splunk-security_content/package/default/use_case_library.conf

#############
# Automatically generated by generator.py in splunk/security-content
# On Date: 2020-04-17T19:06:18 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############

### STORIES ###

[analytic_story://AWS Cross Account Activity]
category = Cloud Security
last_updated = 2018-06-04
version = 1.0
references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History", "ESCU - Previously Seen AWS Cross Account Activity"]
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 = 1.0
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 AMIs", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen EC2 Launches By User"]
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. \
hen 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 = 2.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of Network ACL Activity by ARN", "ESCU - Baseline of blocked outbound traffic from AWS"]
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 Suspicious Provisioning Activities]
category = Cloud Security
last_updated = 2018-03-16
version = 1.0
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region", "ESCU - Previously Seen AWS Provisioning Activity Sources"]
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 whitelist specific IPs (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 = 1.0
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Investigate AWS User Activities by user field", "ESCU - Baseline of API Calls per User ARN", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Create a list of approved AWS service accounts", "ESCU - Previously seen API call per user roles in CloudTrail"]
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://Account Monitoring and Controls]
category = Best Practices
last_updated = 2017-09-06
version = 1.0
references = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}, {"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts.
narrative = Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity.

[analytic_story://Apache Struts Vulnerability]
category = Vulnerability
last_updated = 2018-12-06
version = 1.0
references = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"]
maintainers = [{"company": "Splunk", "email": "jhernandez@splunk.com", "name": "Jose Hernandez"}]
spec_version = 2
searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Investigate Web POSTs From src"]
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.\
hen 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 = 1.0
references = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Count of assets by category"]
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://Brand Monitoring]
category = Abuse
last_updated = 2017-12-19
version = 1.0
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": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
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 Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - DNSTwist Domain Names"]
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 = 1.0
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Compute Instance Types", "ESCU - Previously Seen Cloud Regions"]
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://ColdRoot MacOS RAT]
category = Malware
last_updated = 2019-01-09
version = 1.0
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": "jhernandez@splunk.com", "name": "Jose Hernandez"}]
spec_version = 2
searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"]
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 = 1.1
references = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"]
maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Email servers sending high volume traffic to hosts - 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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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 = 1.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of blocked outbound traffic from AWS"]
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 = 1.0
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": "research@splunk.com", "name": "Splunk Research Team"}]
spec_version = 2
searches = ["ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule", "ESCU - Get Certificate logs for a domain"]
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 & Investigation]
category = Cloud Security
last_updated = 
version = 1.0
references = ["https://github.com/splunk/cloud-datamodel-security-research"]
maintainers = [{"company": "Splunk", "email": "rsoto@splunk.com, rvaldez@splunk.com", "name": "Rod Soto, Rico Valdez"}]
spec_version = 2
searches = ["ESCU - GCP GCR container uploaded - Rule", "ESCU - New container uploaded to AWS ECR - Rule", "ESCU - Investigate AWS ECR container listing activity"]
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 = 3.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}, {"company": "Splunk", "email": "pbareiss@splunk.com", "name": "Patrick Bareiss"}]
spec_version = 2
searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Previous Unseen User"]
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 = 2.0
references = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"]
maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Create local admin accounts using net.exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Registry Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"]
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 = 1.0
references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User"]
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 = 1.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Discover DNS records"]
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 Protection]
category = Abuse
last_updated = 2017-09-14
version = 1.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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://Disabling Security Tools]
category = Adversary Tactics
last_updated = 2020-02-04
version = 2.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"]
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 blacklist (which would prevent them from running).

[analytic_story://Dynamic DNS]
category = Malware
last_updated = 2018-09-06
version = 2.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src_ip"]
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 blacklists.
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 blacklists 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 = 1.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security", "ESCU - Baseline of SMB Traffic - MLTK"]
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://Hidden Cobra Malware]
category = Malware
last_updated = 2020-01-22
version = 2.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Create or delete windows shares using net.exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"]
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 = 1.0
references = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"]
maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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 = 1.0
references = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"]
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.\
hen 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 = 1.0
references = ["https://github.com/splunk/cloud-datamodel-security-research"]
maintainers = [{"company": "Splunk", "email": "rsoto@splunk.com", "name": "Rod Soto"}]
spec_version = 2
searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes activity by src_ip", "ESCU - GCP Kubernetes activity by src_ip", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"]
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://Lateral Movement]
category = Adversary Tactics
last_updated = 2020-02-04
version = 2.0
references = ["https://blog.binarydefense.com/reliably-detecting-pass-the-hash-through-event-log-analysis", "https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["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 Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Successful Remote Desktop Authentications"]
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 = 4.0
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": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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 = 1.0
references = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Monitor Successful Backups", "ESCU - Monitor Unsuccessful Backups"]
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 = 1.0
references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security"]
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 = 1.0
references = ["https://learn.cisecurity.org/20-controls-download"]
maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"]
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://Netsh Abuse]
category = Abuse
last_updated = 2017-01-05
version = 1.0
references = ["https://technet.microsoft.com/library/bb490939.aspx", "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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"]
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://Orangeworm Attack Group]
category = Malware
last_updated = 2020-01-22
version = 2.0
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": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Previously Seen Running Windows Services", "ESCU - Previously seen command line arguments"]
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.\
Although the group's motivation is unknown, its goal may be stealing patient information to sell on the black market. Another possible explanation is corporate espionage. \
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 = 1.0
references = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"]
maintainers = [{"company": "Splunk", "email": "research@splunk.com", "name": "Splunk Research Team"}]
spec_version = 2
searches = ["ESCU - Detect Oulook.exe writing a .zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Get Parent Process Info"]
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. \
hile 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 = 1.0
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": "iDefense.IntelOps@accenture.com", "name": "iDefense Cyber Espionage Team"}]
spec_version = 2
searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"]
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 = 1.0
references = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"]
maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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 = 1.1
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": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK"]
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://Router & Infrastructure Security]
category = Best Practices
last_updated = 2017-09-12
version = 1.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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://SQL Injection]
category = Adversary Tactics
last_updated = 2017-09-19
version = 1.0
references = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"]
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 = 1.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security"]
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://Spectre And Meltdown Vulnerabilities]
category = Vulnerability
last_updated = 2018-01-08
version = 1.0
references = ["https://meltdownattack.com/"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Systems Ready for Spectre-Meltdown Windows Patch"]
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 = 1.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"]
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 = 1.0
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": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"]
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 = 1.0
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 Launches By User"]
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 = 1.0
references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}, {"company": "Splunk", "email": "jbrewer@splunk.com", "name": "Jason Brewer"}]
spec_version = 2
searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Previously seen users in CloudTrail", "ESCU - Update previously seen users in CloudTrail"]
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 = 2.0
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Baseline of S3 Bucket deletion activity by ARN", "ESCU - Previously seen S3 bucket access by remote IP"]
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 = 1.0
references = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of blocked outbound traffic from AWS"]
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 Command-Line Executions]
category = Adversary Tactics
last_updated = 2020-02-03
version = 2.1
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"]
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 = 1.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of DNS Query Length - MLTK"]
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 = 1.0
references = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - DNSTwist Domain Names"]
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 MSHTA Activity]
category = Adversary Tactics
last_updated = 2020-02-03
version = 1.1
references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect mshta.exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"]
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 white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution of malicious files, whether an .hta application, javascript, or VBScript.\
One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\
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.

[analytic_story://Suspicious Okta Activity]
category = Adversary Tactics
last_updated = 2020-04-02
version = 1.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Multiple Okta Users With Invalid Credentails 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", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate User Activities In Okta"]
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 WMI Use]
category = Adversary Tactics
last_updated = 2018-10-23
version = 2.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get User Information from Identity Table"]
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 = 1.0
references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"]
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://Unusual AWS EC2 Modifications]
category = Cloud Security
last_updated = 2018-04-09
version = 1.0
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History", "ESCU - Previously Seen EC2 Modifications By User"]
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 = 2.1
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": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK"]
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 = 1.0
references = ["https://www.monkey.org/~dugsong/dsniff/"]
maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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 = 1.0
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": "Mayhem@splunk.com", "name": "Jim Apger"}]
spec_version = 2
searches = ["ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Web Session Information via session_id"]
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.\
hen 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 Defense Evasion Tactics]
category = Adversary Tactics
last_updated = 2018-05-31
version = 1.0
references = ["https://attack.mitre.org/wiki/Defense_Evasion"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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 = 1.0
references = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"]
maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"]
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 = 2.0
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": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"]
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 = 2.0
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": "davidd@splunk.com", "name": "David Dorsey"}, {"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}]
spec_version = 2
searches = ["ESCU - Detect Path Interception By Creation Of program.exe - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"]
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 = 2.0
references = ["https://attack.mitre.org/tactics/TA0004/"]
maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}]
spec_version = 2
searches = ["ESCU - Child Processes of Spoolsv.exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"]
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 = 3.0
references = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"]
maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}]
spec_version = 2
searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Previously Seen Running Windows Services"]
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 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from cities that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.
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 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 = ["AWS"]

