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# Automatically generated by generator.py in splunk/security_content
# On Date: 2021-03-12T17:19:06 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
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### STORIES ###

[analytic_story://AWS Cross Account Activity]
category = Cloud Security
last_updated = 2018-06-04
version = 1
references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - aws detect sts get session token abuse - Rule", "ESCU - aws detect attach to role policy - Rule", "ESCU - aws detect permanent key creation - Rule", "ESCU - aws detect sts assume role abuse - Rule", "ESCU - aws detect role creation - 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.\
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.

[analytic_story://AWS Cryptomining]
category = Cloud Security
last_updated = 2018-03-08
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Investigate AWS activities via region name - Response Task", "ESCU - Get EC2 Launch Details - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get EC2 Instance Details by instanceId - Response Task"]
description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 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). 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 EC2 environment to help prevent such a disaster. 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.

[analytic_story://AWS Network ACL Activity]
category = Cloud Security
last_updated = 2018-05-21
version = 2
references = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - AWS Network Interface details via resourceId - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - AWS Network ACL Details from ID - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task"]
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.

[analytic_story://AWS Security Hub Alerts]
category = Cloud Security
last_updated = 2020-08-04
version = 1
references = ["https://aws.amazon.com/security-hub/features/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule", "ESCU - Detect Spike in AWS Security Hub Alerts for User - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get EC2 Launch Details - Response Task", "ESCU - Get EC2 Instance Details by instanceId - Response Task"]
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.

[analytic_story://AWS Suspicious Provisioning Activities]
category = Cloud Security
last_updated = 2018-03-16
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - AWS Investigate Security Hub alerts by dest - Response Task", "ESCU - Get All AWS Activity From Region - Response Task", "ESCU - Get All AWS Activity From Country - Response Task", "ESCU - Get All AWS Activity From City - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network.
narrative = Because most enterprise AWS 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.

[analytic_story://AWS User Monitoring]
category = Cloud Security
last_updated = 2018-03-12
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Investigate AWS User Activities by user field - Response Task", "ESCU - Get Notable History - 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.\
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.

[analytic_story://Apache Struts Vulnerability]
category = Vulnerability
last_updated = 2018-12-06
version = 1
references = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Investigate Web POSTs From src - Response Task", "ESCU - Investigate Suspicious Strings in HTTP Header - Response Task", "ESCU - Get Notable History - 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. \
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.\
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.

[analytic_story://Asset Tracking]
category = Best Practices
last_updated = 2017-09-13
version = 1
references = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address - Response Task", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Baron Samedit CVE-2021-3156]
category = Adversary Tactics
last_updated = 2021-01-27
version = 1
references = ["https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit"]
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.

[analytic_story://Brand Monitoring]
category = Abuse
last_updated = 2017-12-19
version = 1
references = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["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 Process Responsible For The DNS Traffic - Response Task", "ESCU - Get Notable History - 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.\
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.

[analytic_story://Cloud Cryptomining]
category = Cloud Security
last_updated = 2019-10-02
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Abnormally High Number Of Cloud Instances Launched - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Get Notable History - Response Task", "ESCU - AWS Investigate Security Hub alerts by dest - Response Task", "ESCU - Investigate AWS activities via region name - Response Task", "ESCU - Get EC2 Launch Details - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get EC2 Instance Details by instanceId - 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. \
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.

[analytic_story://Cloud Federated Credential Abuse]
category = Cloud Security
last_updated = 2021-01-26
version = 1
references = ["https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps", "https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf", "https://us-cert.cisa.gov/ncas/alerts/aa21-008a"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
searches = ["ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - O365 Excessive SSO logon errors - Rule", "ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule", "ESCU - O365 Added Service Principal - Rule", "ESCU - AWS SAML Access by Provider User and Principal - Rule", "ESCU - O365 New Federated Domain Added - Rule", "ESCU - Certutil exe certificate extraction - Rule", "ESCU - O365 Add App Role Assignment Grant User - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - AWS SAML Update identity provider - 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.
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]
category = Adversary Tactics
last_updated = 2021-02-16
version = 1
references = ["https://www.cobaltstrike.com/", "https://www.infocyte.com/blog/2020/09/02/cobalt-strike-the-new-favorite-among-thieves/", "https://bluescreenofjeff.com/2017-01-24-how-to-write-malleable-c2-profiles-for-cobalt-strike/", "https://blog.talosintelligence.com/2020/09/coverage-strikes-back-cobalt-strike-paper.html", "https://www.fireeye.com/blog/threat-research/2020/12/unauthorized-access-of-fireeye-red-team-tools.html", "https://github.com/MichaelKoczwara/Awesome-CobaltStrike-Defence", "https://github.com/zer0yu/Awesome-CobaltStrike"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}]
spec_version = 3
searches = ["ESCU - Detect Regsvr32 Application Control Bypass - Rule", "ESCU - Suspicious SearchProtocolHost no Command Line Arguments - Rule", "ESCU - Suspicious MSBuild Rename - Rule", "ESCU - Suspicious DLLHost no Command Line Arguments - Rule", "ESCU - Suspicious Rundll32 no Command Line Arguments - Rule", "ESCU - Suspicious msbuild path - Rule", "ESCU - Suspicious microsoft workflow compiler rename - Rule", "ESCU - Suspicious Rundll32 StartW - Rule", "ESCU - Cobalt Strike Named Pipes - Rule", "ESCU - Suspicious GPUpdate no Command Line Arguments - Rule"]
description = Cobalt Strike is threat emulation software. Red teams and penetration testers use Cobalt Strike to demonstrate the risk of a breach and evaluate mature security programs. Most recently, Cobalt Strike has become the choice tool by threat groups due to its ease of use and extensibility.
narrative = This Analytic Story supports you to detect Tactics, Techniques and Procedures (TTPs) from Cobalt Strike. Cobalt Strike has many ways to be enhanced by using aggressor scripts, malleable C2 profiles, default attack packages, and much more. For endpoint behavior, Cobalt Strike is most commonly identified via named pipes, spawn to processes, and DLL function names. Many additional variables are provided for in memory operation of the beacon implant. On the network, depending on the malleable C2 profile used, it is near infinite in the amount of ways to conceal the C2 traffic with Cobalt Strike. Not every query may be specific to Cobalt Strike the tool, but the methodologies and techniques used by it.\
Splunk Threat Research reviewed all publicly available instances of Malleabe C2 Profiles and generated a list of the most commonly used spawnto and pipenames.\
`Spawnto_x86` and `spawnto_x64` is the process that Cobalt Strike will spawn and injects shellcode into.\
Pipename sets the named pipe name used in Cobalt Strikes Beacon SMB C2 traffic.\
With that, new detections were generated focused on these spawnto processes spawning without command line arguments. Similar, the named pipes most commonly used by Cobalt Strike added as a detection. In generating content for Cobalt Strike, the following is considered:\
- Is it normal for spawnto_ value to have no command line arguments? No command line arguments and a network connection?\
- What is the default, or normal, process lineage for spawnto_ value?\
- 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.

[analytic_story://ColdRoot MacOS RAT]
category = Malware
last_updated = 2019-01-09
version = 1
references = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Jose Hernandez"}]
spec_version = 3
searches = ["ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Investigate Network Traffic From src ip - Response Task", "ESCU - Get Notable History - 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.\
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]
category = Adversary Tactics
last_updated = 2020-02-03
version = 1
references = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. 
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.

[analytic_story://Command and Control]
category = Adversary Tactics
last_updated = 2018-06-01
version = 1
references = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - AWS Network Interface details via resourceId - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - AWS Network ACL Details from ID - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task"]
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.

[analytic_story://Common Phishing Frameworks]
category = Adversary Tactics
last_updated = 2019-04-29
version = 1
references = ["https://github.com/kgretzky/evilginx2", "https://attack.mitre.org/techniques/T1192/", "https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}]
spec_version = 3
searches = ["ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule", "ESCU - Get Certificate logs for a domain - Response Task", "ESCU - Domain Certificate Investigation - Response Task"]
description = Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. 
narrative = 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. Because phishing is a technique that relies on human psychology, you will never be able to eliminate this vulnerability 100%. But you can use automated detection to significantly reduce the risks.\
This Analytic Story focuses on detecting signs of MiTM attacks enabled by [EvilGinx2](https://github.com/kgretzky/evilginx2), a toolkit that sets up a transparent proxy between the targeted site and the user. In this way, the attacker is able to intercept credentials and two-factor identification tokens. It employs a proxy template to allow a registered domain to impersonate targeted sites, such as Linkedin, Amazon, Okta, Github, Twitter, Instagram, Reddit, Office 365, and others. It can even register SSL certificates and camouflage them via a URL shortener, making them difficult to detect. Searches in this story look for signs of MiTM attacks enabled by EvilGinx2.

[analytic_story://Container Implantation Monitoring and Investigation]
category = Cloud Security
last_updated = 2020-02-20
version = 1
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.
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]
category = Adversary Tactics
last_updated = 2020-02-04
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 - Dump LSASS via procdump Rename - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Dump LSASS via procdump - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Ntdsutil Export NTDS - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Creation of lsass Dump with Taskmgr - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations - Response Task", "ESCU - Investigate Previous Unseen User - Response Task", "ESCU - Investigate Pass the Hash Attempts - Response Task", "ESCU - Investigate Pass the Ticket Attempts - 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.\
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.

[analytic_story://DHS Report TA18-074A]
category = Malware
last_updated = 2020-01-22
version = 2
references = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Scheduled Task Deleted Or Created via CMD - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Process File Activity - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Parent Process Info - 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. \
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.

[analytic_story://DNS Amplification Attacks]
category = Abuse
last_updated = 2016-09-13
version = 1
references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["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.

[analytic_story://DNS Hijacking]
category = Adversary Tactics
last_updated = 2020-02-04
version = 1
references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - DNS Hijack Enrichment - Response Task"]
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: \
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. 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.

[analytic_story://Data Exfiltration]
category = Adversary Tactics
last_updated = 2020-10-21
version = 1
references = ["https://attack.mitre.org/tactics/TA0010/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Shannon Davis"}]
spec_version = 3
searches = ["ESCU - Detect SNICat SNI Exfiltration - Rule", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Data Protection]
category = Abuse
last_updated = 2017-09-14
version = 1
references = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task"]
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.

[analytic_story://Detect Zerologon Attack]
category = Adversary Tactics
last_updated = 2020-09-18
version = 1
references = ["https://attack.mitre.org/wiki/Technique/T1003", "https://github.com/SecuraBV/CVE-2020-1472", "https://www.secura.com/blog/zero-logon", "https://nvd.nist.gov/vuln/detail/CVE-2020-1472"]
maintainers = [{"company": "Jose Hernandez, Stan Miskowicz, David Dorsey, Shannon Davis Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
searches = ["ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Detect Computer Changed with Anonymous Account - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Zerologon via Zeek - Rule", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Disabling Security Tools]
category = Adversary Tactics
last_updated = 2020-02-04
version = 2
references = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
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).

[analytic_story://Dynamic DNS]
category = Malware
last_updated = 2018-09-06
version = 2
references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Notable History - 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.

[analytic_story://Emotet Malware  DHS Report TA18-201A ]
category = Malware
last_updated = 2020-01-27
version = 1
references = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get History Of Email Sources - Response Task"]
description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment.
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. 

[analytic_story://F5 TMUI RCE CVE-2020-5902]
category = Adversary Tactics
last_updated = 2020-08-02
version = 1
references = ["https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/", "https://support.f5.com/csp/article/K52145254", "https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Shannon Davis"}]
spec_version = 3
searches = ["ESCU - Detect F5 TMUI RCE CVE-2020-5902 - Rule", "ESCU - Get Notable History - Response Task"]
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/)

[analytic_story://GCP Cross Account Activity]
category = Cloud Security
last_updated = 2020-09-01
version = 1
references = ["https://cloud.google.com/iam/docs/understanding-service-accounts"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
searches = ["ESCU - GCP Detect accounts with high risk roles by project - Rule", "ESCU - GCP Detect high risk permissions by resource and account - Rule", "ESCU - gcp detect oauth token abuse - Rule", "ESCU - GCP Detect gcploit framework - Rule", "ESCU - Get Notable History - Response Task"]
description = Track when a user assumes an IAM role in another GCP account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity.
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.

