T1530","Data from Cloud Storage Object","Collection","Fox Kitten" +"T1578","Modify Cloud Compute Infrastructure","Defense Evasion","no" +"T1537","Transfer Data to Cloud Account","Exfiltration","no" +"T1526","Cloud Service Discovery","Discovery","no" +"T1505","Server Software Component","Persistence","no" +"T1499","Endpoint Denial of Service","Impact","Sandworm Team" +"T1497","Virtualization/Sandbox Evasion","Defense Evasion|Discovery","Darkhotel" +"T1498","Network Denial of Service","Impact","APT28" +"T1496","Resource Hijacking","Impact","Blue Mockingbird|Rocke|APT41|Lazarus Group" +"T1495","Firmware Corruption","Impact","no" +"T1491","Defacement","Impact","no" +"T1490","Inhibit System Recovery","Impact","no" +"T1489","Service Stop","Impact","Wizard Spider|Lazarus Group" +"T1486","Data Encrypted for Impact","Impact","Indrik Spider|APT41|TA505|APT38" +"T1485","Data Destruction","Impact","Sandworm Team|Lazarus Group|APT38" +"T1484","Domain Policy Modification","Defense Evasion|Privilege Escalation","no" +"T1482","Domain Trust Discovery","Discovery","APT29|Chimera|UNC2452" +"T1480","Execution Guardrails","Defense Evasion","no" +"T1222","File and Directory Permissions Modification","Defense Evasion","no" +"T1220","XSL Script Processing","Defense Evasion","Higaisa|Cobalt Group" +"T1221","Template Injection","Defense Evasion","Gamaredon Group|Frankenstein|Inception|APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus" +"T1189","Drive-by Compromise","Initial Access","Machete|Windigo|Dragonfly|PROMETHIUM|Turla|Windshift|RTM|Darkhotel|APT38|Dragonfly 2.0|Leafminer|Lazarus Group|BRONZE BUTLER|APT19|APT32|Threat Group-3390|Dark Caracal|Elderwood|APT37|Patchwork|PLATINUM" +"T1190","Exploit Public-Facing Application","Initial Access","Volatile Cedar|UNC2452|Fox Kitten|Operation Wocao|APT28|APT29|GOLD SOUTHFIELD|Blue Mockingbird|Rocke|APT39|BlackTech|APT41|GALLIUM|Night Dragon|Axiom" +"T1210","Exploitation of Remote Services","Lateral Movement","Fox Kitten|menuPass|Wizard Spider|Threat Group-3390|APT28" +"T1217","Browser Bookmark Discovery","Discovery","Chimera|Fox Kitten" +"T1213","Data from Information Repositories","Collection","Fox Kitten|FIN6|Turla" +"T1197","BITS Jobs","Defense Evasion|Persistence","APT39|Patchwork|APT41|Leviathan" +"T1219","Remote Access Software","Command And Control","Mustang Panda|MuddyWater|Evilnum|GOLD SOUTHFIELD|Sandworm Team|DarkVishnya|RTM|Kimsuky|Night Dragon|Thrip|Cobalt Group|Carbanak" +"T1195","Supply Chain Compromise","Initial Access","no" +"T1204","User Execution","Execution","no" +"T1212","Exploitation for Credential Access","Credential Access","no" +"T1211","Exploitation for Defense Evasion","Defense Evasion","APT28" +"T1200","Hardware Additions","Initial Access","DarkVishnya" +"T1202","Indirect Command Execution","Defense Evasion","no" +"T1201","Password Policy Discovery","Discovery","Chimera|Turla|OilRig" +"T1207","Rogue Domain Controller","Defense Evasion","no" +"T1203","Exploitation for Client Execution","Execution","Mustang Panda|Darkhotel|Higaisa|HAFNIUM|Sidewinder|Sandworm Team|MuddyWater|Frankenstein|Inception|BlackTech|APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|BRONZE