[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each country. It returns only those events from countries that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.
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 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 = ["AWS"]

[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.
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 = ["AWS"]

[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from regions that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action.
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 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 = ["AWS"]

[savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search\
1. Retrieves the **AssumeRole** event\
1. Verifies that the log entry contains a value for the account ID of the requesting account\
1. Ensures that the requesting account ID does not match the account ID of the requested account\
1. Pulls in the previously seen requesting and requested account IDs\
1. Splits up and executes multiple search paths at the same.\
1. The first path determines the **firstTime** and **lastTime** entries for the cache file\
1. Outputs the data to the cache file.\
1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\
The search finishes by gathering the data that it will display to the 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. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search.
annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "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 = ["AWS"]

[savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = A network access control list (ACL) is a layer of security for your VPC that acts as a firewall for controlling traffic in and out of one or more subnets. Network ACLs with all open ports have a larger attack surface. This search looks for events within your CloudTrail logs to check if there were any Network ACLs created with ports ranging from 1024 to 65525. This search will create a table comprised of AWS account id, src, user and all parameters of the request made by the user and the server response.
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": ["Persistence"], "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 = ["AWS"]

[savedsearch://ESCU - AWS Network Access Control List Deleted - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event.
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": ["Persistence"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = It's possible that a user has legitimately deleted a network ACL.
providing_technologies = ["AWS"]

[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average.
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": ["Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances launched by a particular user against the saved baseline data in the model ec2_excessive_runinstances_v1.
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": ["Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes.
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": ["Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances terminated by a particular user against the saved baseline data in the model ec2_excessive_terminateinstances_v1.
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": ["Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Access LSASS Memory for Dump Creation - Rule]
type = detection
asset_type = Windows
confidence = high
explanation = dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Amazon EKS Kubernetes Pod scan detection - Rule]
type = detection
asset_type = Amazon EKS Kubernetes cluster Pod
confidence = medium
explanation = In this search we can detect unauthenticated web requests against an EKS cluster Pod, by looking at k8s authentication data, user agent and source IPs and API direct request.
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": ["Discovery"], "mitre_technique_id": ["T1190"]}
known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context.
providing_technologies = ["AWS"]

[savedsearch://ESCU - Amazon EKS Kubernetes cluster scan detection - Rule]
type = detection
asset_type = Amazon EKS Kubernetes cluster
confidence = high
explanation = In this search we can detect unauthenticated web requests against an EKS cluster, by looking at k8s authentication data, user agent and source IPs.
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": ["Discovery"], "mitre_technique_id": ["T1190"]}
known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context.
providing_technologies = ["AWS"]

[savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" 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": ["Defense Evasion", "Disabling Security Tools"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks for changes of the ExecutionPolicy in the registry. The ExecutionPolicy is a safety feature that controls the conditions under which PowerShell loads configuration files and runs scripts. Usually, the ExecutionPolicy is "Restricted" for Windows clients and "RemoteSigned" for Windows Servers, allowing only certain scripts to run. This search detects when an attacker sets the ExecutionPolicy to "Unrestricted" or "Bypass."
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": ["Execution", "PowerShell", "Scripting"], "mitre_technique_id": ["T1086", "T1064"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Attempt To Stop Security Service - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor.
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": ["Defense Evasion", "Disabling Security Tools"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]}
known_false_positives = None identified.
providing_technologies = ["Sysmon"]

[savedsearch://ESCU - Batch File Write to System32 - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious.
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": [], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[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, which is associated with the Print Spooler service on Windows. Children of this process typically run under the SYSTEM context. This search should address the POC developed for the Windows local-privilege-escalation exploit announced in September of 2018. The associated vulnerability was assigned CVE-2018-8440. More information is available at https://doublepulsar.com/task-scheduler-alpc-exploit-high-level-analysis-ff08cda6ad4f.
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": ["Privilege Escalation", "Exploitation for Privilege Escalation"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = DNS Queries with multiple DNS servers from a single client is unusual and may be indicative of malicious activity. This search works by performing a count by the source of the distinct destinations for the DNS traffic. The search uses the `Network_Resolution` data model.
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": ["Command and Control", "Exfiltration", "Exfiltration Over Alternative Protocol"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule]
type = detection
asset_type = Cloud Compute Instance
confidence = medium
explanation = For each user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each user back to the lookup file and then clears out any output. The other part of the search limits the results to when the user was seen for the first time within the previous 70 minutes. It then displays the new user, the instances created by that user, and the associated times.
how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users.
annotations = {"cis20": ["CIS 1"], "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 = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule]
type = detection
asset_type = Cloud Compute Instance
confidence = medium
explanation = For each image ID and user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each image back to the lookup file and clears out any output. The other part of the search limits the results to when the image was seen for the first time within the previous 70 minutes. It then displays the new image, the instances created using it, the user who created it, and the associated times.
how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Images" support search to create a baseline of previously seen images.
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 = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule]
type = detection
asset_type = Cloud Compute Instance
confidence = medium
explanation = For each instance type and user, the search returns the first time seen, last time seen, and the system. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each instance type back to the lookup file and clears out any output. The other part of the search limits the results to when the instance type was seen for the first time within the previous 70 minutes. It then displays the new instance type, the instances created using it, the user who created them, and the times associated.
how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the " Previously Seen Cloud Compute Instance Types" support search to create a baseline of previously seen regions.
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 = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule]
type = detection
asset_type = Cloud Compute Instance
confidence = medium
explanation = In this search, we query cloud infrastructure compute logs to look for events that indicate that an instance was started in a particular region. Using the \"previously_seen_cloud_regions\" lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The \"eval\" and \"if\" functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with \"Instance Started in a New Region.\" However, this region will be added to the list in \"previously_seen_cloud_regions.\"
how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search to create a baseline of previously seen regions.
annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "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 = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Common Ransomware Extensions - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated 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": [], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Common Ransomware Notes - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at file modifications in the Change Analysis data model. It checks modified file names against an included lookup file, which contains the names of note files left behind by ransomware (to inform the victim how they can pay the ransom and retrieve their files). The search returns a list of files with matching names.
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 products, 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": [], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Create Remote Thread into LSASS - Rule]
type = detection
asset_type = Windows
confidence = high
explanation = This search detects the creation of a remote thread into LSASS (Local Security Authority Subsystem Service). This technique can be used by attackers to inject code into LSASS and dump the memory in order to obtain credentials.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Create local admin accounts using net.exe - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Net.exe is a built-in Windows command-line tool that can be used to add, display, or modify user accounts. While Microsoft administrators use this tool to manage user groups, threat actors often leverage it to create local admin accounts to maintain persistence. In this search, we are looking for the execution of process net.exe with command-line parameters such as `localgroup`, `add`, or `user` that may correspond to the creation of local admin accounts or setting user/group properties.
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": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Administrators often leverage net.exe to create admin accounts.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Create or delete windows shares using net.exe - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation/deletion of windows drive shares. Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete (hidden) file shares by appending "$" after the name of the share. Since the creation/deletion of hidden shares is a special case of detecting share creation/deletion we have commented out the regex that adds that additional matching criteria. If only hidden share detection is desired add `| regex process="\S+[$]"` before the last pipe in the search.
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": ["Lateral Movement"], "mitre_technique_id": ["T1077", "T1126"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Creation of Shadow Copy - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers will first create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using Ntdsutil, Vssadmin, or Wmic.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]}
known_false_positives = Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives.
providing_technologies = ["Sysmon"]

[savedsearch://ESCU - Creation of Shadow Copy with wmic and powershell - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using wmic, which is executed by Powershell.
how_to_implement = You must enable Powershell scriptblock logging in order to detect this attack.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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]}
known_false_positives = Legtimate administrator usage of wmic to create a shadow copy.
providing_technologies = ["Microsoft Windows"]

[savedsearch://ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = The file system, security, sam and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied. But it is possible by creating first a shadow copy and then copy it from the shadow copy. This search will detect this attack of credential dumping.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = ["Sysmon"]

[savedsearch://ESCU - Credential Dumping via Symlink to Shadow Copy - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = The file system, security, sam, and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied, but it can be done by creating shadow copy and then create a symlink to the shadow copy. This search will detect this attack of credential dumping.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]}
known_false_positives = unknown
providing_technologies = ["Sysmon"]