[analytic_story://HAFNIUM Group]
category = Adversary Tactics
last_updated = 2021-03-03
version = 1
references = ["https://www.splunk.com/en_us/blog/security/detecting-hafnium-exchange-server-zero-day-activity-in-splunk.html", "https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/", "https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/", "https://blog.rapid7.com/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}]
spec_version = 3
searches = ["ESCU - Dump LSASS via procdump Rename - Rule", "ESCU - Detect Exchange Web Shell - Rule", "ESCU - Dump LSASS via procdump - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Ntdsutil Export NTDS - Rule", "ESCU - Unified Messaging Service Spawning a Process - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Nishang PowershellTCPOneLine - Rule", "ESCU - Any Powershell DownloadString - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - W3WP Spawning Shell - Rule"]
description = HAFNIUM group was identified by Microsoft as exploiting 4 Microsoft Exchange CVEs in the wild - CVE-2021-26855, CVE-2021-26857, CVE-2021-26858 and CVE-2021-27065.
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.

[analytic_story://Hidden Cobra Malware]
category = Malware
last_updated = 2020-01-22
version = 2
references = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Investigate Successful Remote Desktop Authentications - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Process Responsible For The DNS Traffic - 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.

[analytic_story://Host Redirection]
category = Abuse
last_updated = 2017-09-14
version = 1
references = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Windows hosts file modification - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get Notable History - Response Task"]
description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website.
narrative = Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications.

[analytic_story://JBoss Vulnerability]
category = Vulnerability
last_updated = 2017-09-14
version = 1
references = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Get Notable History - Response Task"]
description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others.
narrative = This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\
It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\
When looking at the target of the behavior uncovered 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 identify other recent events involving the target. This can help tie different events together and give further situational awareness regarding the target host.\
Various types of information for external systems should be reviewed and, potentially, collected if the incident is, indeed, judged to be malicious. This data may be useful for generating your own threat intelligence, so you can create future alerts.\
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 qualify and understand the event and possible motivation for the attack. In addition, there are various sources that may provide reputation information on the IP address or domain name, which can assist you in determining whether the event is malicious in nature. Finally, determining whether there are other events associated with the IP address may help connect data points or expose other historic events that might be brought back into scope.\
Gathering various data on the system of interest can sometimes help quickly determine whether something suspicious is happening. Some of these items include determining who else may have logged into the system recently, whether any unusual scheduled tasks exist, whether the system is communicating on suspicious ports, whether there are modifications to sensitive registry keys, and/or 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 it is vulnerable to a specific exploit.\
If you suspect 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.\
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.

[analytic_story://Kubernetes Scanning Activity]
category = Cloud Security
last_updated = 2020-04-15
version = 1
references = ["https://github.com/splunk/cloud-datamodel-security-research"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
searches = ["ESCU - Kubernetes Azure scan fingerprint - Rule", "ESCU - GCP Kubernetes cluster pod scan detection - Rule", "ESCU - Kubernetes Azure pod scan fingerprint - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes activity by src ip - Response Task", "ESCU - Amazon EKS Kubernetes activity by src ip - Response Task", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Kubernetes Sensitive Object Access Activity]
category = Cloud Security
last_updated = 2020-05-20
version = 1
references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
searches = ["ESCU - Kubernetes GCP detect sensitive object access - Rule", "ESCU - Kubernetes GCP detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes Azure detect sensitive object access - Rule", "ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule", "ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule", "ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes GCP detect suspicious kubectl calls - Rule", "ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Kubernetes Sensitive Role Activity]
category = Cloud Security
last_updated = 2020-05-20
version = 1
references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
searches = ["ESCU - Kubernetes GCP detect sensitive role access - Rule", "ESCU - Kubernetes Azure detect RBAC authorization by account - Rule", "ESCU - Kubernetes AWS detect sensitive role access - Rule", "ESCU - Kubernetes AWS detect RBAC authorization by account - Rule", "ESCU - Kubernetes Azure detect sensitive role access - Rule", "ESCU - Kubernetes GCP detect RBAC authorizations by account - Rule", "ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule", "ESCU - Kubernetes AWS detect most active service accounts by pod - Rule", "ESCU - Kubernetes GCP detect most active service accounts by pod - Rule", "ESCU - Get Notable History - Response Task"]
description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces.
narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles 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 role activities

[analytic_story://Lateral Movement]
category = Adversary Tactics
last_updated = 2020-02-04
version = 2
references = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Kerberoasting spn request with RC4 encryption - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - 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 Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Investigate Successful Remote Desktop Authentications - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get History Of Email Sources - 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.\
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.

[analytic_story://Malicious PowerShell]
category = Adversary Tactics
last_updated = 2017-08-23
version = 4
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 - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Any Powershell DownloadFile - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Any Powershell DownloadString - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - 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: \
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 addressDetermining 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.\
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.

[analytic_story://Monitor Backup Solution]
category = Best Practices
last_updated = 2017-09-12
version = 1
references = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - All backup logs for host - Response Task", "ESCU - Get Notable History - Response Task"]
description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints.
narrative = Having backups is a standard best practice that helps ensure continuity of business operations.  Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint.

[analytic_story://Monitor for Unauthorized Software]
category = Best Practices
last_updated = 2017-09-15
version = 1
references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. 
narrative = It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\
It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. 

[analytic_story://Monitor for Updates]
category = Best Practices
last_updated = 2017-09-15
version = 1
references = ["https://learn.cisecurity.org/20-controls-download"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History - Response Task"]
description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches.
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.

[analytic_story://NOBELIUM Group]
category = Adversary Tactics
last_updated = 2020-12-14
version = 2
references = ["https://www.microsoft.com/security/blog/2021/03/04/goldmax-goldfinder-sibot-analyzing-nobelium-malware/", "https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html", "https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/"]
maintainers = [{"company": "Michael Haag, Splunk", "email": "-", "name": "Patrick Bareiss"}]
spec_version = 3
searches = ["ESCU - Supernova Webshell - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Sunburst Correlation DLL and Network Event - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect Rundll32 Inline HTA Execution - Rule", "ESCU - Scheduled Task Deleted Or Created via CMD - Rule", "ESCU - Windows AdFind Exe - Rule", "ESCU - TOR Traffic - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Schtasks scheduling job on remote system - Rule"]
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.

[analytic_story://Netsh Abuse]
category = Abuse
last_updated = 2017-01-05
version = 1
references = ["https://docs.microsoft.com/en-us/previous-versions/tn-archive/bb490939(v=technet.10)", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Processes created by netsh - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
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`.

[analytic_story://Office 365 Detections]
category = Cloud Security
last_updated = 2020-12-16
version = 1
references = ["https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Patrick Bareiss"}]
spec_version = 3
searches = ["ESCU - O365 Suspicious Rights Delegation - Rule", "ESCU - O365 Disable MFA - Rule", "ESCU - O365 Excessive SSO logon errors - Rule", "ESCU - O365 Suspicious User Email Forwarding - Rule", "ESCU - O365 PST export alert - Rule", "ESCU - O365 Added Service Principal - Rule", "ESCU - O365 New Federated Domain Added - Rule", "ESCU - O365 Add App Role Assignment Grant User - Rule", "ESCU - High Number of Login Failures from a single source - Rule", "ESCU - O365 Bypass MFA via Trusted IP - Rule", "ESCU - O365 Suspicious Admin Email Forwarding - Rule", "ESCU - O365 Excessive Authentication Failures Alert - Rule"]
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.

[analytic_story://Orangeworm Attack Group]
category = Malware
last_updated = 2020-01-22
version = 2
references = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - Response Task"]
description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry.
narrative = In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\
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.

[analytic_story://Phishing Payloads]
category = Adversary Tactics
last_updated = 2019-04-29
version = 1
references = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}]
spec_version = 3
searches = ["ESCU - Process Creating LNK file in Suspicious Location - Rule", "ESCU - Detect Oulook exe writing a  zip file - Rule", "ESCU - Get Parent Process Info - Response Task"]
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. \
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).\
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://Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns]
category = Adversary Tactics
last_updated = 2020-01-22
version = 1
references = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"]
maintainers = [{"company": "iDefense", "email": "-", "name": "iDefense Cyber Espionage Team"}]
spec_version = 3
searches = ["ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get History Of Email Sources - Response Task", "ESCU - Get Notable History - 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.\
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:\
\
1. www.chemscalere[.]com\
1. chemscalere[.]com\
1. about.chemscalere[.]com\
1. autoconfig.chemscalere[.]com\
1. autodiscover.chemscalere[.]com\
1. catalog.chemscalere[.]com\
1. cpanel.chemscalere[.]com\
1. db.chemscalere[.]com\
1. ftp.chemscalere[.]com\
1. mail.chemscalere[.]com\
1. news.chemscalere[.]com\
1. update.chemscalere[.]com\
1. webmail.chemscalere[.]com\
1. www.candlelightparty[.]org\
1. candlelightparty[.]org\
1. newapp.freshasianews[.]comIn addition, iDefense also recommends that organizations review their environments for activity related to the following hashes:\
\
1. cd195ee448a3657b5c2c2d13e9c7a2e2\
1. b43ad826fe6928245d3c02b648296b43\
1. 889a9b52566448231f112a5ce9b5dfaf\
1. b8ec65dab97cdef3cd256cc4753f0c54\
1. 04d83cd3813698de28cfbba326d7647c

[analytic_story://Prohibited Traffic Allowed or Protocol Mismatch]
category = Best Practices
last_updated = 2017-09-11
version = 1
references = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Parent Process Info - Response Task"]
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.

[analytic_story://Ransomware]
category = Malware
last_updated = 2020-02-04
version = 1
references = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Suspicious Scheduled Task from Public Directory - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - TOR Traffic - Rule", "ESCU - WBAdmin Delete System Backups - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - BCDEdit Failure Recovery Modification - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Backup Logs For Endpoint - Response Task", "ESCU - Get Sysmon WMI Activity for Host - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get History Of Email Sources - Response Task"]
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.

[analytic_story://Ransomware Cloud]
category = Malware
last_updated = 2020-10-27
version = 1
references = ["https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/", "https://github.com/d1vious/git-wild-hunt", "https://www.youtube.com/watch?v=PgzNib37g0M"]
maintainers = [{"company": "David Dorsey, Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
searches = ["ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule", "ESCU - AWS Detect Users with KMS keys performing encryption S3 - Rule", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Router and Infrastructure Security]
category = Best Practices
last_updated = 2017-09-12
version = 1
references = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect Port Security Violation - Rule", "ESCU - Detect IPv6 Network Infrastructure Threats - Rule", "ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Detect Rogue DHCP Server - Rule", "ESCU - Detect ARP Poisoning - Rule", "ESCU - Detect Traffic Mirroring - Rule", "ESCU - Detect Software Download To Network Device - 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.

[analytic_story://Ryuk Ransomware]
category = Malware
last_updated = 2020-11-06
version = 1
references = ["https://www.splunk.com/en_us/blog/security/detecting-ryuk-using-splunk-attack-range.html", "https://www.crowdstrike.com/blog/big-game-hunting-with-ryuk-another-lucrative-targeted-ransomware/", "https://us-cert.cisa.gov/ncas/alerts/aa20-302a"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Jose Hernandez"}]
spec_version = 3
searches = ["ESCU - Spike in File Writes - Rule", "ESCU - BCDEdit Failure Recovery Modification - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Suspicious Scheduled Task from Public Directory - Rule", "ESCU - Windows connhost exe started forcefully - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Windows DisableAntiSpyware Registry - Rule", "ESCU - Ryuk Test Files Detected - Rule", "ESCU - Ryuk Wake on LAN Command - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - NLTest Domain Trust Discovery - Rule", "ESCU - Windows Security Account Manager Stopped - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - WBAdmin Delete System Backups - Rule", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://SQL Injection]
category = Adversary Tactics
last_updated = 2017-09-19
version = 1
references = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://SamSam Ransomware]
category = Malware
last_updated = 2018-12-13
version = 1
references = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Spike in File Writes - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get Backup Logs For Endpoint - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Investigate Successful Remote Desktop Authentications - Response Task", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get History Of Email Sources - 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.\
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.