BUTLER|Lazarus Group|Cobalt Group|APT37|Patchwork|APT29|TA459|Leviathan|Elderwood" +"T1216","Signed Script Proxy Execution","Defense Evasion","no" +"T1199","Trusted Relationship","Initial Access","Sandworm Team|GOLD SOUTHFIELD|APT28|menuPass" +"T1218","Signed Binary Proxy Execution","Defense Evasion","no" +"T1205","Traffic Signaling","Defense Evasion|Persistence|Command And Control","no" +"T1176","Browser Extensions","Persistence","Kimsuky|Stolen Pencil" +"T1175","Component Object Model and Distributed COM","Lateral Movement|Execution","no" +"T1187","Forced Authentication","Credential Access","DarkHydrus|Dragonfly 2.0" +"T1185","Man in the Browser","Collection","no" +"T1149","LC_MAIN Hijacking","Defense Evasion","no" +"T1134","Access Token Manipulation","Defense Evasion|Privilege Escalation","FIN6|Blue Mockingbird" +"T1136","Create Account","Persistence","no" +"T1137","Office Application Startup","Persistence","Gamaredon Group|APT32" +"T1140","Deobfuscate/Decode Files or Information","Defense Evasion","APT39|APT29|ZIRCONIUM|Higaisa|UNC2452|Rocke|Sandworm Team|Gamaredon Group|Molerats|Frankenstein|Turla|WIRTE|Darkhotel|Tropic Trooper|Gorgon Group|menuPass|Honeybee|Threat Group-3390|APT19|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER" +"T1135","Network Share Discovery","Discovery","Chimera|Operation Wocao|Wizard Spider|APT32|APT39|DarkVishnya|APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug" +"T1153","Source","Execution","no" +"T1133","External Remote Services","Persistence|Initial Access","APT29|UNC2452|Operation Wocao|Wizard Spider|Kimsuky|GOLD SOUTHFIELD|Chimera|Sandworm Team|APT41|GALLIUM|TEMP.Veles|Night Dragon|OilRig|Dragonfly 2.0|Ke3chang|FIN5|Threat Group-3390|APT18" +"T1132","Data Encoding","Command And Control","no" +"T1129","Shared Modules","Execution","no" +"T1127","Trusted Developer Utilities Proxy Execution","Defense Evasion","no" +"T1125","Video Capture","Collection","Silence|FIN7" +"T1124","System Time Discovery","Discovery","Darkhotel|ZIRCONIUM|Higaisa|Sidewinder|Chimera|Operation Wocao|The White Company|Lazarus Group|BRONZE BUTLER|Turla" +"T1123","Audio Capture","Collection","APT37" +"T1120","Peripheral Device Discovery","Discovery","Operation Wocao|Turla|APT37|Gamaredon Group|Equation|APT28" +"T1119","Automated Collection","Collection","Mustang Panda|Sidewinder|Chimera|menuPass|Operation Wocao|Gamaredon Group|Tropic Trooper|Frankenstein|APT1|APT28|Patchwork|OilRig|FIN5|Threat Group-3390|FIN6" +"T1115","Clipboard Data","Collection","Operation Wocao|APT39|APT38" +"T1114","Email Collection","Collection","Silent Librarian" +"T1113","Screen Capture","Collection","GOLD SOUTHFIELD|Gamaredon Group|APT39|Silence|MuddyWater|OilRig|Dragonfly 2.0|Dark Caracal|FIN7|BRONZE BUTLER|Magic Hound|Group5|APT28" +"T1112","Modify Registry","Defense Evasion","Operation Wocao|Kimsuky|Lazarus Group|Gamaredon Group|Blue Mockingbird|Wizard Spider|Silence|APT41|Turla|APT32|APT38|Dragonfly 2.0|APT19|Threat Group-3390|Patchwork|Gorgon Group|Honeybee|FIN8" +"T1111","Two-Factor Authentication Interception","Credential Access","Chimera|Operation Wocao" +"T1110","Brute Force","Credential Access","APT28|Fox