[savedsearch://ESCU - DNS Query Length Outliers - MLTK - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers often use random, long domain names for components of their attack infrastructure. This search leverages the probability distribution function algorithm provided by the Machine Learning Toolkit (MLTK) to identify outliers in the length of the DNS query for each record type observed. The companion search "Baseline of DNS Query Length - MLTK" creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
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": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers often use random, long domain names for their attack infrastructure. This search looks at all the queries observed over the search time frame, and identifies any domains being resolved with names that are greater that 2 times the standard deviation.
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": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
known_false_positives = It's possible there can be long domain names that are legitimate.
providing_technologies = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers.
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": ["Exfiltration", "Defense Evasion"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - DNS record changed - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Using a lookup `discover_dns_records` generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed.
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": ["Exfiltration", "Command and Control", "Defense Evasion"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Deleting Shadow Copies - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for execution of vssadmin or wmic with both the "delete" and "shadows" parameters passed on the command-line. The two arguments are searched for separately because we can't predict the number of spaces between the words on the command-line. The search will return the number of times this activity was observed, and the times of the first and last event.
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": ["Impact"], "mitre_technique_id": ["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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect API activity from users without MFA - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation =  In this search, we query CloudTrail logs and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of the 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. 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"], "mitre_attack": ["Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Detect AWS API Activities From Unapproved Accounts - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we are looking for successful API calls via CloudTrail. We filter out events triggered by known users listed in the `identity_lookup_expanded` lookup file and the service accounts. Once filtered out, we output a table with the event names and count, as well as the first and last time a specific user or service is detected.
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": ["Credential Access", "Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Detect AWS Console Login by User from New City - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new city and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and city combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.
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": ["Credential Access"], "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 = ["AWS"]

[savedsearch://ESCU - Detect AWS Console Login by User from New Country - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new country and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and country combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.
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": ["Credential Access"], "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 = ["AWS"]

[savedsearch://ESCU - Detect AWS Console Login by User from New Region - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new region and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and region combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.
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": ["Credential Access"], "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 = ["AWS"]

[savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = To detect pass the hash activity, we look at all events with event code 4624 that specify a logon type 3 (network logons) for remote pass the hash attacks and logon type 9 for local pass the hash attacks. The search also filters out events with an account name of 'Anonymous' to help reduce false positives.
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": ["Lateral Movement", "Pass the Hash"], "mitre_technique_id": ["T1075"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Detect Credential Dumping through LSASS access - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search looks for LSASS access using Credential Dumping tools by detecting Process access with Sysmon logs (EventCode 10), TargetImage lsass.exe and GrantedAccess 0x1410 or 0x1010. This will for example detect the use of sekurlsa::logonpasswords in Mimikatz.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search gathers all the answers to each system's DNS query, then filters for queries that have subdomains extracted from the EvilGinx toolkit. It will then run a regex to extract `legit_domains` from the query and remove that from the detection if it is listed in the `legit_domains.csv`
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": ["Spearphishing Link", "Command and Control"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Detect Excessive Account Lockouts From Endpoint - Rule]
type = detection
asset_type = Windows
confidence = low
explanation = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times an endpoint has caused an account lockout within a four hour window and displays those hosts with a count greater than or equal to five.
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": ["Initial Access", "Valid Accounts"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times a user  has caused an account lockout within a four hour window and displays those users with a count greater than or equal to five.
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": ["Initial Access", "Valid Accounts"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search works by looking at fields in the Network_Traffic data model, which is populated by various firewalls and passive networking monitoring technologies. Specifically, the search looks for ICMP packets larger than 1,000 bytes with a destination that is external to your organization.
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": ["Command and Control", "Standard Non-Application Layer Protocol"], "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 search to adjust the byte threshold or whitelist specific IP addresses, as necessary.
providing_technologies = ["Bro", "Splunk Stream", "Palo Alto Firewall"]

[savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length.
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": ["Command and Control", "Exfiltration", "Commonly Used Port"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Detect Mimikatz Using Loaded Images - Rule]
type = detection
asset_type = Windows
confidence = high
explanation = This search looks for loaded images (dll) unique for Mimikatz using Sysmon EventCode 7 logs.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.AE", "DE.CM"]}
known_false_positives = Other tools can import the same DLLs. These tools should be part of a whtelist.
providing_technologies = ["Microsoft Windows"]

[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search looks for Windows Event Code(signature_id) 4703 (token right adjusted), where the process requesting the token change is PowerShell.exe and the requested privilege is "SeDebugPrivilege". This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event.
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": ["Credential Access", "Credential Dumping"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Detect New Local Admin account - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search looks for Windows Event Code 4720 (account creation) and 4732 (account added to a security-enabled local group), where the group name is "Administrators", and determines whether they are generated for the same user's Security ID within three hours of each other.  It will return the user account that was added, the Security ID, the group name to which the user was added, the account name of the user who initiated the action, and the subsequent message returned.
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.\
This search produces fields (`Security_ID`,`Group_Name`,`Message`) 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:** Security ID, **Field:** Security_ID\
1. \
1. **Label:** Group Name, **Field:** Group_Name\
1. \
1. **Label:** Message, **Field:** Message\
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", "Command and Control"], "mitre_attack": ["Valid Accounts", "Defense Evasion", "Persistence"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Detect New Login Attempts to Routers - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers will often attempt to compromise network devices such as routers for a variety of nefarious purposes, including modifying VPN settings or re-routing network traffic. Typically, only a relatively small number of user accounts log into these devices on a regular basis. This search identifies 'new' connections to your routers by checking to see if a similar login was made in the last 30 days. Routers are identified by checking the IP address against those categorized as a "router" in the ES assets and identity framework.
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 = ["Active Directory", "Palo Alto Firewall"]

[savedsearch://ESCU - Detect New Open S3 buckets - Rule]
type = detection
asset_type = S3 Bucket
confidence = medium
explanation = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the 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), and 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": ["Execution", "Initial Access", "Exfiltration"], "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 = ["AWS"]

[savedsearch://ESCU - Detect Oulook.exe writing a .zip file - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = In this search, we are essentially trying to detect if outlook.exe is writing a `.zip` file to the disk. The way this search would run is, it will execute the the subsearch first which looks for all .zip files being written to the disk and outputs a crucial field "process_id", that we use the main search to check if that process\_id belongs to a process_name of outlook.exe. The search uses a join command to essentially give you an end result of the first and last time that zip file was written by outlook.exe, the dest and user logged on the system, the hash value and the complete path to the zip file on 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 products, 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": ["Initial Access", "Spearphishing Attachment"], "nist": ["ID.AM", "PR.DS"]}
known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect Outbound SMB Traffic - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = In this search, we are looking for the network connections that were not blocked by the firewall and that are destined for destination port 139 or 445. We then filter out events that have Classless Inter-Domain Routing (CIDR) blocks categorized as internal in the `assets_by_cidr.csv` lookup file which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. Since we are only looking for outbound traffic from the hosts made to the Internet, we filter out traffic whose destination IP address is private.
how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in `assets_by_cidr.csv` lookup file, which is located in `$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": ["Commonly Used Port", "Credential Access", "Lateral Movement"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "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 = ["Bro", "Splunk Stream"]

[savedsearch://ESCU - Detect Path Interception By Creation Of program.exe - Rule]
type = detection
asset_type = 
confidence = medium
explanation = This search queries the Endpoint file-system data model node to list out all the values of destination machines, as well as the values of file hashes and file paths that have the file "program.exe" in the C: drive. Path interception occurs when an executable is placed in a specific path so that it is executed by an application instead of by the intended target. In this case, applications vulnerable to path interception (because of unquoted service paths with spaces in Windows registry) allow attackers to execute maliciously crafted program.exes.
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. This is typically populated via endpoint detection-and-response products, 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 file system reads and writes.
annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Privilege Escalation", "Persistence"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = It is unlikely that a normal user may create and place this file in the C: drive.  Confirm with the user.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of 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": ["Execution", "Command-Line Interface"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = In this search, we are looking for the PsExec process with `accepteula` 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 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface"], "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 = ["Sysmon"]

[savedsearch://ESCU - Detect Rare Executables - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search first executes the subsearch and counts all of your processes to determine the 10 most rare (the limit set is 10). It then filters out whitelisted processes and outputs the first and last time a rare process was encountered, the destination where the process is running, the count of occurrences, and the users who initiated the processes.
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_whitelist` searches two lookup files to whitelist your processes.  These consist of `rare_process_whitelist_default.csv` and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, add them to `rare_process_whitelist_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 whitelist 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"], "mitre_attack": ["Execution"], "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_whitelist_local.csv` to filter them out of your search results.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect S3 access from a new IP - Rule]
type = detection
asset_type = S3 Bucket
confidence = low
explanation = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource 
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": ["Execution", "Exfiltration"], "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 = ["AWS"]