[analytic_story://Silver Sparrow]
category = Adversary Tactics
last_updated = 2021-02-24
version = 1
references = ["https://redcanary.com/blog/clipping-silver-sparrows-wings/", "https://www.sentinelone.com/blog/5-things-you-need-to-know-about-silver-sparrow/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}]
spec_version = 3
searches = ["ESCU - Suspicious Curl Network Connection - Rule", "ESCU - Suspicious PlistBuddy Usage - Rule", "ESCU - Suspicious PlistBuddy Usage via OSquery - Rule", "ESCU - Suspicious SQLite3 LSQuarantine Behavior - Rule"]
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.

[analytic_story://Spectre And Meltdown Vulnerabilities]
category = Vulnerability
last_updated = 2018-01-08
version = 1
references = ["https://meltdownattack.com/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Notable History - Response Task"]
description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story.
narrative = Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched.

[analytic_story://Splunk Enterprise Vulnerability]
category = Vulnerability
last_updated = 2017-09-19
version = 1
references = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History - Response Task"]
description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains.
narrative = This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\
\
1. Splunk Enterprise 6.4.x, prior to 6.4.3\
1. Splunk Enterprise 6.3.x, prior to 6.3.6\
1. Splunk Enterprise 6.2.x, prior to 6.2.10\
1. Splunk Enterprise 6.1.x, prior to 6.1.11\
1. Splunk Enterprise 6.0.x, prior to 6.0.12\
1. Splunk Enterprise 5.0.x, prior to 5.0.16\
1. Splunk Light, prior to 6.4.3CVE-2016-4859 allows attackers to redirect users to arbitrary web sites and conduct phishing attacks via unspecified vectors. (Credit: Noriaki Iwasaki, Cyber Defense Institute, Inc.).\
It is important to ensure that your Splunk deployment is being kept up to date and is properly configured. This detection search allows analysts to monitor internal logs to ensure users are properly authenticated and cannot be redirected to any malicious third-party websites.

[analytic_story://Splunk Enterprise Vulnerability CVE-2018-11409]
category = Vulnerability
last_updated = 2018-06-14
version = 1
references = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Investigate Network Traffic From src ip - Response Task", "ESCU - Get Notable History - Response Task"]
description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users.
narrative = Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\
Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\
Read more in Splunk's official response: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings.\
A detection search within this Analytic Story looks for vulnerabilities described in CVE-2018-11409: Information Exposure (https://nvd.nist.gov/vuln/detail/CVE-2018-11409). If it turns up activities that may be specific, you can use the included investigative searches to return information regarding web activity and network traffic by src_ip.

[analytic_story://Suspicious AWS EC2 Activities]
category = Cloud Security
last_updated = 2018-02-09
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Get Notable History - Response Task", "ESCU - AWS Investigate Security Hub alerts by dest - Response Task", "ESCU - Investigate AWS activities via region name - Response Task", "ESCU - Get EC2 Launch Details - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get EC2 Instance Details by instanceId - Response Task"]
description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it.
narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within 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 Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities.

[analytic_story://Suspicious AWS Login Activities]
category = Cloud Security
last_updated = 2019-05-01
version = 1
references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
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.

[analytic_story://Suspicious AWS S3 Activities]
category = Cloud Security
last_updated = 2018-07-24
version = 2
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect New Open S3 Buckets over AWS CLI - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Get Notable History - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - AWS S3 Bucket details via bucketName - Response Task", "ESCU - Investigate AWS activities via region name - Response Task", "ESCU - AWS Investigate User Activities By ARN - 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.\
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]
category = Cloud Security
last_updated = 2018-05-07
version = 1
references = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - AWS Network Interface details via resourceId - Response Task", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - AWS Network ACL Details from ID - Response Task", "ESCU - AWS Investigate User Activities By ARN - 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.\
 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.

[analytic_story://Suspicious Cloud Authentication Activities]
category = Cloud Security
last_updated = 2020-06-04
version = 1
references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/", "https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Detect AWS Console Login by New User - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Investigate AWS User Activities by user field - Response Task", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Suspicious Cloud Instance Activities]
category = Cloud Security
last_updated = 2020-08-25
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Abnormally High Number Of Cloud Instances Launched - Rule", "ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule", "ESCU - Cloud Instance Modified By Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get All AWS Activity From IP Address - 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 = 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.

[analytic_story://Suspicious Cloud Provisioning Activities]
category = Cloud Security
last_updated = 2018-08-20
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule", "ESCU - Cloud Provisioning Activity From Previously Unseen Region - Rule", "ESCU - Cloud Provisioning Activity From Previously Unseen Country - Rule", "ESCU - Cloud Provisioning Activity From Previously Unseen IP Address - 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.

[analytic_story://Suspicious Cloud User Activities]
category = Cloud Security
last_updated = 2020-09-04
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["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.\
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]
category = Adversary Tactics
last_updated = 2020-02-03
version = 2
references = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Unusually Long Command Line - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - 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.
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 DNS Traffic]
category = Adversary Tactics
last_updated = 2017-09-18
version = 1
references = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Get DNS Server History for a host - Response Task", "ESCU - Get Notable History - Response Task", "ESCU - Get DNS traffic ratio - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Parent 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.

[analytic_story://Suspicious Emails]
category = Adversary Tactics
last_updated = 2020-01-27
version = 1
references = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Get Email Info - Response Task", "ESCU - Get Emails From Specific Sender - Response Task", "ESCU - Get Notable History - Response Task"]
description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story.
narrative = It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk to detect suspicious content.\
Once a phishing message has been detected, the next steps are to answer the following questions: \
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.

[analytic_story://Suspicious GCP Storage Activities]
category = Cloud Security
last_updated = 2020-08-05
version = 1
references = ["https://cloud.google.com/blog/product/gcp/4-steps-for-hardening-your-cloud-storage-buckets-taking-charge-of-your-security", "https://rhinosecuritylabs.com/gcp/google-cloud-platform-gcp-bucket-enumeration/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Shannon Davis"}]
spec_version = 3
searches = ["ESCU - Detect GCP Storage access from a new IP - Rule", "ESCU - Detect New Open GCP Storage Buckets - Rule", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Suspicious MSHTA Activity]
category = Adversary Tactics
last_updated = 2021-01-20
version = 2
references = ["https://redcanary.com/blog/introducing-atomictestharnesses/", "https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/techniques/T1218/005/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5"]
maintainers = [{"company": "Michael Haag, Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Suspicious mshta spawn - Rule", "ESCU - Suspicious mshta child process - Rule", "ESCU - Detect mshta renamed - Rule", "ESCU - Detect MSHTA Url in Command Line - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Detect Rundll32 Inline HTA Execution - Rule", "ESCU - Detect mshta inline hta execution - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code.
narrative = One common adversary tactic is to bypass application control solutions via the mshta.exe process, which loads Microsoft HTML applications (mshtml.dll) with the .hta suffix. In these cases, attackers use the trusted Windows utility to proxy execution of malicious files, whether an .hta application, javascript, or VBScript.\
The searches in this story help you detect and investigate suspicious activity that may indicate that an attacker is leveraging mshta.exe to execute malicious code.\
Triage\
Validate execution \
1. Determine if MSHTA.exe executed. Validate the OriginalFileName of MSHTA.exe and further PE metadata. If executed outside of c:\windows\system32 or c:\windows\syswow64, it should be highly suspect.\
1. Determine if script code was executed with MSHTA.\
Situational Awareness\
The objective of this step is meant to identify suspicious behavioral indicators related to executed of Script code by MSHTA.exe.\
1. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application?\
1. Module loads. Are the known MSHTA.exe modules being loaded by a non-standard application? Is MSHTA loading any suspicious .DLLs?\
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.

[analytic_story://Suspicious Okta Activity]
category = Adversary Tactics
last_updated = 2020-04-02
version = 1
references = ["https://attack.mitre.org/wiki/Technique/T1078", "https://owasp.org/www-community/attacks/Credential_stuffing", "https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Investigate Okta Activity by IP Address - Response Task", "ESCU - Investigate User Activities In Okta - Response Task", "ESCU - Investigate Okta Activity by app - Response Task"]
description = Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors.
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.

[analytic_story://Suspicious Regsvr32 Activity]
category = Adversary Tactics
last_updated = 2021-01-29
version = 1
references = ["https://attack.mitre.org/techniques/T1218/010/", "https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md", "https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}]
spec_version = 3
searches = ["ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule", "ESCU - Detect Regsvr32 Application Control Bypass - 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.

[analytic_story://Suspicious Rundll32 Activity]
category = Adversary Tactics
last_updated = 2021-02-03
version = 1
references = ["https://attack.mitre.org/techniques/T1218/011/", "https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md", "https://lolbas-project.github.io/lolbas/Binaries/Rundll32"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}]
spec_version = 3
searches = ["ESCU - Suspicious Rundll32 Rename - Rule", "ESCU - Suspicious Rundll32 no Command Line Arguments - Rule", "ESCU - Suspicious Rundll32 dllregisterserver - Rule", "ESCU - Suspicious Rundll32 StartW - Rule", "ESCU - Detect Rundll32 Application Control Bypass - syssetup - Rule", "ESCU - Detect Rundll32 Application Control Bypass - advpack - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Detect Rundll32 Application Control Bypass - setupapi - Rule"]
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.

[analytic_story://Suspicious WMI Use]
category = Adversary Tactics
last_updated = 2018-10-23
version = 2
references = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - Process Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Sysmon WMI Activity for Host - Response Task", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Suspicious Windows Registry Activities]
category = Adversary Tactics
last_updated = 2018-05-31
version = 1
references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - 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 searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry.

[analytic_story://Suspicious Zoom Child Processes]
category = Adversary Tactics
last_updated = 2020-04-13
version = 1
references = ["https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/", "https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - First Time Seen Child Process of Zoom - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Get Process File Activity - Response Task"]
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.

[analytic_story://Trusted Developer Utilities Proxy Execution]
category = Adversary Tactics
last_updated = 2021-01-12
version = 1
references = ["https://attack.mitre.org/techniques/T1127/", "https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md", "https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}]
spec_version = 3
searches = ["ESCU - Suspicious microsoft workflow compiler rename - Rule", "ESCU - Suspicious microsoft workflow compiler usage - Rule"]
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.

[analytic_story://Trusted Developer Utilities Proxy Execution MSBuild]
category = Adversary Tactics
last_updated = 2021-01-21
version = 1
references = ["https://attack.mitre.org/techniques/T1127/001/", "https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md", "https://github.com/infosecn1nja/MaliciousMacroMSBuild", "https://github.com/xorrior/RandomPS-Scripts/blob/master/Invoke-ExecuteMSBuild.ps1", "https://lolbas-project.github.io/lolbas/Binaries/Msbuild/", "https://github.com/MHaggis/CBR-Queries/blob/master/msbuild.md"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Michael Haag"}]
spec_version = 3
searches = ["ESCU - Suspicious MSBuild Rename - Rule", "ESCU - Suspicious MSBuild Spawn - Rule", "ESCU - Suspicious msbuild path - Rule"]
description = Monitor and detect techniques used by attackers who leverage the msbuild.exe process to execute malicious code.
narrative = Adversaries may use MSBuild to proxy execution of code through a trusted Windows utility. MSBuild.exe (Microsoft Build Engine) is a software build platform used by Visual Studio and is native to Windows. It handles XML formatted project files that define requirements for loading and building various platforms and configurations.\
The inline task capability of MSBuild that was introduced in .NET version 4 allows for C# code to be inserted into an XML project file. MSBuild will compile and execute the inline task. MSBuild.exe is a signed Microsoft binary, so when it is used this way it can execute arbitrary code and bypass application control defenses that are configured to allow MSBuild.exe execution.\
The searches in this story help you detect and investigate suspicious activity that may indicate that an adversary is leveraging msbuild.exe to execute malicious code.\
Triage\
Validate execution\
1. Determine if MSBuild.exe executed. Validate the OriginalFileName of MSBuild.exe and further PE metadata.\
1. Determine if script code was executed with MSBuild.\
Situational Awareness\
The objective of this step is meant to identify suspicious behavioral indicators related to executed of Script code by MSBuild.exe.\
1. Parent process. Is the parent process a known LOLBin? Is the parent process an Office Application?\
1. Module loads. Are the known MSBuild.exe modules being loaded by a non-standard application? Is MSbuild loading any suspicious .DLLs?\
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.