Kitten|DarkVishnya|APT39|OilRig|FIN5|Turla" +"T1108","Redundant Access","Defense Evasion|Persistence","no" +"T1106","Native API","Execution","Higaisa|menuPass|Operation Wocao|Chimera|Gamaredon Group|Tropic Trooper|Sharpshooter|Turla|Silence|APT37|Gorgon Group" +"T1105","Ingress Tool Transfer","Command And Control","HAFNIUM|APT29|Ajax Security Team|Mustang Panda|Windshift|Darkhotel|ZIRCONIUM|TA551|Volatile Cedar|Indrik Spider|Evilnum|Sidewinder|UNC2452|Fox Kitten|Kimsuky|Operation Wocao|Chimera|Sandworm Team|Whitefly|Rocke|APT39|Tropic Trooper|Sharpshooter|Molerats|Frankenstein|Silence|APT-C-36|APT41|GALLIUM|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Rancor|Cobalt Group|Gorgon Group|Turla|OilRig|Dragonfly 2.0|APT37|Leviathan|FIN8|PLATINUM|Elderwood|APT3|Magic Hound|APT32|BRONZE BUTLER|FIN7|menuPass|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28" +"T1104","Multi-Stage Channels","Command And Control","APT41|MuddyWater|APT3" +"T1102","Web Service","Command And Control","Fox Kitten|Turla|APT32|Gamaredon Group|Rocke|Inception|FIN6" +"T1098","Account Manipulation","Persistence","APT3|Dragonfly 2.0|Lazarus Group" +"T1095","Non-Application Layer Protocol","Command And Control","HAFNIUM|Operation Wocao|FIN6|APT29|PLATINUM|APT3" +"T1092","Communication Through Removable Media","Command And Control","APT28" +"T1091","Replication Through Removable Media","Lateral Movement|Initial Access","Mustang Panda|Tropic Trooper|Darkhotel|APT28" +"T1090","Proxy","Command And Control","Windigo|Fox Kitten|Operation Wocao|Sandworm Team|Blue Mockingbird|APT41|Turla" +"T1087","Account Discovery","Discovery","APT29|UNC2452" +"T1083","File and Directory Discovery","Discovery","APT29|Mustang Panda|Darkhotel|Windigo|Sidewinder|Chimera|UNC2452|Fox Kitten|menuPass|APT39|Sandworm Team|Operation Wocao|Gamaredon Group|Tropic Trooper|Inception|APT41|Kimsuky|APT32|MuddyWater|APT18|Dragonfly 2.0|Leafminer|Honeybee|Dark Caracal|Magic Hound|APT3|BRONZE BUTLER|Sowbug|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang" +"T1082","System Information Discovery","Discovery","APT29|Mustang Panda|Windshift|ZIRCONIUM|Higaisa|Windigo|Sidewinder|UNC2452|Chimera|Operation Wocao|Wizard Spider|Rocke|Sandworm Team|Blue Mockingbird|Tropic Trooper|Frankenstein|Inception|Kimsuky|Darkhotel|MuddyWater|APT18|APT37|APT19|Honeybee|APT32|Magic Hound|Sowbug|OilRig|APT3|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang" +"T1080","Taint Shared Content","Lateral Movement","Gamaredon Group|BRONZE BUTLER|Darkhotel" +"T1078","Valid Accounts","Defense Evasion|Persistence|Privilege Escalation|Initial Access","APT29|Silent Librarian|UNC2452|Fox Kitten|Operation Wocao|Chimera|Sandworm Team|Wizard Spider|Silence|APT41|GALLIUM|TEMP.Veles|APT39|FIN4|Night Dragon|Dragonfly 2.0|FIN8|APT33|Leviathan|OilRig|FIN5|menuPass|FIN10|APT28|Suckfly|FIN6|Threat Group-3390|APT18|PittyTiger|Carbanak" +"T1074","Data Staged","Collection","Wizard Spider" +"T1072","Software Deployment Tools","Execution|Lateral Movement","Silence|APT32|Threat Group-1314" +"T1071","Application Layer Protocol","Command And Control","Rocke|Magic Hound|Dragonfly 2.0" +"T1070","Indicator