[savedsearch://ESCU - Detect Spike in AWS API Activity - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search and its corresponding subsearch run through a series of steps, as per the following: \
1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\
1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\
1. Counts the number of API calls per ARN.\
1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\
1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \
1. Renames `apiCalls` as `latestCount`.\
1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\
1. Updates the cache file with the latest results.\
1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\
1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\
1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average.
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": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Detect Spike in Network ACL Activity - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search and its corresponding subsearch run through the following series of steps: \
1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for creating/modifying/replacing network Access Control Lists (ACLs).\
1. Kick off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\
1. Count the number of API calls per Amazon Resource Name (ARN).\
1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\
1. Drop the count from the latest hour, since it is not necessary, and merge the rest of the data with the results of the stats command. \
1. Rename `apiCalls` as `latestCount`.\
1. Calculate the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. They do the same for the standard deviation--weighting the past more heavily than the current.\
1. Update the cache file with the latest results.\
1. Set the minimum threshold for the number of data points and set the number of standard deviations away from the mean it must be to be considered a spike.\
1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\
1. Filter out anything that it determines is not a spike and return the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average.
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": ["Persistence", "Exfiltration"], "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 = ["AWS"]

[savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule]
type = detection
asset_type = S3 Bucket
confidence = medium
explanation = This search and its corresponding subsearch run through the following series of steps: \
1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\
1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\
1. Count the number of API calls per ARN.\
1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\
1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \
1. Rename `apiCalls` as `latestCount`.\
1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation&#151;weighting the past more heavily than the current.\
1. Update the cache file with the latest results.\
1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\
1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\
1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs.
how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your 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": ["Credential Access", "Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Detect Spike in Security Group Activity - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search and its corresponding subsearch run through the following series of steps: \
1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls specifically for security groups.\
1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\
1. Counts the number of API calls per ARN.\
1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\
1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \
1. Renames `apiCalls` as `latestCount`.\
1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\
1. Updates the cache file with the latest results.\
1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\
1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\
1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average.
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": ["Credential Access", "Execution"], "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 = ["AWS"]

[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \
1. Counts the number of blocked outbound connections by each source IP\
1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\
1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \
1. Renames `numberOfBlockedConnections` as `latestCount`.\
1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\
1. Updates the cache file with the latest results.\
1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\
1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\
1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average.
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"], "mitre_attack": ["Exfiltration", "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 = ["AWS"]

[savedsearch://ESCU - Detect USB device insertion - Rule]
type = detection
asset_type = Endpoint
confidence = low
explanation = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device
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"], "mitre_attack": ["Exfiltration"], "nist": ["PR.PT", "PR.DS"]}
known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate.
providing_technologies = ["Microsoft Windows"]

[savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule]
type = detection
asset_type = Infrastructure
confidence = medium
explanation = This search requires you to leverage the Enterprise Security Assets and Identity framework to populate assets_by_str.csv. Once the assets_by_str.csv is populated, we then query your DHCP logs to detect unknown systems connecting to your network. More documentation is available at: http://docs.splunk.com/Documentation/ES/4.7.1/Admin/Verifyassetandidentitydata.
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"], "mitre_attack": ["Defense Evasion"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers often leverage various scripting languages to execute their attacks. In a Windows environment, the Windows Script Host is the tool that interprets the scripts and is included in all modern versions of Windows. The Windows Script Host is available as a command-line tool called "cscript.exe" or "wscript.exe." To detect this behavior, the search looks for process-creation events for cscript.exe or wscript.exe with a parent process of cmd.exe. The search will return the count, the first and last times this behavior was seen on a destination machine, and user and process information.
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": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Some legitimate applications may exhibit this behavior.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule]
type = detection
asset_type = Web Server
confidence = medium
explanation = This search returns the number of times a URL associated with this type of JexBoss probe is observed.
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": ["Discovery", "System Information Discovery"]}
known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths.
providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"]

[savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search leverages an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query. It checks the results against the list of Dynamic DNS providers in the lookup `dynamic_dns_providers` by each host (DNS.src).
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": ["Exfiltration", "Defense Evasion"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule]
type = detection
asset_type = Web Server
confidence = high
explanation = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers.
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"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "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 = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"]

[savedsearch://ESCU - Detect mshta.exe running scripts in command-line arguments - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Mshta.exe is a built-in Windows utility that can launch HTML files with .hta extensions (HTML applications), javascript, or VBScript. The search detects this behavior by looking for events where the process mshta.exe is executed with command-line arguments that indicate that a script is invoked
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": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect new API calls from user roles - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs.
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"], "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 = ["AWS"]

[savedsearch://ESCU - Detect new user AWS Console Login - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour.
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": ["Credential Access"], "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 = ["AWS"]

[savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = Attackers have a range of built-in Windows tools they leverage to ascertain the topography of a network from the point of view of a compromised machine. It is uncommon to see these commands execute quickly within short periods of time. This search returns the number of times, as well as the first time and last times, that every process has run for each endpoint. It then executes the macro `system_network_configuration_discovery_tools`, which looks for processes that are typically used for network configuration discovery. Once you have a list of suspicious process launches for each destination, you can leverage the transaction command to see what processes are fired within a five-minute span on an endpoint and detect only those events where the count of these processes is greater than five.
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 products, 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": ["Execution"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks for hosts in your environment that may be communicating with a dynamic DNS provider. It checks each URL an endpoint is connecting to against a list of dynamic DNS providers. It returns the source and destination IP address of the web request, the URL requested, and the first time the event occurred.
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`
annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Command and Control", "Web Service", "Exfiltration Over Command and Control Channel", "Defense Evasion"], "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 = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"]

[savedsearch://ESCU - Detection of DNS Tunnels - Rule]
type = detection
asset_type = Endpoint
confidence = low
explanation = The search will calculate the distinct count and sum of the length of DNS queries made and DNS answers received by a particular host to alert the analyst if the combined length is greater than 10000, which is not typical behavior.
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": ["Command and Control", "Exfiltration", "Commonly Used Port"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Detection of tools built by NirSoft - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for process-creation events accompanied by specific command-line arguments ("scomma" and "stext"). These parameters may be leveraged by a set of free, legitimate tools built by NirSoft. Attackers have been seen abusing the tools' capabilities to steal passwords, set up key loggers, recover account information from mail clients, and conduct other nefarious activities. The search will identify the count, the first and last times a process is executed, the command-line arguments, and the parent 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"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Discovery", "Execution", "Lateral Movement", "Third-party Software", "Account Discovery"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Disabling Remote User Account Control - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search checks to see if the registry key SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy was modified.  This registry key can be used to disable remote User Account Control.  The search returns the count, the first time activity was seen, last time activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification.
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 products, 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": ["Defense Evasion", "Modify Registry"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Dump LSASS via comsvcs DLL - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = LSASS is the Local Security Authority Subsystem Service, which is responsible for storing the user credentials. There are multiple ways to attack LSASS. This search detects 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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]}
known_false_positives = None identified.
providing_technologies = ["Sysmon"]

[savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2_modification_api_calls`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance ID of those systems.
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"], "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 = ["AWS"]

[savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv`
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.
annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "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 = ["AWS"]

[savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch returns the AMI image ID of all successful EC2 instance launches within the last hour and then appends the historical data from the lookup file to those results.  It then recalculates the earliest and latest seen time field for each AMI image ID and returns only those AMI image IDs that have first been seen in the past hour.  This is combined with the main search to return the time, user, and instance id of those systems.
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 = ["AWS"]

[savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results.  It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour.  This is combined with the main search to return the time, user, and instance id of those systems.
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 = ["AWS"]

[savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule]
type = detection
asset_type = AWS Instance
confidence = medium
explanation = The subsearch returns the ARNs of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results.  It then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour.  This is combined with the main search to return the time, user, and instance id of those systems.
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"], "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 = ["AWS"]

[savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at any emails with file attachment names that contain many spaces, relative to the length of the file name. Specifically, it checks to see whether spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will output the message ID of the email, the count, the sender and recipient addresses, the first and last time this event was seen, and the space ratio of the file attachment name.
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"], "mitre_attack": [], "nist": ["PR.IP"]}
known_false_positives = None at this time
providing_technologies = ["Microsoft Exchange"]

[savedsearch://ESCU - Email files written outside of the Outlook directory - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\.
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 products, 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": ["Collection", "Email Collection"]}
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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. 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.  We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages.
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": ["Collection", "Email Collection", "Commonly Used Port"], "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 = ["Bro", "Splunk Stream"]