[analytic_story://Unusual AWS EC2 Modifications]
category = Cloud Security
last_updated = 2018-04-09
version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get EC2 Instance Details by instanceId - Response Task", "ESCU - Get Notable History - Response Task"]
description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation.
narrative = A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \
 Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior.

[analytic_story://Unusual Processes]
category = Malware
last_updated = 2020-02-04
version = 2
references = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - 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.\
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.

[analytic_story://Use of Cleartext Protocols]
category = Best Practices
last_updated = 2017-09-15
version = 1
references = ["https://www.monkey.org/~dugsong/dsniff/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Process Information For Port Activity - Response Task", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Web Fraud Detection]
category = Abuse
last_updated = 2018-10-08
version = 1
references = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Jim Apger"}]
spec_version = 3
searches = ["ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Get Web Session Information via session id - Response Task", "ESCU - Get Emails From Specific Sender - Response Task", "ESCU - Get Notable History - Response Task"]
description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets.
narrative = The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes are used to steal millions of dollars each year from victims and continue to plague the Internet through various methods. The agency includes phishing scams, data breaches, Denial of Service (DOS) attacks, email account compromise, malware, spoofing, and ransomware in this category.\
These crimes are not the fraud itself, but rather the attack techniques commonly employed by fraudsters in their pursuit of data that enables them to commit malicious actssuch as obtaining and using stolen credit cards. They represent a serious problem that is steadily increasing and not likely to go away anytime soon.\
When developing a strategy for preventing fraud in your environment, its important to  look across all of your web services for evidence that attackers are abusing enterprise resources to enumerate systems, harvest data for secondary fraudulent activity, or abuse terms of service.This Analytic Story looks for evidence of common Internet attack techniques that could be indicative of web fraud in your environmentincluding account harvesting, anomalous user clickspeed, and password sharing across accounts, to name just a few.\
The account-harvesting search focuses on web pages used for user-account registration. It detects the creation of a large number of user accounts using the same email domain name, a type of activity frequently seen in advance of a fraud campaign.\
The anomalous clickspeed search looks for users who are moving through your website at a faster-than-normal speed or with a perfect click cadence (high periodicity or low standard deviation), which could indicate that the user is a script, not an actual human.\
Another search detects incidents wherein a single password is used across multiple accounts, which may indicate that a fraudster has infiltrated your environment and embedded a common password within a script.

[analytic_story://Windows DNS SIGRed CVE-2020-1350]
category = Adversary Tactics
last_updated = 2020-07-28
version = 1
references = ["https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/", "https://support.microsoft.com/en-au/help/4569509/windows-dns-server-remote-code-execution-vulnerability"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Shannon Davis"}]
spec_version = 3
searches = ["ESCU - Detect Windows DNS SIGRed via Zeek - Rule", "ESCU - Detect Windows DNS SIGRed via Splunk Stream - 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).

[analytic_story://Windows Defense Evasion Tactics]
category = Adversary Tactics
last_updated = 2018-05-31
version = 1
references = ["https://attack.mitre.org/wiki/Defense_Evasion"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Remote Registry Key modifications - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Eventvwr UAC Bypass - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - FodHelper UAC Bypass - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Windows DisableAntiSpyware Registry - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - 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.

[analytic_story://Windows File Extension and Association Abuse]
category = Malware
last_updated = 2018-01-26
version = 1
references = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
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.\
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]
category = Adversary Tactics
last_updated = 2017-09-12
version = 2
references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
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).

[analytic_story://Windows Persistence Techniques]
category = Adversary Tactics
last_updated = 2018-05-31
version = 2
references = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
searches = ["ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Suspicious Scheduled Task from Public Directory - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Certutil exe certificate extraction - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
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.

[analytic_story://Windows Privilege Escalation]
category = Adversary Tactics
last_updated = 2020-02-04
version = 2
references = ["https://attack.mitre.org/tactics/TA0004/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
searches = ["ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - 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.

[analytic_story://Windows Service Abuse]
category = Malware
last_updated = 2017-11-02
version = 3
references = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
searches = ["ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Get Parent Process Info - Response Task", "ESCU - Get Process Info - Response Task", "ESCU - Get Notable History - Response Task"]
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.

### END STORIES ###

### DETECTIONS ###

[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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.\
 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 = []

[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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.\
 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 = []

[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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.\
 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 = []

[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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.\
 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 = []

[savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time.  This search is deprecated and have been translated to use the latest Authentication Datamodel.
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.
providing_technologies = []

[savedsearch://ESCU - AWS Detect Users creating keys with encrypt policy without MFA - Rule]
type = detection
asset_type = AWS Account
confidence = medium
explanation = This search provides detection of KMS keys which action kms:Encrypt is accessible for everyone (also outside of your organization). This is an identicator that your account is compromised and the attacker uses the encryption key to compromise another company.
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs
annotations = {"mitre_attack": ["T1486"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - AWS Detect Users with KMS keys performing encryption S3 - Rule]
type = detection
asset_type = S3 Bucket
confidence = medium
explanation = This search provides detection of users with KMS keys performing encryption specifically against S3 buckets.
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs
annotations = {"mitre_attack": ["T1486"]}
known_false_positives = bucket with S3 encryption
providing_technologies = []

[savedsearch://ESCU - AWS EKS Kubernetes cluster sensitive object access - Rule]
type = detection
asset_type = AWS EKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets
how_to_implement = You must install Splunk Add-on for Amazon Web Services and Splunk App for AWS. This search works with cloudwatch logs.
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
providing_technologies = []

[savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The search looks for 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 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.
providing_technologies = []

[savedsearch://ESCU - AWS Network Access Control List Deleted - Rule]
type = detection
asset_type = AWS Instance
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 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 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.
providing_technologies = []

[savedsearch://ESCU - AWS SAML Access by Provider User and Principal - Rule]
type = detection
asset_type = AWS Federated Account
confidence = medium
explanation = This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider.
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs
annotations = {"mitre_attack": ["T1078"]}
known_false_positives = Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks.
providing_technologies = []

[savedsearch://ESCU - AWS SAML Update identity provider - Rule]
type = detection
asset_type = AWS Federated Account
confidence = medium
explanation = This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker.
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.
annotations = {"mitre_attack": ["T1078"]}
known_false_positives = Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored.
providing_technologies = []

[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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
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. The threshold value should be tuned to your environment.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
providing_technologies = []

[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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.
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. The threshold value should be tuned to your environment.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
providing_technologies = []

[savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window. 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 CloudTrail inputs.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user.
providing_technologies = []

[savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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.
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. The threshold value should be tuned to your environment.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
providing_technologies = []

[savedsearch://ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user.
how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function.
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
known_false_positives = 
providing_technologies = []

[savedsearch://ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule]
type = detection
asset_type = Cloud Instance
confidence = medium
explanation = This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.
how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
providing_technologies = []

[savedsearch://ESCU - Abnormally High Number Of Cloud Instances Launched - Rule]
type = detection
asset_type = Cloud Instance
confidence = medium
explanation = This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.
how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
providing_technologies = []

[savedsearch://ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user.
how_to_implement = You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model.
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
known_false_positives = 
providing_technologies = []

[savedsearch://ESCU - Access LSASS Memory for Dump Creation - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = Detect memory dumping of the LSASS process.
how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for 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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.
providing_technologies = []

[savedsearch://ESCU - Amazon EKS Kubernetes Pod scan detection - Rule]
type = detection
asset_type = Amazon EKS Kubernetes cluster Pod
confidence = medium
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.
providing_technologies = []

[savedsearch://ESCU - Amazon EKS Kubernetes cluster scan detection - Rule]
type = detection
asset_type = Amazon EKS Kubernetes cluster
confidence = medium
explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS
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 CloudWatch EKS Logs inputs.
annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context.
providing_technologies = []

[savedsearch://ESCU - Any Powershell DownloadFile - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1059.001"]}
known_false_positives = False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.
providing_technologies = []

[savedsearch://ESCU - Any Powershell DownloadString - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1059.001"]}
known_false_positives = False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.
providing_technologies = []

[savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attempt to add a certificate to the certificate 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.
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 = []

[savedsearch://ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Attempt To Stop Security Service - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for attempts to stop security-related services on the endpoint.
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. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service.,
annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
known_false_positives = None identified. Attempts to disable security-related services should be identified and understood.
providing_technologies = []

[savedsearch://ESCU - Attempted Credential Dump From Registry via Reg exe - Rule]
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.
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 = []

[savedsearch://ESCU - BCDEdit Failure Recovery Modification - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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. Tune based on parent process names.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"]}
known_false_positives = Administrators may modify the boot configuration.
providing_technologies = []

[savedsearch://ESCU - Batch File Write to System32 - Rule]
type = detection
asset_type = Endpoint
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.
providing_technologies = []

[savedsearch://ESCU - Certutil exe certificate extraction - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
how_to_implement = 
annotations = {"kill_chain_phases": ["Installation"]}
known_false_positives = Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services.
providing_technologies = []

[savedsearch://ESCU - Child Processes of Spoolsv exe - Rule]
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.
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 = []

[savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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\
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.
providing_technologies = []

[savedsearch://ESCU - Cloud API Calls From Previously Unseen User Roles - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for new commands from each user role.
how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - 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 `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter`
annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078"], "nist": ["ID.AM"]}
known_false_positives = .
providing_technologies = []

[savedsearch://ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule]
type = detection
asset_type = Cloud Compute Instance
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.
providing_technologies = []

[savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule]
type = detection
asset_type = Cloud Compute Instance
confidence = medium
explanation = This search looks for cloud compute instances being created with previously unseen image IDs.
how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - 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_with_previously_unseen_image_filter` macro.
annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]}
known_false_positives = After a new image is created, the first systems created with that image will cause this alert to fire.  Verify that the image being used was created by a legitimate user.
providing_technologies = []

[savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule]
type = detection
asset_type = Cloud Compute Instance
confidence = medium
explanation = Find EC2 instances being created with previously unseen instance types.
how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - 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_with_previously_unseen_instance_type_filter` macro.
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 that has never been used before. Verify with the creator that they intended to create the system with the new instance type.
providing_technologies = []

[savedsearch://ESCU - Cloud Instance Modified By Previously Unseen User - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Cloud Network Access Control List Deleted - Rule]
type = detection
asset_type = Instance
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 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.
providing_technologies = []

[savedsearch://ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule]
type = detection
asset_type = AWS Instance
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.\
 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 = []

[savedsearch://ESCU - Cloud Provisioning Activity From Previously Unseen Country - Rule]
type = detection
asset_type = AWS Instance
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.\
 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 = []

[savedsearch://ESCU - Cloud Provisioning Activity From Previously Unseen IP Address - Rule]
type = detection
asset_type = AWS Instance
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.\
 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 = []

[savedsearch://ESCU - Cloud Provisioning Activity From Previously Unseen Region - Rule]
type = detection
asset_type = AWS Instance
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.\
 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 = []

[savedsearch://ESCU - Cobalt Strike Named Pipes - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["Actions on Objectives"], "mitre_attack": ["T1055"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some of the named pipes identified and added may cause false positives. Filter by process name or pipe name to reduce false positives.
providing_technologies = []

[savedsearch://ESCU - Common Ransomware Extensions - Rule]
type = detection
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\
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`
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions.
providing_technologies = []

[savedsearch://ESCU - Common Ransomware Notes - Rule]
type = detection
asset_type = Endpoint
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.
providing_technologies = []

[savedsearch://ESCU - Create Remote Thread into LSASS - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = Detect remote thread creation into LSASS consistent with credential dumping.
how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 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 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise.
providing_technologies = []

[savedsearch://ESCU - Create local admin accounts using net exe - Rule]
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.
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 = []