Removal on Host","Defense Evasion","APT29|UNC2452" +"T1069","Permission Groups Discovery","Discovery","APT29|UNC2452|TA505|APT3" +"T1068","Exploitation for Privilege Escalation","Privilege Escalation","ZIRCONIUM|Turla|Whitefly|APT33|Cobalt Group|PLATINUM|FIN8|APT32|Threat Group-3390|FIN6|APT28" +"T1064","Scripting","Defense Evasion|Execution","no" +"T1062","Hypervisor","Persistence","no" +"T1061","Graphical User Interface","Execution","no" +"T1059","Command and Scripting Interpreter","Execution","Windigo|Fox Kitten|APT32|Whitefly|APT39|Dragonfly 2.0|APT19|FIN7|OilRig|FIN5|Stealth Falcon|FIN6|Ke3chang" +"T1057","Process Discovery","Discovery","APT29|Mustang Panda|Windshift|Higaisa|Sidewinder|Chimera|UNC2452|Operation Wocao|Rocke|Frankenstein|Inception|Darkhotel|MuddyWater|APT1|APT38|Tropic Trooper|APT37|Honeybee|OilRig|APT3|Magic Hound|APT28|Winnti Group|Stealth Falcon|Poseidon Group|Lazarus Group|Molerats|Turla|Deep Panda|Ke3chang" +"T1056","Input Capture","Collection|Credential Access","APT39" +"T1055","Process Injection","Defense Evasion|Privilege Escalation","Operation Wocao|APT32|Sharpshooter|Silence|APT41|Kimsuky|Turla|Cobalt Group|APT37|Honeybee|PLATINUM" +"T1053","Scheduled Task/Job","Execution|Persistence|Privilege Escalation","no" +"T1052","Exfiltration Over Physical Medium","Exfiltration","no" +"T1051","Shared Webroot","Lateral Movement","no" +"T1049","System Network Connections Discovery","Discovery","Mustang Panda|MuddyWater|Chimera|Sandworm Team|Operation Wocao|Tropic Trooper|APT41|APT38|GALLIUM|APT32|APT1|APT3|OilRig|menuPass|Threat Group-3390|Poseidon Group|admin@338|Turla|Ke3chang" +"T1048","Exfiltration Over Alternative Protocol","Exfiltration","no" +"T1047","Windows Management Instrumentation","Execution","Mustang Panda|Windshift|UNC2452|Operation Wocao|Chimera|Blue Mockingbird|Wizard Spider|Frankenstein|APT41|FIN6|GALLIUM|APT32|MuddyWater|OilRig|Threat Group-3390|Leviathan|FIN8|menuPass|Stealth Falcon|Lazarus Group|APT29|Deep Panda" +"T1046","Network Service Scanning","Discovery","Chimera|Fox Kitten|Operation Wocao|Rocke|DarkVishnya|APT41|Tropic Trooper|APT39|APT32|OilRig|Leafminer|Cobalt Group|menuPass|Suckfly|FIN6|Threat Group-3390" +"T1043","Commonly Used Port","Command And Control","OilRig|APT28|TEMP.Veles|Night Dragon|APT29|APT18|APT19|FIN7|Dragonfly 2.0|FIN8|APT37|Magic Hound|APT3|Lazarus Group|Threat Group-3390" +"T1041","Exfiltration Over C2 Channel","Exfiltration","ZIRCONIUM|Higaisa|Chimera|APT39|Operation Wocao|Sandworm Team|MuddyWater|Wizard Spider|Frankenstein|Kimsuky|GALLIUM|APT32|APT3|Gamaredon Group|Stealth Falcon|Lazarus Group|Ke3chang" +"T1040","Network Sniffing","Credential Access|Discovery","Kimsuky|Sandworm Team|DarkVishnya|APT33|Stolen Pencil|APT28" +"T1039","Data from Network Shared Drive","Collection","Chimera|Fox Kitten|Gamaredon Group|Sowbug|BRONZE BUTLER|menuPass" +"T1037","Boot or Logon Initialization Scripts","Persistence|Privilege Escalation","Rocke" +"T1036","Masquerading","Defense Evasion","APT29|Mustang Panda|ZIRCONIUM|TA551|UNC2452|Windshift|APT32|BRONZE BUTLER|menuPass|PLATINUM|Dragonfly 2.0" +"T1034","Path Interception","Persistence|Privilege Escalation","no" +"T1033","System