[savedsearch://ESCU - Excessive DNS Failures - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks at DNS traffic with a reply code that is NOT indicative of a successful response. Numerous unsuccessful replies may be indicative of DNS protocol tampering or other malicious activity. If more than 50 of these unsuccessful responses are observed over the time frame of the search, a notable event will be generated.
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": ["Exfiltration", "Exfiltration Over Alternative Protocol", "Command and Control", "Commonly Used Port"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search uses the endpoint data model to look for process names with at least five spaces between the file name and its extension.
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": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
known_false_positives = None identified.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Execution of File with Multiple Extensions - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for.
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": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
known_false_positives = None identified.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search finds all the successful backup messages in your logs, and then looks for the most recent backup time for each system. It then identifies those systems where the most recent successful backup time is over a week ago, and reports on them.
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 = ["Netbackup"]

[savedsearch://ESCU - File with Samsam Extension - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at file modifications across your hosts and creates notable events when it identifies files with extensions associated with the SamSam ransomware, including `.stubbin`, `.berkshire`, `.satoshi`, `.sophos`, or `.keyxml`. Files with these extensions have been observed in SamSam attacks consisting of payload data or keying material.
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"], "mitre_attack": [], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - First Time Seen Running Windows Service - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for a change in the status of a Windows service and extracts the name of the service and the action taken by the service. Then the cache file of previously seen Windows services is added to the search. At this point, the search takes two different paths: the first updates the cache file with the latest information and the second searches for services that have never before been seen. It returns the time, the Windows host name, and the service name.
how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, should be run before this search to create the baseline of known Windows services. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above.
annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Execution", "New Service"], "mitre_technique_id": ["T1050"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - First time seen command line argument - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past one hour. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument.
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": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "mitre_technique_id": ["T1059", "T1117", "T1202"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - GCP GCR container uploaded - Rule]
type = detection
asset_type = GCP GCR Container
confidence = medium
explanation = In this search we can detect if a new container has been uploaded to Google Container Registry, operator can monitor users uploading containers, object paths of new uploaded containers.
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": ["Persistence"], "mitre_technique_id": ["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 = ["GCP"]

[savedsearch://ESCU - GCP Kubernetes cluster scan detection - Rule]
type = detection
asset_type = GCP Kubernetes cluster
confidence = high
explanation = In this search we can detect unauthenticated web requests and possible attack against a GCP cluster, by looking at k8s authentication data, user agent, source IPs and destionation
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": ["Discovery"], "mitre_technique_id": ["T1190"]}
known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context.
providing_technologies = ["GCP"]

[savedsearch://ESCU - Hiding Files And Directories With Attrib.exe - Rule]
type = detection
asset_type = 
confidence = medium
explanation = This search is looking to detect command-line execution with of attrib.exe binary with the +h flag set.  The +h flag is used to hide a file.
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": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]}
known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. 
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Hosts receiving high volume of network traffic from email server - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. 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.  We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages.
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": ["Collection", "Commonly Used Port"], "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 = ["Bro", "Splunk Stream"]

[savedsearch://ESCU - Identify New User Accounts - Rule]
type = detection
asset_type = Domain Server
confidence = medium
explanation = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time.
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": ["Persistence", "Create Account"], "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 = ["Active Directory"]

[savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule]
type = detection
asset_type = DNS Servers
confidence = high
explanation = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set.
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"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - MacOS - Re-opened Applications - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks at the Endpoint data model to identify any MacOS process events referencing a property list file which determines which applications are "re-opened" during startup. This could indicate a malicious attempt to establish persistence on the system.
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"], "mitre_attack": ["Persistence"], "mitre_technique_id": ["T1164"], "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 whitelisted.
providing_technologies = ["OSquery"]

[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 running with specific command-line arguments that indicate that the process will download a file from the Internet without display anything to the user. The search for "*-Exec*" is to check and see if the default execution policy for PowerShell is being overridden on the command-line. The search for "*-WindowStyle*" and "*hidden*" are to see if the window that would normally be displayed will be hidden from the user instead. Finally, the search for "*New-Object*" and "*System.Net.WebClient*" are there to check to see if a PowerShell object that can be used to download files will be created. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process.
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": ["Execution", "PowerShell", "Scripting"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell.  This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process.
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": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
known_false_positives = System administrators may use this option, but it's not common.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for PowerShell processes that were launched using a parameter designed to bypass the local PowerShell execution policy. By default, the policy is set to "Restricted," which disables the execution of PowerShell scripts. In environments that make heavy use of PowerShell, the policy can be set to allow only scripts signed by a trusted publisher. Malicious PowerShell use almost always includes the parameter `-ExecutionPolicy bypass`. PowerShell is very liberal when it comes to interpreting command-line parameters passed to it. For example, the parameter we look for, `-ExecutionPolicy`, can be abbreviated to `-Execution`, `-Exec`, or even `-ex`. As such, we look for `* -ex*`, which should catch all variations of this parameter, followed by the keyword `bypass`. This search will return the host, the user the process ran under, the process and its command-line arguments, the number of times it has seen this process, and the first and last times it saw this process.
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": ["Execution", "PowerShell", "Scripting"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[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 that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process.
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": ["Execution", "PowerShell", "Scripting"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for PowerShell processes that are passing command-line arguments with unusual characters (backticks and carets) that are PowerShell specific escape characters. Attackers use this obfuscation technique since it does not affect the functionality of PowerShell and it will bypass standard security controls that look for straight up malicious strings and commands. The search counts the occurrence of these obfuscation characters and lists out destination IPs running these PowerShell commands.
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": ["Execution", "PowerShell", "Scripting"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search gathers all the answers to each system's DNS query, then filters out all queries that do not appear on the list of faux "look-a-like" domains that have been generated from the brand abuse domains you are monitoring.
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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Monitor Email For Brand Abuse - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at the sender address in email headers, and identifies those with a sender address using a domain name that matches the list of permutations generated for the domain you want to monitor.
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 = ["Microsoft Exchange", "Bro", "Splunk Stream"]

[savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = In this search, we look for modifications to registry keys used for adding print-monitor entries on Microsoft platforms via the `registry_path` field in the endpoint data model. It then provides the destination, command used to initiate the change, the user who conducted this activity, the resource affected (registry_key_name), and the entire path of the registry.
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 products, 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": ["Persistence", "Privilege Escalation", "Local Port Monitor"], "nist": ["PR.PT", "DE.CM", "PR.AC"]}
known_false_positives = You will encounter noise from legitimate print-monitor registry entries.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand 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"], "mitre_attack": [], "nist": ["PR.IP"]}
known_false_positives = None at this time
providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"]

[savedsearch://ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule]
type = detection
asset_type = Infrastructure
confidence = high
explanation = This search detects instances when there are more than 5 distinct users failing Okta logins due to invalid credentails from the same IP address. This may be indicative of attack techniques such as credential stuffing or password spraying, where an attacker attempts to login using common or found passwords and attempts to authenticate with them.
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": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "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 XXXXXXXXXXXXX to raise the threshold or except specific IP adresses from triggering this search.
providing_technologies = ["Okta"]

[savedsearch://ESCU - New container uploaded to AWS ECR - Rule]
type = detection
asset_type = AWS ECR container
confidence = medium
explanation = In this search we can detect if a new container has been uploaded to Amazon Elastic Container Registry, operator can monitor users uploading containers, image ids of new uploaded containers.
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": ["Persistence"], "mitre_technique_id": ["T1525"]}
known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry.
providing_technologies = ["AWS"]

[savedsearch://ESCU - No Windows Updates in a time frame - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Keeping your systems up-to-date with the latest patches is an important step in keeping your systems secured. For Windows endpoints, Microsoft typically releases patches on the second Tuesday of every month. These patches contain fixes for vulnerabilities in the system that could potentially be exploited by malicious actors. This search checks for messages regarding Windows updates in the 'Update' data model. If a message indicating a successful update has not been observed in 60 days, a notable event will be generated. These systems should be checked to determine why it has not been updated in that time frame.
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 = ["Microsoft Windows"]

[savedsearch://ESCU - Okta Account Lockout Events - Rule]
type = detection
asset_type = Infrastructure
confidence = high
explanation = This search detects when a user exceeds the maximum configured Okta login attempts and the account is subsequently locked out. This is often indicative of brtue force attempts against a user account.
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": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "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 = ["Okta"]

[savedsearch://ESCU - Okta Failed SSO Attempts - Rule]
type = detection
asset_type = Infrastructure
confidence = high
explanation = This search looks for events that indicate a user attempted to access an app they did not have permissions to access. This could indicate attempts to access prohibited applications. Please leverage the `okta_failed_sso_attempt_filter` macro to filter out false positives
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": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "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 = ["Okta"]