[savedsearch://ESCU - Create or delete windows shares using net exe - Rule]
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.
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 = []

[savedsearch://ESCU - Creation of Shadow Copy - Rule]
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.
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 = []

[savedsearch://ESCU - Creation of Shadow Copy with wmic and powershell - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search detects the use of wmic and Powershell to create a shadow copy.
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", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
known_false_positives = Legtimate administrator usage of wmic to create a shadow copy.
providing_technologies = []

[savedsearch://ESCU - Creation of lsass Dump with Taskmgr - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess 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.
how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. 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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.
providing_technologies = []

[savedsearch://ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule]
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.
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 = []

[savedsearch://ESCU - Credential Dumping via Symlink to Shadow Copy - Rule]
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.
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 = []

[savedsearch://ESCU - DNS Query Length Outliers - MLTK - Rule]
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\
1. \
1. **Label:** DNS Query Length, **Field:** query_length\
1. \
1. **Label:** Number of events, **Field:** 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 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.
providing_technologies = []

[savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule]
type = detection
asset_type = Endpoint
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.
providing_technologies = []

[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security.
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"]}
known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate.
providing_technologies = []

[savedsearch://ESCU - DNS record changed - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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. \
(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"]}
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.
providing_technologies = []

[savedsearch://ESCU - Deleting Shadow Copies - Rule]
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.
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 = []

[savedsearch://ESCU - Detect API activity from users without MFA - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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 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\
1. \
1. **Label:** AWS User ARN, **Field:** userIdentity.arn\
1. \
1. **Label:** AWS User Type, **Field:** userIdentity.type\
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"], "nist": ["DE.DP", "PR.AC"]}
known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS.
providing_technologies = []

[savedsearch://ESCU - Detect ARP Poisoning - Rule]
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.
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 = []

[savedsearch://ESCU - Detect AWS API Activities From Unapproved Accounts - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for successful 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 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.
providing_technologies = []

[savedsearch://ESCU - Detect AWS Console Login by New User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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
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. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` 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"], "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 = []

[savedsearch://ESCU - Detect AWS Console Login by User from New City - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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
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. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro.
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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 = []

[savedsearch://ESCU - Detect AWS Console Login by User from New Country - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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
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. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro.
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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 = []

[savedsearch://ESCU - Detect AWS Console Login by User from New Region - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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
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. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro.
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "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 = []

[savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique.
how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows.
annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1550.002"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate.
providing_technologies = []

[savedsearch://ESCU - Detect Baron Samedit CVE-2021-3156 - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search detects the heap-based buffer overflow of sudoedit
how_to_implement = Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Detect Baron Samedit CVE-2021-3156 Segfault - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search detects the heap-based buffer overflow of sudoedit
how_to_implement = Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged.  This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host
annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
known_false_positives = If sudoedit is throwing segfaults for other reasons this will pick those up too.
providing_technologies = []

[savedsearch://ESCU - Detect Baron Samedit CVE-2021-3156 via OSQuery - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search detects the heap-based buffer overflow of sudoedit
how_to_implement = OSQuery 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. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Detect Computer Changed with Anonymous Account - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account.
how_to_implement = This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event 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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1210"], "nist": ["DE.AE", "DE.CM"]}
known_false_positives = None thus far found
providing_technologies = []

[savedsearch://ESCU - Detect Credential Dumping through LSASS access - Rule]
type = detection
asset_type = Windows
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.
providing_technologies = []

[savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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. \
(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"]}
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.
providing_technologies = []

[savedsearch://ESCU - Detect Excessive Account Lockouts From Endpoint - Rule]
type = detection
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. \
(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.
providing_technologies = []

[savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search detects user accounts that have been locked out a relatively high number of times in a short period.
how_to_implement = ou 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.
annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.003"], "nist": ["PR.IP"]}
known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts.
providing_technologies = []

[savedsearch://ESCU - Detect Exchange Web Shell - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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 have a randomized name of 8 characters long. Review the file for suspect commands. Identify additional log sources, IIS included, to review source and other potential exploitation.
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 and `Filesystem` node.
annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505.003"]}
known_false_positives = The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
providing_technologies = []

[savedsearch://ESCU - Detect F5 TMUI RCE CVE-2020-5902 - Rule]
type = detection
asset_type = Network
confidence = medium
explanation = This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices
how_to_implement = To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog.  As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it.  I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;).
annotations = {"cis20": ["CIS 8", "CIS 11"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Detect GCP Storage access from a new IP - Rule]
type = detection
asset_type = GCP Storage Bucket
confidence = medium
explanation = This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a 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). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs.  These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour.  It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *).  A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets.
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 = GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), 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 two hours.
providing_technologies = []

[savedsearch://ESCU - Detect HTML Help Renamed - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect HTML Help Spawn Child Process - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect HTML Help URL in Command Line - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect HTML Help Using InfoTech Storage Handlers - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect IPv6 Network Infrastructure Threats - Rule]
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.
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
providing_technologies = []

[savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule]
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
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 = []

[savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect Mimikatz Using Loaded Images - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code.
how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.AE", "DE.CM"]}
known_false_positives = Other tools can import the same DLLs. These tools should be part of a whitelist. False positives may be present with any process that authenticates or uses credentials, PowerShell included. Filter based on parent process.
providing_technologies = []

[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = 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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect New Local Admin account - Rule]
type = detection
asset_type = Windows
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
providing_technologies = []

[savedsearch://ESCU - Detect New Login Attempts to Routers - Rule]
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.
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 = []

[savedsearch://ESCU - Detect New Open GCP Storage Buckets - Rule]
type = detection
asset_type = GCP Storage Bucket
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.
providing_technologies = []

[savedsearch://ESCU - Detect New Open S3 Buckets over AWS CLI - Rule]
type = detection
asset_type = S3 Bucket
confidence = medium
explanation = This search looks for 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.
providing_technologies = []

[savedsearch://ESCU - Detect New Open S3 buckets - Rule]
type = detection
asset_type = S3 Bucket
confidence = medium
explanation = This search looks for 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.
providing_technologies = []

[savedsearch://ESCU - Detect Oulook exe writing a  zip file - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.
how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to 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.
annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.001"], "nist": ["ID.AM", "PR.DS"]}
known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk.
providing_technologies = []

[savedsearch://ESCU - Detect Outbound SMB Traffic - Rule]
type = detection
asset_type = Endpoint
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.
providing_technologies = []

[savedsearch://ESCU - Detect Path Interception By Creation Of program exe - Rule]
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.
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 = []

[savedsearch://ESCU - Detect Port Security Violation - Rule]
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.
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 = []

[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd exe - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.
providing_technologies = []

[savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule]
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.
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
providing_technologies = []

[savedsearch://ESCU - Detect Rare Executables - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.
how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes.  These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment.
annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]}
known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results.
providing_technologies = []

[savedsearch://ESCU - Detect Regasm Spawning a Process - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
providing_technologies = []

[savedsearch://ESCU - Detect Regasm with Network Connection - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
providing_technologies = []

[savedsearch://ESCU - Detect Regasm with no Command Line Arguments - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
providing_technologies = []

[savedsearch://ESCU - Detect Regsvcs Spawning a Process - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
providing_technologies = []

[savedsearch://ESCU - Detect Regsvcs with Network Connection - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
providing_technologies = []

[savedsearch://ESCU - Detect Regsvcs with No Command Line Arguments - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
providing_technologies = []

[savedsearch://ESCU - Detect Regsvr32 Application Control Bypass - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect Rogue DHCP Server - Rule]
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.
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 = []

[savedsearch://ESCU - Detect Rundll32 Application Control Bypass - advpack - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect Rundll32 Application Control Bypass - setupapi - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect Rundll32 Application Control Bypass - syssetup - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect Rundll32 Inline HTA Execution - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect S3 access from a new IP - Rule]
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.
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 = []

[savedsearch://ESCU - Detect SNICat SNI Exfiltration - Rule]
type = detection
asset_type = Network
confidence = medium
explanation = This search looks for commands that the SNICat tool uses in the TLS SNI field.
how_to_implement = You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito.  You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1041"], "nist": ["PR.DS", "DE.CM", "DE.AE"]}
known_false_positives = Unknown
providing_technologies = []

[savedsearch://ESCU - Detect Software Download To Network Device - Rule]
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.
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 = []

[savedsearch://ESCU - Detect Spike in AWS API Activity - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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\
1. \
1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\
1. \
1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\
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"]}
known_false_positives = 
providing_technologies = []

[savedsearch://ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals
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 Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
annotations = {"cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = None
providing_technologies = []

[savedsearch://ESCU - Detect Spike in AWS Security Hub Alerts for User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals.
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 Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
annotations = {"cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = None
providing_technologies = []

[savedsearch://ESCU - Detect Spike in Network ACL Activity - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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 = []

[savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule]
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 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 = []

[savedsearch://ESCU - Detect Spike in Security Group Activity - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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 = []

[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule]
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.
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 = []

[savedsearch://ESCU - Detect Traffic Mirroring - Rule]
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.
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 = []

[savedsearch://ESCU - Detect USB device insertion - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework.
annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]}
known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate.
providing_technologies = []

[savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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
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. 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": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Some legitimate applications may exhibit this behavior.
providing_technologies = []

[savedsearch://ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule]
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.
annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Detect Windows DNS SIGRed via Zeek - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search detects SIGRed via Zeek DNS and Zeek Conn data.
how_to_implement = You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json.  The Network Resolution and Network Traffic datamodels are in use for this search.
annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Detect Zerologon via Zeek - Rule]
type = detection
asset_type = Network
confidence = medium
explanation = This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC
how_to_implement = You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json.  These three operations are then correlated on the Zeek UID field.
annotations = {"cis20": ["CIS 8", "CIS 11"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule]
type = detection
asset_type = Web Server
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.
providing_technologies = []

[savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule]
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\
1. \
1. **Label:** DNS Answer, **Field:** answer\
1. \
1. **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`
annotations = {"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"]}
known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified.
providing_technologies = []

[savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule]
type = detection
asset_type = Web Server
confidence = medium
explanation = 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.
how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model
annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]}
known_false_positives = No known false positives for this detection.
providing_technologies = []

[savedsearch://ESCU - Detect mshta inline hta execution - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Detect mshta renamed - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 use a moved copy of mshta.exe, but never renamed, triggering a false positive.
providing_technologies = []

[savedsearch://ESCU - Detect new API calls from user roles - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in 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 = []

[savedsearch://ESCU - Detect new user AWS Console Login - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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 CloudTrail inputs. Run the "Previously seen users in 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 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 = []

[savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for fast execution of processes used for system network configuration discovery on the endpoint.
how_to_implement = You must be ingesting data that records registry 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 reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings.
annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1016"], "nist": ["ID.AM", "PR.DS"]}
known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives.
providing_technologies = []

[savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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\
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.
providing_technologies = []

[savedsearch://ESCU - Detection of DNS Tunnels - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
providing_technologies = []

[savedsearch://ESCU - Detection of tools built by NirSoft - Rule]
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.
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 = []

[savedsearch://ESCU - Disabling Remote User Account Control - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC).
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 via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1548.002"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence.
providing_technologies = []

[savedsearch://ESCU - Dump LSASS via comsvcs DLL - Rule]
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.
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 = []

[savedsearch://ESCU - Dump LSASS via procdump - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 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 = []

[savedsearch://ESCU - Dump LSASS via procdump Rename - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 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 = []

[savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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.
providing_technologies = []

[savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search looks for 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 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.
providing_technologies = []

[savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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 = []

[savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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 = []

[savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = 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 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.
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. \
 **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.
annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
known_false_positives = None at this time
providing_technologies = []

[savedsearch://ESCU - Email files written outside of the Outlook directory - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.
how_to_implement = To successfully implement this search, you must be ingesting data that records the 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 by 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": ["T1114.001"]}
known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.
providing_technologies = []

[savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule]
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.
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 = []

[savedsearch://ESCU - Eventvwr UAC Bypass - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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 `Registry` node.
annotations = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548.002"]}
known_false_positives = Some false positives may be present and will need to be filtered.
providing_technologies = []