Owner/User Discovery","Discovery","Windshift|ZIRCONIUM|Sidewinder|Chimera|Sandworm Team|Operation Wocao|Wizard Spider|Frankenstein|APT41|GALLIUM|Tropic Trooper|APT39|MuddyWater|APT32|APT37|APT19|Dragonfly 2.0|OilRig|Magic Hound|FIN10|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|APT3" +"T1030","Data Transfer Size Limits","Exfiltration","Threat Group-3390" +"T1029","Scheduled Transfer","Exfiltration","Higaisa" +"T1027","Obfuscated Files or Information","Defense Evasion","APT39|Mustang Panda|Windshift|TA551|Higaisa|Sidewinder|UNC2452|Fox Kitten|GOLD SOUTHFIELD|Operation Wocao|Kimsuky|FIN6|Chimera|Gamaredon Group|Rocke|Sandworm Team|Blue Mockingbird|Whitefly|Molerats|Wizard Spider|Mofang|Frankenstein|Inception|APT-C-36|APT41|GALLIUM|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|Patchwork|APT37|Honeybee|menuPass|Leafminer|Cobalt Group|Threat Group-3390|Dark Caracal|APT19|FIN8|BlackOasis|MuddyWater|Elderwood|Leviathan|FIN7|Magic Hound|OilRig|APT3|APT32|Group5|Dust Storm|Lazarus Group|Putter Panda|APT28" +"T1026","Multiband Communication","Command And Control","Lazarus Group" +"T1025","Data from Removable Media","Collection","Turla|Gamaredon Group|APT28" +"T1021","Remote Services","Lateral Movement","no" +"T1020","Automated Exfiltration","Exfiltration","Sidewinder|Gamaredon Group|Tropic Trooper|Frankenstein|Honeybee" +"T1018","Remote System Discovery","Discovery","APT29|UNC2452|Chimera|Fox Kitten|Operation Wocao|Sandworm Team|Rocke|Wizard Spider|Silence|GALLIUM|APT39|APT32|Dragonfly 2.0|Deep Panda|Threat Group-3390|Leafminer|Ke3chang|FIN8|FIN5|APT3|BRONZE BUTLER|menuPass|FIN6|Turla" +"T1016","System Network Configuration Discovery","Discovery","ZIRCONIUM|Mustang Panda|Higaisa|Sidewinder|Chimera|Operation Wocao|Wizard Spider|Sandworm Team|Tropic Trooper|Frankenstein|APT41|GALLIUM|APT32|Darkhotel|MuddyWater|APT1|APT19|Dragonfly 2.0|OilRig|Magic Hound|menuPass|Threat Group-3390|Stealth Falcon|Lazarus Group|APT3|Naikon|admin@338|Turla|Ke3chang" +"T1014","Rootkit","Defense Evasion","Rocke|APT41|APT28|Winnti Group" +"T1012","Query Registry","Discovery","ZIRCONIUM|Chimera|Fox Kitten|APT39|Operation Wocao|APT32|Dragonfly 2.0|Threat Group-3390|OilRig|Stealth Falcon|Lazarus Group|Turla" +"T1011","Exfiltration Over Other Network Medium","Exfiltration","no" +"T1010","Application Window Discovery","Discovery","Lazarus Group" +"T1008","Fallback Channels","Command And Control","Carbanak|APT41|OilRig|Lazarus Group" +"T1007","System Service Discovery","Discovery","Chimera|Operation Wocao|BRONZE BUTLER|APT1|OilRig|Poseidon Group|admin@338|Turla|Ke3chang" +"T1006","Direct Volume Access","Defense Evasion","no" +"T1005","Data from Local System","Collection","APT29|Windigo|UNC2452|Fox Kitten|Sandworm Team|Operation Wocao|FIN6|Gamaredon Group|APT39|Frankenstein|Inception|Kimsuky|GALLIUM|Turla|menuPass|Dragonfly 2.0|Dark Caracal|Honeybee|APT37|APT28|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang" +"T1003","OS Credential Dumping","Credential Access","APT39|Frankenstein|APT32|APT28|Leviathan|Sowbug|Suckfly|Poseidon Group|Axiom" +"T1001","Data Obfuscation","Command And Control","Operation Wocao|Axiom"