[savedsearch://ESCU - Okta User Logins From Multiple Cities - Rule]
type = detection
asset_type = Infrastructure
confidence = high
explanation = This search detects users logging in from multiple states in the last 24 hours. This can be indicative of an attacker using compromised credentials to log in to Okta. The efficacy of this search is highly dependant on the mobility of the users using Okta. It is particularly useful in situations where users should explicitly *not* be travelling, such as during the COVID-19 pandemic.
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": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "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 = ["Okta"]

[savedsearch://ESCU - Open Redirect in Splunk Web - Rule]
type = detection
asset_type = Splunk Server
confidence = medium
explanation = This search looks within Splunk's internal logs for evidence of CVE-2016-4859 open redirect exploitation attempts.
how_to_implement = No extra steps needed to implement this search.
annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
known_false_positives = None identified
providing_technologies = ["Splunk Enterprise"]

[savedsearch://ESCU - Osquery pack - ColdRoot detection - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks at the Alerts data model to identify those  generated from the osquery osx-attacks.conf pack, which search for the ColdRoot RAT.
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"], "mitre_attack": ["Execution", "Persistence", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]}
known_false_positives = There are no known false positives.
providing_technologies = ["OSquery"]

[savedsearch://ESCU - Overwriting Accessibility Binaries - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search returns all the different accessibility binaries that have been modified for each Windows host.
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": ["Persistence", "Accessibility Features"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Process Execution via WMI - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for processes launched via WMI, either remotely or locally, by looking for processes launched by WmiPrvSE.exe, which is the process WMI uses to execute new processes and commands.
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": ["Execution", "Windows Management Instrumentation"], "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 = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Processes Tapping Keyboard Events - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search leverages Alerts generated from the osquery osx-attacks.conf pack search `Keyboard_Event_Taps` to detect when a process is monitoring the keystrokes of a machine, This is a common technique used by macOS remote access trojans to log keystrokes from a machine
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"], "mitre_attack": ["Collection"], "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 = ["OSquery"]

[savedsearch://ESCU - Processes created by netsh - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for all processes with the parent process "c:\Windows\System32\netsh.exe" and returns the process, the command line used to execute it, the host name, and the user context under which it ran.
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": ["Execution", "Command-Line Interface", "Persistence"], "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.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Processes launching netsh - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for all the parent processes of netsh.exe and returns that process, the command-line used to execute it, the host name, and the user context under which it ran.
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": ["Execution", "Command-Line Interface", "Persistence", "Defense Evasion", "Disabling Security Tools"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model.
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": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "nist": ["DE.AE", "PR.AC"]}
known_false_positives = None identified
providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"]

[savedsearch://ESCU - Prohibited Software On Endpoint - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search returns the number of times, as well as the first and last time, every process has run for each endpoint and user. It then displays only those processes that you have marked as "prohibited" in the Enterprise Security "Interesting Processes" table.
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 products, 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"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]}
known_false_positives = None identified
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Protocol or Port Mismatch - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for instances in which the protocol observed is not consistent with the port and transport protocol typically used for that protocol. For example, looking for network traffic other than HTTP running over TCP port 80. Such behavior could indicate a misconfiguration or a custom command and control protocol that has been designed to look like ordinary web traffic. The search will also identify if HTTP traffic is observed running on unexpected ports. This can be common in many environments.
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": ["Command and Control", "Commonly Used Port"], "nist": ["DE.AE", "PR.AC"]}
known_false_positives = None identified
providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"]

[savedsearch://ESCU - Protocols passing authentication in cleartext - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search is checking for traffic on well-known ports that are associated with protocols that pass authentication in cleartext.
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"], "mitre_attack": ["Credential Access", "Lateral Movement", "Collection"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks for modifications to registry paths that specify the definition and configuration of Windows services by reg.exe. Reg.exe is a Windows utility that allows for manipulation of the registry via the command line. Malware often uses the Windows services architecture to persist, hide in plain sight, and gain the ability to interact with the Windows kernel. While it is common to modify the configuration of Windows services (and new services may be created with software installs), the use of reg.exe to create or modify a service configuration is unusual and a technique commonly used by attackers. The search returns the count, the first time the activity was seen, the last time activity was seen, the registry path that was modified, the host where the modification took place, and the user that performed the modification.
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 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Reg.exe used to hide files/directories via registry keys - Rule]
type = detection
asset_type = 
confidence = medium
explanation = Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory.
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": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]}
known_false_positives = None at the moment
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Registry Keys Used For Persistence - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for specific registry paths that malware often uses to ensure survivability and persistence on system startup. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification.
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 products, 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": ["Persistence", "Registry Run Keys / Start Folder", "AppInit DLLs", "Authentication Package"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Registry Keys Used For Privilege Escalation - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for specific registry paths that malware often uses to elevate privileges. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place, and the user who performed the modification.
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 products, 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": ["Privilege Escalation", "Persistence", "Accessibility Features"], "mitre_technique_id": ["T1183"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor.
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 products, 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": ["Persistence", "Application Shimming"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack.
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": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "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 = ["Bro", "Splunk Stream"]

[savedsearch://ESCU - Remote Desktop Network Traffic - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search finds systems that do not commonly communicate use remote desktop.  It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system.  Categories are applied to systems using the Assets and Identity framework.
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": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
known_false_positives = Remote Desktop may be used legitimately by users on the network.
providing_technologies = ["Bro", "Splunk Stream"]

[savedsearch://ESCU - Remote Desktop Process Running On System - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search finds systems that do not commonly use remote desktop, but which begin using it. It filters out all systems that have the "common_rdp_source" category applied. Categories are applied to systems using 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": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
known_false_positives = Remote Desktop may be used legitimately by users on the network.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Remote Process Instantiation via WMI - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers are increasingly abusing native Windows utilities such as wmic.exe as a means to "live off the land", and avoid introducing new executables to the target system. In this search, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators.
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": ["Execution", "Windows Management Instrumentation"], "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 = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Remote Registry Key modifications - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for modifications made to the Windows registry from remote locations using reg.exe&#151;a tool used to create/update/delete/modify Windows registry keys. It is accomplished through specifying the machine names in the registry path, by entering double backslashes, followed by a computer name. In this search, we look for registry changes where the registry path contains the name of a remote computer. The search returns the number of times the remote server has been accessed, the first and last times the activity occurred, the name of the modified registry path, the host on which the modification took place, and the name of the user that performed the modification.
how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response products, 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": ["Defense Evasion", "Persistence", "Lateral Movement"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Remote WMI Command Attempt - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators.
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": ["Execution", "Windows Management Instrumentation"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - RunDLL Loading DLL By Ordinal - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for rundll32.exe being run, loading a DLL out of a directory or subdirectory of AppData, and specifying the function at ordinal 2 be run.
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": ["Execution", "Rundll32"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - SMB Traffic Spike - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic.
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": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]}
known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger.
providing_technologies = ["Bro", "Splunk Stream"]

[savedsearch://ESCU - SMB Traffic Spike - MLTK - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers often leverage Server Message Block (SMB) traffic, a protocol used for Windows file-sharing activity. A high-profile example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search leverages Splunk's Machine Learning Toolkit (MLTK) to identify spikes in SMB traffic that are unusual for a given hour of day/day of week combination. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
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": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "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 = ["Bro", "Splunk Stream"]