[savedsearch://ESCU - Excessive DNS Failures - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences.
how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment.
providing_technologies = []

[savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
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.
providing_technologies = []

[savedsearch://ESCU - Execution of File with Multiple Extensions - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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
providing_technologies = []

[savedsearch://ESCU - File with Samsam Extension - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for file writes with extensions consistent with a SamSam ransomware attack.
how_to_implement = You must be ingesting data that records 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": ["Installation"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results.
providing_technologies = []

[savedsearch://ESCU - First Time Seen Child Process of Zoom - Rule]
type = detection
asset_type = Endpoint
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.
providing_technologies = []

[savedsearch://ESCU - First Time Seen Running Windows Service - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached.
how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - 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 Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
annotations = {"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"]}
known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process.
providing_technologies = []

[savedsearch://ESCU - First time seen command line argument - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 = []

[savedsearch://ESCU - FodHelper UAC Bypass - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\(default)`\
Upon triage, fodhelper.exe will have a child process and read access will occur on the registry keys. Isolate the endpoint and review parallel processes for additional behavior.
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": ["T1112", "T1548.002"]}
known_false_positives = Limited to no false positives are expected.
providing_technologies = []

[savedsearch://ESCU - GCP Detect accounts with high risk roles by project - Rule]
type = detection
asset_type = GCP Account
confidence = medium
explanation = This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema.
how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization
providing_technologies = []

[savedsearch://ESCU - GCP Detect gcploit framework - Rule]
type = detection
asset_type = GCP Account
confidence = medium
explanation = This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts.
how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects
providing_technologies = []

[savedsearch://ESCU - GCP Detect high risk permissions by resource and account - Rule]
type = detection
asset_type = GCP Account
confidence = medium
explanation = This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges.
how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives.
providing_technologies = []

[savedsearch://ESCU - GCP GCR container uploaded - Rule]
type = detection
asset_type = GCP GCR Container
confidence = medium
explanation = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path.
how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives.
annotations = {"mitre_attack": ["T1525"]}
known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path.
providing_technologies = []

[savedsearch://ESCU - GCP Kubernetes cluster pod scan detection - Rule]
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
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.
providing_technologies = []

[savedsearch://ESCU - GCP Kubernetes cluster scan detection - Rule]
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
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. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs.
annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context.
providing_technologies = []

[savedsearch://ESCU - Hiding Files And Directories With Attrib exe - Rule]
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.
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 = []

[savedsearch://ESCU - High Number of Login Failures from a single source - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = 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.
how_to_implement = 
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1110.001"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Hosts receiving high volume of network traffic from email server - Rule]
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.
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 = []

[savedsearch://ESCU - Identify New User Accounts - Rule]
type = detection
asset_type = Domain Server
confidence = medium
explanation = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week.
how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework.
annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.002"], "nist": ["PR.IP"]}
known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately.
providing_technologies = []

[savedsearch://ESCU - Kerberoasting spn request with RC4 encryption - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search detects a potential kerberoasting attack via service principal name requests
how_to_implement = You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos
annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1558.003"], "nist": ["DE.CM"]}
known_false_positives = Older systems that support kerberos RC4 by default NetApp may generate false positives
providing_technologies = []

[savedsearch://ESCU - Kubernetes AWS detect RBAC authorization by account - Rule]
type = detection
asset_type = AWS EKS Kubernetes cluster
confidence = medium
explanation = 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
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
providing_technologies = []

[savedsearch://ESCU - Kubernetes AWS detect most active service accounts by pod - Rule]
type = detection
asset_type = AWS EKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.
providing_technologies = []

[savedsearch://ESCU - Kubernetes AWS detect sensitive role access - Rule]
type = detection
asset_type = AWS EKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. 
providing_technologies = []

[savedsearch://ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule]
type = detection
asset_type = AWS EKS Kubernetes cluster
confidence = medium
explanation = 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
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
providing_technologies = []

[savedsearch://ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule]
type = detection
asset_type = AWS EKS Kubernetes cluster
confidence = medium
explanation = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure detect RBAC authorization by account - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness.
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure detect sensitive object access - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure detect sensitive role access - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. 
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information on rare Kubectl calls with IP, verb namespace and object access context
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure pod scan fingerprint - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Reconnaissance"]}
known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.
providing_technologies = []

[savedsearch://ESCU - Kubernetes Azure scan fingerprint - Rule]
type = detection
asset_type = Azure AKS Kubernetes cluster
confidence = medium
explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure
how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.
providing_technologies = []

[savedsearch://ESCU - Kubernetes GCP detect RBAC authorizations by account - Rule]
type = detection
asset_type = GCP GKE Kubernetes cluster
confidence = medium
explanation = 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
how_to_implement = You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
providing_technologies = []

[savedsearch://ESCU - Kubernetes GCP detect most active service accounts by pod - Rule]
type = detection
asset_type = GCP GKE Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision
how_to_implement = You must install splunk GCP add on. This search works with pubsub messaging service logs
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.
providing_technologies = []

[savedsearch://ESCU - Kubernetes GCP detect sensitive object access - Rule]
type = detection
asset_type = GCP GKE Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets
how_to_implement = You must install splunk add on for GCP . This search works with pubsub messaging service logs.
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
providing_technologies = []

[savedsearch://ESCU - Kubernetes GCP detect sensitive role access - Rule]
type = detection
asset_type = GCP GKE EKS Kubernetes cluster
confidence = medium
explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging servicelogs.
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use. 
providing_technologies = []

[savedsearch://ESCU - Kubernetes GCP detect service accounts forbidden failure access - Rule]
type = detection
asset_type = GCP GKE Kubernetes cluster
confidence = medium
explanation = 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
how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging service logs.
annotations = {"kill_chain_phases": ["Lateral Movement"]}
known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
providing_technologies = []

[savedsearch://ESCU - Kubernetes GCP detect suspicious kubectl calls - Rule]
type = detection
asset_type = GCP GKE Kubernetes cluster
confidence = medium
explanation = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging logs.
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 - Large Volume of DNS ANY Queries - Rule]
type = detection
asset_type = DNS Servers
confidence = medium
explanation = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.
how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
annotations = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1498.002"], "nist": ["PR.PT", "DE.AE", "PR.IP"]}
known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.
providing_technologies = []

[savedsearch://ESCU - MacOS - Re-opened Applications - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine.
how_to_implement = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]}
known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list.
providing_technologies = []

[savedsearch://ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule]
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.
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 = []

[savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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.
providing_technologies = []

[savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule]
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.
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 = []

[savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
annotations = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]}
known_false_positives = None at this time
providing_technologies = []

[savedsearch://ESCU - Monitor Email For Brand Abuse - Rule]
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.
annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
known_false_positives = None at this time
providing_technologies = []

[savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.
annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.010"], "nist": ["PR.PT", "DE.CM", "PR.AC"]}
known_false_positives = You will encounter noise from legitimate print-monitor registry entries.
providing_technologies = []

[savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule]
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.
annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
known_false_positives = None at this time
providing_technologies = []

[savedsearch://ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule]
type = detection
asset_type = Infrastructure
confidence = medium
explanation = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address.
how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search.
providing_technologies = []

[savedsearch://ESCU - NLTest Domain Trust Discovery - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["T1482"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Administrators may use nltest for troubleshooting purposes, otherwise, rarely used.
providing_technologies = []

[savedsearch://ESCU - New container uploaded to AWS ECR - Rule]
type = detection
asset_type = AWS ECR container
confidence = medium
explanation = 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.
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. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives.
annotations = {"mitre_attack": ["T1525"]}
known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry.
providing_technologies = []

[savedsearch://ESCU - Nishang PowershellTCPOneLine - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1059.001"]}
known_false_positives = Limited false positives may be present. Filter as needed based on initial analysis.
providing_technologies = []

[savedsearch://ESCU - No Windows Updates in a time frame - Rule]
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.
annotations = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]}
known_false_positives = None identified
providing_technologies = []

[savedsearch://ESCU - Ntdsutil Export NTDS - Rule]
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.
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 = []

[savedsearch://ESCU - O365 Add App Role Assignment Grant User - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.
how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
annotations = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003"]}
known_false_positives = The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.
providing_technologies = []

[savedsearch://ESCU - O365 Added Service Principal - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.
how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
annotations = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003"]}
known_false_positives = The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.
providing_technologies = []

[savedsearch://ESCU - O365 Bypass MFA via Trusted IP - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system.
how_to_implement = You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity
annotations = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1562.007"]}
known_false_positives = Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration.
providing_technologies = []

[savedsearch://ESCU - O365 Disable MFA - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user
how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
annotations = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"]}
known_false_positives = Unless it is a special case, it is uncommon to disable MFA or Strong Authentication
providing_technologies = []

[savedsearch://ESCU - O365 Excessive Authentication Failures Alert - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes
how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
annotations = {"kill_chain_phases": ["Not Applicable"], "mitre_attack": ["T1110"]}
known_false_positives = The threshold for alert is above 10 attempts and this should reduce the number of false positives.
providing_technologies = []

[savedsearch://ESCU - O365 Excessive SSO logon errors - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse.
how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
annotations = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1556"]}
known_false_positives = Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack.
providing_technologies = []

[savedsearch://ESCU - O365 New Federated Domain Added - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects the addition of a new Federated domain.
how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity.
annotations = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1136.003"]}
known_false_positives = The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider.
providing_technologies = []

[savedsearch://ESCU - O365 PST export alert - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content
how_to_implement = You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
annotations = {"kill_chain_phases": ["Actions on Objective"], "mitre_attack": ["T1114"]}
known_false_positives = PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored.
providing_technologies = []

[savedsearch://ESCU - O365 Suspicious Admin Email Forwarding - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination.
how_to_implement = 
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - O365 Suspicious Rights Delegation - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account.
how_to_implement = 
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = Service Accounts
providing_technologies = []

[savedsearch://ESCU - O365 Suspicious User Email Forwarding - Rule]
type = detection
asset_type = Office 365
confidence = medium
explanation = This search detects when multiple user configured a forwarding rule to the same destination.
how_to_implement = 
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.003"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Okta Account Lockout Events - Rule]
type = detection
asset_type = Infrastructure
confidence = medium
explanation = Detect Okta user lockout events
how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor.
providing_technologies = []

[savedsearch://ESCU - Okta Failed SSO Attempts - Rule]
type = detection
asset_type = Infrastructure
confidence = medium
explanation = Detect failed Okta SSO events
how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to.
providing_technologies = []

[savedsearch://ESCU - Okta User Logins From Multiple Cities - Rule]
type = detection
asset_type = Infrastructure
confidence = medium
explanation = This search detects logins from the same user from different cities in a 24 hour period.
how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint.
providing_technologies = []

[savedsearch://ESCU - Open Redirect in Splunk Web - Rule]
type = detection
asset_type = Splunk Server
confidence = medium
explanation = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability.
how_to_implement = No extra steps needed to implement this search.
annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
known_false_positives = None identified
providing_technologies = []

[savedsearch://ESCU - Osquery pack - ColdRoot detection - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for ColdRoot events from the osx-attacks osquery pack.
how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model
annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]}
known_false_positives = There are no known false positives.
providing_technologies = []

[savedsearch://ESCU - Overwriting Accessibility Binaries - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.008"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.
providing_technologies = []

[savedsearch://ESCU - Process Creating LNK file in Suspicious Location - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to 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.
annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.002"], "nist": ["ID.AM", "PR.DS"]}
known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories.
providing_technologies = []

[savedsearch://ESCU - Process Execution via WMI - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for processes 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.
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 = []

[savedsearch://ESCU - Processes Tapping Keyboard Events - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input
how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model.
annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]}
known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment.
providing_technologies = []

[savedsearch://ESCU - Processes created by netsh - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
providing_technologies = []

[savedsearch://ESCU - Processes launching netsh - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands.
providing_technologies = []

[savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule]
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.
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 = []

[savedsearch://ESCU - Prohibited Software On Endpoint - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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>.
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 = []