[savedsearch://ESCU - SQL Injection with Long URLs - Rule]
type = detection
asset_type = Database Server
confidence = medium
explanation = This search looks only at your web servers and returns the source, the web server, the URL and its length, and the user agent associated with HTTP GET requests for extremely long URLs or user agent lengths with more than three common SQL commands found within the URL.
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": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Commonly Used Port"], "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 = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Samsam Test File Write - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at file modifications across your hosts and monitors for a file named "test.txt" written to "windows\system32". This file is copied to potential targets during SamSam ransomware attacks to test the attacker's ability to access remote systems. If the file is successfully copied to the system, the system is added to a list of targets on which to deploy ransomware.
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": [], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = No false positives have been identified.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Sc.exe Manipulating Windows Services - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the execution of sc.exe with parameters that indicate the utility is being used to create a new Windows service, or modify an existing one. Attackers often create a new service to host their malicious code, or they may take a non-critical service or one that is disabled, and modify it to point to their malware and enable the service if necessary. It is unusual for a service to be created or modified using the sc.exe utility.
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"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for execution of schtasks.exe with parameters that indicate that a specific task "reset," whose name is associated with the Dragonfly threat actor--has been created or deleted. Schtasks.exe is a native Windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence.
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": ["Execution", "Scheduled Task"], "nist": ["PR.IP"]}
known_false_positives = No known false positives
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Scheduled tasks used in BadRabbit ransomware - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for execution of schtasks.exe with parameters that indicate that specific task names related to the Bad Rabbit ransomware were created or deleted. The specific task name used are rhaegal, drogon and viserion_. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence.
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": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task"], "nist": ["PR.IP"]}
known_false_positives = No known false positives
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Schtasks scheduling job on remote system - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables 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.
annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Privilege Escalation", "Execution", "Scheduled Task"], "mitre_technique_id": ["T1053"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Schtasks used for forcing a reboot - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled that would cause a forced reboot on the host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware.
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": ["Persistence", "Execution", "Scheduled Task"], "nist": ["PR.IP"]}
known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Script Execution via WMI - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers are increasingly abusing Windows Management Infrastructure for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for scripts launched via WMI, either remotely or locally, by looking for the execution of scrcons.exe, which is the scripting host used by WMI, similar to wscript or cscript.
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": ["Execution", "Windows Management Instrumentation"], "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 = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Shim Database File Creation - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks for files being created in `Windows\AppPatch\Custom and Windows\AppPatch\Custom64`, the location where shim databases are installed. It will return all the files created, as well as the time of creation for the first and last file for each endpoint.
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": ["Persistence", "Application Shimming"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p.  The -q option performs a silent installation with no visible window, status, or warning information.  The -p option allows the shim database to contain patches.  It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user.
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": ["Persistence", "Application Shimming"], "nist": ["DE.CM"]}
known_false_positives = None identified
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Short Lived Windows Accounts - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search looks for Windows Event Logs 4720 (account creation) and 4726 (account deletion) and determines if they happen for the same user within 4 hours of each other.  It will report the user and machine that reported the events and the time it first and last saw this activity.
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": ["Persistence", "Create Account"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Single Letter Process On Endpoint - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search returns all the processes for each endpoint and user and filters out any process that isn't 5 characters long and ends with .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 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Spectre and Meltdown Vulnerable Systems - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks for the three CVEs associated with 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 = ["Nessus", "Qualys"]

[savedsearch://ESCU - Spike in File Writes - Rule]
type = detection
asset_type = Endpoint
confidence = low
explanation = This search calculates counts the number of file modification events per hour per host in your environment. It then takes the average and standard deviations of those numbers and displays any hosts with more than 20 events that have over four times the standard deviation more than the average number of file modifications.
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 products, 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"], "mitre_attack": ["Execution"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Splunk Enterprise Information Disclosure - Rule]
type = detection
asset_type = Splunk Server
confidence = medium
explanation = This search searches Splunk's internal logs for evidence of CVE-2018-11409 exploitation attempts.
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"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "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 = ["Splunk Enterprise"]

[savedsearch://ESCU - Suspicious Changes to File Associations - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for changes made to the registry that control Windows file associations. It is typical for users to change the file association to open certain types of files with specific applications. However, when these changes are legitimately performed, they are typically done via the processes explorer.exe or openwith.exe. The search first executes the subsearch that looks at the Registry node, which specifies setting a value in the registry and creates a table of process_id and dest. It then uses those arguments to find out what process and parent process were responsible for making those registry changes.
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": ["Persistence", "Change Default File Association"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Suspicious Email - UBA Anomaly - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This detection monitors for emails that are suspicious because of their sender, domain rareness, or behavior differences, as determined by Splunk UBA. In this search, we query the "UEBA" data model to look for anomalies that are raised by the "SuspiciousEmailDetectionModel" and will output the count, description of the anomaly, signature, the type of event in UBA, the severity, and the user who received a potentially suspicious email from a newly seen domain. It will also output all the categories associated with that anomaly.
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": [], "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 whitelist the URL if you determine that it is a legitimate sender.
providing_technologies = ["Microsoft Exchange"]

[savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at any email messages with attachments and checks the file names of those attachments against an included lookup file to see if it has a suspicious file extension.
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": ["Execution", "Defense Evasion"], "mitre_technique_id": ["T1193"], "nist": ["DE.AE", "PR.IP"]}
known_false_positives = None identified
providing_technologies = ["Microsoft Exchange"]

[savedsearch://ESCU - Suspicious File Write - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at files being created or modified in the Endpoint file-system data model. The names of those files are checked against an included lookup file, which contains the names of files associated with malware or attack activity. The search returns any files with matching names, along with a note (also specified in the lookup file) that gives or points to more information about the files.
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 products, 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"], "mitre_attack": [], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

[savedsearch://ESCU - Suspicious Java Classes - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search leverages HTTP form data from typically POST events that can be captured with Splunk streams or similar wire data capture tools. The search looks for java classes like `processbuilder` and `runtime` are used to create a new process and execute commands inside java, and are synonymous with spawning a shell. There are very exceptional reasons to ever these classes in Java via an HTTP API and hence when seen are highly suspicious. Also, this is a common vectors leverage to exploit 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"], "mitre_attack": ["Execution"], "nist": ["DE.AE"]}
known_false_positives = There are no known false positives.
providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"]

[savedsearch://ESCU - Suspicious LNK file launching a process - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = In this search, we are essentially trying to detect if a LNK file created under the C:\User* or *\Local\Temp\* directory structures is launching a process with in 1 hour of its creation. LNK files or also known as Windows shortcut files are commonly associated with phishing and are a [preferred method used for exploitation](https://www.fireeye.com/blog/threat-research/2017/04/fin7-phishing-lnk.html).
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 products, 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": ["Initial Access", "Spearphishing Attachment"], "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 execute process from temporary or user directories.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Suspicious Reg.exe Process - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the execution of reg.exe with a parent process of cmd.exe. It then executes a subsearch looking for those cmd.exe processes with a parent that is not explorer.exe. It then joins those two searches to make sure that the reg.exe process is a grandchild of the non explorer.exe process. The search will return the number of such instances and the first and last time this activity has been seen on each endpoint and user.
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": ["Defense Evasion", "Modify Registry", "Disabling Security Tools"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Suspicious wevtutil Usage - Rule]
type = detection
asset_type = 
confidence = medium
explanation = This search looks for execution of wevtutil.exe with command-line arguments that indicate that it has been used to delete the setup, application, security, or system event logs. The search returns the number of times the behavior was observed, the first and last time it was seen, the host exhibiting the behavior and the user context of the process execution.
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": ["Defense Evasion", "Indicator Removal on Host"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Suspicious writes to System Volume Information - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search uses data on file writes captured via Sysmon to watch for writes to the "System Volume Information" folder by processes other than the system process. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. It then looks for a file created with a path that includes "System Volume Information" and a process ID (PID) other than 4. PID 4 is assigned to the System process on Windows systems. Excluding these writes allows us to filter out legitimate activity. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity.
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": ["Collection", "Data Staged"], "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 = ["Sysmon"]

[savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity.
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": ["Collection", "Data Staged"], "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 = ["Sysmon"]

[savedsearch://ESCU - System Processes Run From Unexpected Locations - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search returns all the processes that are not executing out of the C:\Windows\System32 or C:\Windows\SysWOW64 directories. Next, it takes the filename and looks it up in a table `is_windows_system_file` of files that should normally run out of the C:\Windows\System32 or C:\Windows\SysWOW64 directory. Any matches are then returned.
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": ["Defense Evasion", "Masquerading"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = None identified
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - TOR Traffic - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = The search leverages the Enterprise Security Network_Traffic data model to look for network traffic that has been identified as TOR and marked as 'allowed'.
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": ["Command and Control", "Commonly Used Port", "Exfiltration"], "nist": ["DE.AE"]}
known_false_positives = None at this time
providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"]

[savedsearch://ESCU - USN Journal Deletion - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = This search looks for the execution of fsutil.exe with command-line arguments to delete the USN journal. The search returns the count of the number of times it's seen this process execution with these arguments, the first and last time it's seen this behavior, the hosts it was executed on, and the user context under which it was executed.
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": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]}
known_false_positives = None identified
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Uncommon Processes On Endpoint - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search returns the number of times, as well as the first and last time, it has seen every process run for each endpoint and user, and then displays only those processes that you have marked as uncommon in the `uncommon_processes_default.csv` table.
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": ["Execution", "Accessibility Features"], "nist": ["ID.AM", "PR.DS"]}
known_false_positives = None identified
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Unload Sysmon Filter Driver - Rule]
type = detection
asset_type = 
confidence = medium
explanation = This search is looking to detect execution of `fltMC.exe` that specifically used for unloading the Sysmon Filter Driver
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": ["Disabling Security Tools"], "mitre_technique_id": ["T1089"], "nist": ["DE.CM"]}
known_false_positives = 
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Unsigned Image Loaded by LSASS - Rule]
type = detection
asset_type = Windows
confidence = medium
explanation = This search detects unsigned images loaded by LSASS (Local Security Authrity Subsystem Service). Normally, LSASS only loads signed images. Therefore, it is a malicious indicator when unsigned images are loaded by LSASS. This can be an indicator for credential dumping using tools like Windows Credential Editor.
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": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Unsuccessful Netbackup backups - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure.
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 = ["Netbackup"]