[savedsearch://ESCU - Protocol or Port Mismatch - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected.
how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports.
annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["DE.AE", "PR.AC"]}
known_false_positives = None identified
providing_technologies = []

[savedsearch://ESCU - Protocols passing authentication in cleartext - Rule]
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.
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 = []

[savedsearch://ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for reg.exe modifying registry keys that define Windows services and their configurations.
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 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1574.011"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]}
known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate.
providing_technologies = []

[savedsearch://ESCU - Reg exe used to hide files directories via registry keys - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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 = []

[savedsearch://ESCU - Registry Keys Used For Persistence - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for modifications to registry keys that can be used to launch an application or service at system startup.
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": ["T1547.001"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task.
providing_technologies = []

[savedsearch://ESCU - Registry Keys Used For Privilege Escalation - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.
how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other 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": ["T1546.011"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications
providing_technologies = []

[savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule]
type = detection
asset_type = Endpoint
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.
providing_technologies = []

[savedsearch://ESCU - Remote Desktop Network Traffic - Rule]
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.
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 = []

[savedsearch://ESCU - Remote Desktop Process Running On System - Rule]
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`.
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 = []

[savedsearch://ESCU - Remote Process Instantiation via WMI - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Remote Registry Key modifications - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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.
providing_technologies = []

[savedsearch://ESCU - Remote WMI Command Attempt - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for wmic.exe being launched with parameters to operate on remote systems.
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.
providing_technologies = []

[savedsearch://ESCU - RunDLL Loading DLL By Ordinal - Rule]
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.
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 = []

[savedsearch://ESCU - Ryuk Test Files Detected - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation.
how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. 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 = If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs.
providing_technologies = []

[savedsearch://ESCU - Ryuk Wake on LAN Command - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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", "Lateral Movement"], "mitre_attack": ["T1059.003"]}
known_false_positives = Limited to no known false positives.
providing_technologies = []

[savedsearch://ESCU - SMB Traffic Spike - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for spikes in the number of Server Message Block (SMB) traffic connections.
how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["DE.CM"]}
known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger.
providing_technologies = []

[savedsearch://ESCU - SMB Traffic Spike - MLTK - Rule]
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): \
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"]}
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
providing_technologies = []

[savedsearch://ESCU - SQL Injection with Long URLs - Rule]
type = detection
asset_type = Database Server
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.
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.
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.
providing_technologies = []

[savedsearch://ESCU - Sc exe Manipulating Windows Services - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service.
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 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1543.003"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]}
known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate.
providing_technologies = []

[savedsearch://ESCU - Scheduled Task Deleted Or Created via CMD - Rule]
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.
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 = []

[savedsearch://ESCU - Scheduled tasks used in BadRabbit ransomware - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 = []

[savedsearch://ESCU - Schtasks scheduling job on remote system - Rule]
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.
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 = []

[savedsearch://ESCU - Schtasks used for forcing a reboot - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled.
how_to_implement = To successfully implement this search 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 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc.
providing_technologies = []

[savedsearch://ESCU - Script Execution via WMI - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Shim Database File Creation - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]}
known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation.
providing_technologies = []

[savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule]
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.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]}
known_false_positives = None identified
providing_technologies = []

[savedsearch://ESCU - Short Lived Windows Accounts - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search detects accounts that were created and deleted in a short time period.
how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and 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/
annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136.001"], "nist": ["PR.IP"]}
known_false_positives = It is possible that an administrator created and deleted an account in a short time period.  Verifying activity with an administrator is advised.
providing_technologies = []

[savedsearch://ESCU - Single Letter Process On Endpoint - Rule]
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.
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 = []

[savedsearch://ESCU - Spectre and Meltdown Vulnerable Systems - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities.
how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified.
annotations = {"cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]}
known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied.
providing_technologies = []

[savedsearch://ESCU - Spike in File Writes - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for a sharp increase in the number of files written to a particular host
how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model 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 file system.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]}
known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications.
providing_technologies = []

[savedsearch://ESCU - Splunk Enterprise Information Disclosure - Rule]
type = detection
asset_type = Splunk Server
confidence = medium
explanation = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug.
how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives.
annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information.
providing_technologies = []

[savedsearch://ESCU - Sunburst Correlation DLL and Network Event - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = 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.
how_to_implement = This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days.
annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = []

[savedsearch://ESCU - Supernova Webshell - Rule]
type = detection
asset_type = 
confidence = medium
explanation = This search aims to detect the Supernova webshell used in the SUNBURST attack.
how_to_implement = To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model.
annotations = {"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"]}
known_false_positives = There might be false positives associted with this detection since items like args as a web argument is pretty generic.
providing_technologies = []

[savedsearch://ESCU - Suspicious Changes to File Associations - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes.
annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.001"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions.
providing_technologies = []

[savedsearch://ESCU - Suspicious Curl Network Connection - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["T1105"]}
known_false_positives = Unknown. Filter as needed.
providing_technologies = []

[savedsearch://ESCU - Suspicious DLLHost no Command Line Arguments - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1055"]}
known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
providing_technologies = []

[savedsearch://ESCU - Suspicious Email - UBA Anomaly - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 = []

[savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule]
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. \
 **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.
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 = []

[savedsearch://ESCU - Suspicious File Write - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
providing_technologies = []

[savedsearch://ESCU - Suspicious GPUpdate no Command Line Arguments - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1055"]}
known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
providing_technologies = []

[savedsearch://ESCU - Suspicious Java Classes - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts.
how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro.
annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]}
known_false_positives = There are no known false positives.
providing_technologies = []

[savedsearch://ESCU - Suspicious MSBuild Rename - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive.
providing_technologies = []

[savedsearch://ESCU - Suspicious MSBuild Spawn - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
providing_technologies = []

[savedsearch://ESCU - Suspicious PlistBuddy Usage - Rule]
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 \
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"]}
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.
providing_technologies = []

[savedsearch://ESCU - Suspicious PlistBuddy Usage via OSquery - Rule]
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 \
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"]}
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.
providing_technologies = []

[savedsearch://ESCU - Suspicious Reg exe Process - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule]
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.
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 = []

[savedsearch://ESCU - Suspicious Rundll32 Rename - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
providing_technologies = []

[savedsearch://ESCU - Suspicious Rundll32 StartW - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 Start as a function and call it via the command line. Filter as needed.
providing_technologies = []

[savedsearch://ESCU - Suspicious Rundll32 dllregisterserver - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 = This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names.
providing_technologies = []

[savedsearch://ESCU - Suspicious Rundll32 no Command Line Arguments - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
providing_technologies = []

[savedsearch://ESCU - Suspicious SQLite3 LSQuarantine Behavior - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["T1074"]}
known_false_positives = Unknown.
providing_technologies = []

[savedsearch://ESCU - Suspicious Scheduled Task from Public Directory - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1053.005"]}
known_false_positives = Limited false positives may be present. Filter as needed by parent process or command line argument.
providing_technologies = []

[savedsearch://ESCU - Suspicious SearchProtocolHost no Command Line Arguments - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1055"]}
known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
providing_technologies = []

[savedsearch://ESCU - Suspicious microsoft workflow compiler rename - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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": ["T1127, T1036.003"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive.
providing_technologies = []

[savedsearch://ESCU - Suspicious microsoft workflow compiler usage - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM.
providing_technologies = []

[savedsearch://ESCU - Suspicious msbuild path - Rule]
type = detection
asset_type = Endpoint
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.
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.
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.
providing_technologies = []

[savedsearch://ESCU - Suspicious mshta spawn - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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.
providing_technologies = []

[savedsearch://ESCU - Suspicious wevtutil Usage - Rule]
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.
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 = []

[savedsearch://ESCU - Suspicious writes to System Volume Information - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = 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.
providing_technologies = []

[savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search detects writes to the recycle bin by a process other than explorer.exe.
how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes.
annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate.
providing_technologies = []

[savedsearch://ESCU - System Information Discovery Detection - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it.
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 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]}
known_false_positives = Administrators debugging servers
providing_technologies = []

[savedsearch://ESCU - System Processes Run From Unexpected Locations - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location.  This can indicate a malicious process that is trying to hide as a legitimate process.
how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = None identified
providing_technologies = []

[savedsearch://ESCU - TOR Traffic - Rule]
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.
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 = []

[savedsearch://ESCU - USN Journal Deletion - Rule]
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.
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 = []

[savedsearch://ESCU - Uncommon Processes On Endpoint - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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 = []

[savedsearch://ESCU - Unified Messaging Service Spawning a Process - Rule]
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.
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.
providing_technologies = []

[savedsearch://ESCU - Unload Sysmon Filter Driver - Rule]
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.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]}
known_false_positives = 
providing_technologies = []

[savedsearch://ESCU - Unsigned Image Loaded by LSASS - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search detects loading of unsigned images by LSASS. Deprecated because too noisy.
how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 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 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs.
providing_technologies = []

[savedsearch://ESCU - Unsuccessful Netbackup backups - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search gives you the hosts where a backup was attempted and then failed.
how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution.
annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]}
known_false_positives = None identified
providing_technologies = []

[savedsearch://ESCU - Unusually Long Command Line - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts.
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"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Some legitimate applications start with long command lines.
providing_technologies = []

[savedsearch://ESCU - Unusually Long Command Line - MLTK - Rule]
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.
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 = []

[savedsearch://ESCU - Unusually Long Content-Type Length - Rule]
type = detection
asset_type = Web Server
confidence = medium
explanation = This search looks for unusually long strings in the Content-Type http header that the client sends the server.
how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field.
annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]}
known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters.
providing_technologies = []

[savedsearch://ESCU - W3WP Spawning Shell - Rule]
type = detection
asset_type = 
confidence = medium
explanation = 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.
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": ["T1505.003"]}
known_false_positives = Baseline your environment before production. It is possible build systems using IIS will spawn cmd.exe to perform a software build. Filter as needed.
providing_technologies = []

[savedsearch://ESCU - WBAdmin Delete System Backups - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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. Tune based on parent process names.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"]}
known_false_positives = Administrators may modify the boot configuration.
providing_technologies = []

[savedsearch://ESCU - WMI Permanent Event Subscription - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the creation of WMI permanent event subscriptions.
how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
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 event subscriptions for legitimate purposes.
providing_technologies = []

[savedsearch://ESCU - WMI Permanent Event Subscription - Sysmon - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the creation of WMI permanent event subscriptions.
how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.
annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.003"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes.
providing_technologies = []

[savedsearch://ESCU - WMI Temporary Event Subscription - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the creation of WMI temporary event subscriptions.
how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
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 = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events.
providing_technologies = []

[savedsearch://ESCU - Web Fraud - Account Harvesting - Rule]
type = detection
asset_type = Account
confidence = medium
explanation = 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.
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.
providing_technologies = []

[savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule]
type = detection
asset_type = account
confidence = medium
explanation = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session.
how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.
annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]}
known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior.
providing_technologies = []

[savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule]
type = detection
asset_type = account
confidence = medium
explanation = This search is used to identify user accounts that share a common password.
how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.
annotations = {"cis20": ["CIS 16"], "nist": ["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 anamoluous behavior.
providing_technologies = []

[savedsearch://ESCU - Web Servers Executing Suspicious Processes - Rule]
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.
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 = []

[savedsearch://ESCU - Windows AdFind Exe - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, 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": ["T1018"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = administrators rarely use adfind, usually not used for legitimate reasons
providing_technologies = []

[savedsearch://ESCU - Windows DisableAntiSpyware Registry - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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.
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 `Registry` node.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1562.001"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
providing_technologies = []

[savedsearch://ESCU - Windows Event Log Cleared - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for Windows events that indicate one of the Windows event logs has been purged.
how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts.
annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]}
known_false_positives = It is possible that these logs may be legitimately cleared by Administrators.
providing_technologies = []

[savedsearch://ESCU - Windows Security Account Manager Stopped - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints.
how_to_implement = You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. 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": ["T1489"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified.
providing_technologies = []

[savedsearch://ESCU - Windows connhost exe started forcefully - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = 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. 
how_to_implement = You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. 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": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = This process should not be ran forcefully, we have not see any false positives for this detection
providing_technologies = []

[savedsearch://ESCU - Windows hosts file modification - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for modifications to the hosts file on all Windows endpoints across your environment.
how_to_implement = To successfully implement this search, you must be ingesting data that records the 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 by 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 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]}
known_false_positives = There may be legitimate reasons for system administrators to add entries to this file.
providing_technologies = []

[savedsearch://ESCU - aws detect attach to role policy - Rule]
type = detection
asset_type = AWS Account
confidence = medium
explanation = This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges.
how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies.
providing_technologies = []

[savedsearch://ESCU - aws detect permanent key creation - Rule]
type = detection
asset_type = AWS Account
confidence = medium
explanation = This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor.
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context.
providing_technologies = []

[savedsearch://ESCU - aws detect role creation - Rule]
type = detection
asset_type = AWS Account
confidence = medium
explanation = This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges.
how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases.
providing_technologies = []

[savedsearch://ESCU - aws detect sts assume role abuse - Rule]
type = detection
asset_type = AWS Account
confidence = medium
explanation = This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse.
providing_technologies = []

[savedsearch://ESCU - aws detect sts get session token abuse - Rule]
type = detection
asset_type = AWS Account
confidence = medium
explanation = This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1550"]}
known_false_positives = Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used.
providing_technologies = []

[savedsearch://ESCU - gcp detect oauth token abuse - Rule]
type = detection
asset_type = GCP Account
confidence = medium
explanation = This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally.
how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
known_false_positives = GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs.
providing_technologies = []

### END DETECTIONS ###

### RESPONSE TASKS ###

[savedsearch://ESCU - AWS Investigate Security Hub alerts by dest - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - AWS Investigate User Activities By ARN - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - AWS Investigate User Activities By AccessKeyId - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - AWS Network ACL Details from ID - Response Task]
type = investigation
explanation = none
how_to_implement = In order to implement this search, 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 description inputs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - AWS Network Interface details via resourceId - Response Task]
type = investigation
explanation = none
how_to_implement = In order to implement this search, 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 configuration inputs
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - AWS S3 Bucket details via bucketName - Response Task]
type = investigation
explanation = none
how_to_implement = To implement this search, 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 inputs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - All backup logs for host - Response Task]
type = investigation
explanation = none
how_to_implement = The successfully implement this search you must first send your backup logs to Splunk.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Amazon EKS Kubernetes activity by src ip - Response Task]
type = investigation
explanation = none
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 Cloud Watch EKS inputs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - DNS Hijack Enrichment - Response Task]
type = investigation
explanation = none
how_to_implement = 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/`).\

known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Domain Certificate Investigation - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this phantom playbook, you must integrate Enterprise Security with Phantom. Configure this playbook in the correlation search `Detect DNS requests to Phishing Sites leveraging EvilGinx2` ,as an adaptive response action.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Excessive Account Lockouts Enrichment And Response - Response Task]
type = investigation
explanation = none
how_to_implement = Import playbook into phantom
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - GCP Kubernetes activity by src ip - Response Task]
type = investigation
explanation = none
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. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get All AWS Activity From City - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get All AWS Activity From Country - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get All AWS Activity From IP Address - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get All AWS Activity From Region - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Backup Logs For Endpoint - Response Task]
type = investigation
explanation = none
how_to_implement = You must be ingesting your backup logs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Certificate logs for a domain - Response Task]
type = investigation
explanation = none
how_to_implement = You must be ingesting your certificates or SSL logs from your network traffic into your Certificates datamodel. Please note the wildcard(*) before domain in the search syntax, we use to match for all domain and subdomain combinations
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get DNS Server History for a host - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search, you must be ingesting your DNS traffic
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get DNS traffic ratio - Response Task]
type = investigation
explanation = none
how_to_implement = You must be ingesting your network traffic
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get EC2 Instance Details by instanceId - Response Task]
type = investigation
explanation = none
how_to_implement = In order to implement this search, 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 description inputs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get EC2 Launch Details - Response Task]
type = investigation
explanation = none
how_to_implement = In order to implement this search, 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 description inputs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Email Info - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting your email logs or capturing unencrypted network traffic which contains email communications.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Emails From Specific Sender - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get History Of Email Sources - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Logon Rights Modifications For Endpoint - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting your Windows event logs
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Logon Rights Modifications For User - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting your Windows event logs
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Notable History - Response Task]
type = investigation
explanation = none
how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.
known_false_positives = not defined
earliest_time_offset = 14400
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Process File Activity - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Process Info - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Process Information For Port Activity - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events and populate the Endpoint Datamodel
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Process Responsible For The DNS Traffic - Response Task]
type = investigation
explanation = none
how_to_implement = You must be ingesting endpoint data that associates processes with network events into the Endpoint datamodel. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Sysmon WMI Activity for Host - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Web Session Information via session id - Response Task]
type = investigation
explanation = none
how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate AWS User Activities by user field - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate AWS activities via region name - Response Task]
type = investigation
explanation = none
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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Failed Logins for Multiple Destinations - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Network Traffic From src ip - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Okta Activity by IP Address - Response Task]
type = investigation
explanation = none
how_to_implement = You must be ingesting Okta logs
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Okta Activity by app - Response Task]
type = investigation
explanation = none
how_to_implement = You must be ingesting Okta logs
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Pass the Hash Attempts - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you need be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security 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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Pass the Ticket Attempts - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you need to be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security 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.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Previous Unseen User - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Successful Remote Desktop Authentications - Response Task]
type = investigation
explanation = none
how_to_implement = You must be populating the Authentication data model with security events from your Windows event logs.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Suspicious Strings in HTTP Header - Response Task]
type = investigation
explanation = none
how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate User Activities In Okta - Response Task]
type = investigation
explanation = none
how_to_implement = You must be ingesting Okta logs
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Web POSTs From src - Response Task]
type = investigation
explanation = none
how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model.
known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0

### END RESPONSE TASKS ###

### BASELINES ###
[savedsearch://ESCU - Baseline Of Cloud Infrastructure API Calls Per User]
type = support
explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline Of Cloud Instances Destroyed]
type = support
explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are destroyed in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances destroyed in a small time window.
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 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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline Of Cloud Instances Launched]
type = support
explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are created in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.
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.\
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline Of Cloud Security Group API Calls Per User]
type = support
explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls for security groups are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of API Calls per User ARN]
type = support
explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of Command Line Length - MLTK]
type = support
explanation = 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.
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of DNS Query Length - MLTK]
type = support
explanation = 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.
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK]
type = support
explanation = 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.
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.\
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK]
type = support
explanation = 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.
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.\
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of Network ACL Activity by ARN]
type = support
explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
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 API event names for network ACLs, edit the macro `network_acl_events`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of S3 Bucket deletion activity by ARN]
type = support
explanation = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of SMB Traffic - MLTK]
type = support
explanation = 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.
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of Security Group Activity by ARN]
type = support
explanation = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
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 API event names for security groups, edit the macro `security_group_api_calls`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Baseline of blocked outbound traffic from AWS]
type = support
explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly.
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.`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Count of Unique IPs Connecting to Ports]
type = support
explanation = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them.
how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Count of assets by category]
type = support
explanation = This search shows you every asset category you have and the assets that belong to those categories.
how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Create a list of approved AWS service accounts]
type = support
explanation = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file.
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 service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - DNSTwist Domain Names]
type = support
explanation = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches.
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**.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Discover DNS records]
type = support
explanation = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup
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
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Identify Systems Creating Remote Desktop Traffic]
type = support
explanation = This search counts the numbers of times the system has generated remote desktop traffic.
how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Identify Systems Receiving Remote Desktop Traffic]
type = support
explanation = This search counts the numbers of times the system has created remote desktop traffic
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Identify Systems Using Remote Desktop]
type = support
explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system.
how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Monitor Successful Backups]
type = support
explanation = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate.
how_to_implement = To successfully implement this search you must be ingesting your backup logs.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Monitor Unsuccessful Backups]
type = support
explanation = This search is intended to give you a feel for how often backup failures happen in your environments.  Fluctuations in these numbers will allow you to determine when you should investigate.
how_to_implement = To successfully implement this search you must be ingesting your backup logs.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen AWS Cross Account Activity]
type = support
explanation = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
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. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen AWS Cross Account Activity - Initial]
type = support
explanation = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen AWS Cross Account Activity - Update]
type = support
explanation = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen AWS Provisioning Activity Sources]
type = support
explanation = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen AWS Regions]
type = support
explanation = 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
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud API Calls Per User Role - Initial]
type = support
explanation = This search builds a table of the first and last times seen for every user role and command combination. This is broadly defined as any event that runs or creates something. This table is then cached.
how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud API Calls Per User Role - Update]
type = support
explanation = This search updates the table of the first and last times seen for every user role and command combination.
how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Compute Creations By User - Initial]
type = support
explanation = This search builds a table of previously seen users that have launched a cloud compute instance.
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Compute Creations By User - Update]
type = support
explanation = This search builds a table of previously seen users that have launched a cloud compute instance.
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Compute Images - Initial]
type = support
explanation = This search builds a table of previously seen images used to launch cloud compute instances
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Compute Images - Update]
type = support
explanation = This search builds a table of previously seen images used to launch cloud compute instances
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Compute Instance Types - Initial]
type = support
explanation = This search builds a table of previously seen cloud compute instance types
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Compute Instance Types - Update]
type = support
explanation = This search builds a table of previously seen cloud compute instance types
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Instance Modifications By User - Initial]
type = support
explanation = This search builds a table of previously seen users that have modified a cloud instance.
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Instance Modifications By User - Update]
type = support
explanation = This search updates a table of previously seen Cloud Instance modifications that have been made by a user
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Provisioning Activity Sources - Initial]
type = support
explanation = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. This table is then cached.
how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Provisioning Activity Sources - Update]
type = support
explanation = This returns the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity within the last day. Cloud provisioning is broadly defined as any event that runs or creates something.  It then updates this information with historical data and filters out locations that have not been seen within the specified time window. This updated table is then cached.
how_to_implement = You must be ingesting Cloud infrastructure logs from your cloud provider.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Regions - Initial]
type = support
explanation = 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
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Cloud Regions - Update]
type = support
explanation = 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
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen EC2 AMIs]
type = support
explanation = This search builds a table of previously seen AMIs used to launch EC2 instances
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen EC2 Instance Types]
type = support
explanation = This search builds a table of previously seen EC2 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 CloudTrail inputs.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen EC2 Launches By User]
type = support
explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen EC2 Modifications By User]
type = support
explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance.
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`.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Running Windows Services - Initial]
type = support
explanation = This collects the services that have been started across your entire enterprise.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Running Windows Services - Update]
type = support
explanation = This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Users In CloudTrail - Update]
type = support
explanation = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by user, within the last hour.
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_users_console_logins`, which is a lookup file created by this support search.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Users in CloudTrail - Initial]
type = support
explanation = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by username, within the last 30 days.
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_users_console_logins`, which is a lookup file created by this support search.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Zoom Child Processes - Initial]
type = support
explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously Seen Zoom Child Processes - Update]
type = support
explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously seen API call per user roles in CloudTrail]
type = support
explanation = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.
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 role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously seen S3 bucket access by remote IP]
type = support
explanation = 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"
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously seen command line arguments]
type = support
explanation = 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.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Previously seen users in CloudTrail]
type = support
explanation = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. NOTE - This baseline search is deprecated and has been 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 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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch]
type = support
explanation = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown.
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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Update previously seen users in CloudTrail]
type = support
explanation = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. NOTE - This baseline search is deprecated and has been 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 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.
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Windows Updates Install Failures]
type = support
explanation = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned.
how_to_implement = You must be ingesting your Windows Update Logs
known_false_positives = not defined
providing_technologies = none

[savedsearch://ESCU - Windows Updates Install Successes]
type = support
explanation = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned.
how_to_implement = You must be ingesting your Windows Update Logs
known_false_positives = not defined
providing_technologies = none

### END ESCU BASELINES ###