[savedsearch://ESCU - Unusually Long Command Line - Rule]
type = detection
asset_type = 
confidence = medium
explanation = This search calculates the average and standard deviation for the length of the command lines on each of your endpoints and alerts when it detects a command line with a length over 10 times the standard deviation larger than the average command line.
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"], "mitre_attack": ["Execution"], "nist": ["PR.PT", "DE.CM"]}
known_false_positives = Some legitimate applications start with long command lines.
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Unusually Long Command Line - MLTK - Rule]
type = detection
asset_type = 
confidence = medium
explanation = This search leverages the Machine Learning Toolkit (MLTK) to identify outliers in the length of the command lines observed to be used by a specific user. The companion search, "Baseline of Command Line Length - MLTK," creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
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"], "mitre_attack": ["Execution"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Unusually Long Content-Type Length - Rule]
type = detection
asset_type = Web Server
confidence = high
explanation = This detection search uses HTTP traffic data captured with Splunk Stream.  The search is constructed to use "stream:http" sourcetype and counts of the number of times an HTTP request is received by a destination which the length of the Content-Type header value the client sends the server is greater than 100 characters long. We calculate this content_type_length field and output the results.
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"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "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 = ["Splunk Stream"]

[savedsearch://ESCU - WMI Permanent Event Subscription - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861.
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": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes.
providing_technologies = ["Microsoft Windows"]

[savedsearch://ESCU - WMI Permanent Event Subscription - Sysmon - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Sysmon event ID 21.
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": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes.
providing_technologies = ["Microsoft Windows"]

[savedsearch://ESCU - WMI Temporary Event Subscription - Rule]
type = detection
asset_type = Endpoint
confidence = medium
explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI temporary event subscription by watching for Windows event ID 5860.
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": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Web Fraud - Account Harvesting - Rule]
type = detection
asset_type = Account
confidence = medium
explanation = When a fraudster is setting the stage for a campaign, they will often create many user accounts on the website. This is a simple example of how to detect a many-account creation hosted on a Magento2 e-commerce platform, where the fraudster is using email addresses from a single email domain.
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": ["Persistence", "Create Account"], "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 = ["Splunk Stream"]

[savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule]
type = detection
asset_type = account
confidence = medium
explanation = It's suspicious when someone or something is moving throughout your website too quickly or with a perfect click cadence. Fortunately, it's easy to detect by calculating the time between clicks for each session and highlighting the anomalous behavior.
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": ["Initial Access", "Valid Accounts"], "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 = ["Splunk Stream"]

[savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule]
type = detection
asset_type = account
confidence = medium
explanation = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5
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 = ["Splunk Stream"]

[savedsearch://ESCU - Web Servers Executing Suspicious Processes - Rule]
type = detection
asset_type = Web Server
confidence = medium
explanation = This detection search uses the Enterprise Security Endpoint data model. The search uses tstats to search within an accelerated data model to find suspicious applications or processes such as whoami, ping, iptables, wget, service, or curl, running on hosts which are marked as web servers in the Assets and Identity Framework of ES.
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": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Discovery", "System Information Discovery"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Windows Event Log Cleared - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = This search looks at the Windows security and system event logs. EventCode 1002 in the security log indicates that the log has been cleared, EventCode 1000 in the security log indicates the event logging service has been shut down, and EventCode 104 in the system log indicates the application log has been cleared. If any of these events are found, a notable will be generated.
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": ["Defense Evasion", "Indicator Removal on Host"], "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 = ["Microsoft Windows"]

[savedsearch://ESCU - Windows hosts file modification - Rule]
type = detection
asset_type = Endpoint
confidence = high
explanation = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases.
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 products, 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"], "mitre_attack": ["Command and Control", "Exfiltration"], "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 = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"]

### END DETECTIONS ###

### INVESTIGATIONS ###

[savedsearch://ESCU - AWS Investigate User Activities By ARN]
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 = None at this time
earliest_time_offset = 72000
latest_time_offset = 36000

[savedsearch://ESCU - AWS Investigate User Activities By AccessKeyId]
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 = None at this time
earliest_time_offset = 0
latest_time_offset = 14400

[savedsearch://ESCU - AWS Investigate User Activities By Source User]
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 = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - AWS Network ACL Details from ID]
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 = None at this time
earliest_time_offset = 3600
latest_time_offset = 0

[savedsearch://ESCU - AWS Network Interface details via resourceId]
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 = None at this time
earliest_time_offset = 86400
latest_time_offset = 0

[savedsearch://ESCU - AWS S3 Bucket details via bucketName]
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 = None at this time
earliest_time_offset = 86400
latest_time_offset = 0

[savedsearch://ESCU - All backup logs for host]
type = investigation
explanation = none
how_to_implement = The successfully implement this search you must first send your backup logs to Splunk.
known_false_positives = None at this time
earliest_time_offset = 1209600
latest_time_offset = 0

[savedsearch://ESCU - Amazon EKS Kubernetes activity by src_ip]
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 = None at this time
earliest_time_offset = -70m@m
latest_time_offset = -10m@m

[savedsearch://ESCU - DNS Hijack Enrichment]
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 = None at this time
earliest_time_offset = 7200
latest_time_offset = 0

[savedsearch://ESCU - Domain Certificate Investigation]
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 = None at this time
earliest_time_offset = 0
latest_time_offset = 86400

[savedsearch://ESCU - Excessive Account Lockouts Enrichment And Response]
type = investigation
explanation = none
how_to_implement = Import playbook into phantom
known_false_positives = None at this time
earliest_time_offset = -4h@h
latest_time_offset = -5m@m

[savedsearch://ESCU - GCP Kubernetes activity by src_ip]
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 = None at this time
earliest_time_offset = -70m@m
latest_time_offset = -10m@m

[savedsearch://ESCU - Get All AWS Activity From City]
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 = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get All AWS Activity From Country]
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 = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get All AWS Activity From IP Address]
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 = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get All AWS Activity From Region]
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 = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Get Authentication Logs For Endpoint]
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 = None at this time
earliest_time_offset = 43200
latest_time_offset = 1

[savedsearch://ESCU - Get Backup Logs For Endpoint]
type = investigation
explanation = none
how_to_implement = You must be ingesting your backup logs.
known_false_positives = None at this time
earliest_time_offset = 604800
latest_time_offset = 0

[savedsearch://ESCU - Get Certificate logs for a domain]
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 = None at this time
earliest_time_offset = 36000
latest_time_offset = 0

[savedsearch://ESCU - Get DNS Server History for a host]
type = investigation
explanation = none
how_to_implement = To successfully implement this search, you must be ingesting your DNS traffic
known_false_positives = None at this time
earliest_time_offset = 0
latest_time_offset = 86400

[savedsearch://ESCU - Get DNS traffic ratio]
type = investigation
explanation = none
how_to_implement = You must be ingesting your network traffic
known_false_positives = None at this time
earliest_time_offset = 0
latest_time_offset = 86400

[savedsearch://ESCU - Get EC2 Instance Details by instanceId]
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 = None at this time
earliest_time_offset = 86400
latest_time_offset = 0

[savedsearch://ESCU - Get EC2 Launch Details]
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 = None at this time
earliest_time_offset = 7200
latest_time_offset = 0

[savedsearch://ESCU - Get Email Info]
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 = None at this time
earliest_time_offset = 0
latest_time_offset = 7200

[savedsearch://ESCU - Get Emails From Specific Sender]
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 = None at this time
earliest_time_offset = 86400
latest_time_offset = 86400

[savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address]
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 = None at this time
earliest_time_offset = 864000
latest_time_offset = 86400

[savedsearch://ESCU - Get History Of Email Sources]
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 = None at this time
earliest_time_offset = 172800
latest_time_offset = 86400

[savedsearch://ESCU - Get Logon Rights Modifications For Endpoint]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting your Windows event logs
known_false_positives = None at this time
earliest_time_offset = 86400
latest_time_offset = 86400

[savedsearch://ESCU - Get Logon Rights Modifications For User]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting your Windows event logs
known_false_positives = None at this time
earliest_time_offset = 86400
latest_time_offset = 86400

[savedsearch://ESCU - Get Notable History]
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 = None at this time
earliest_time_offset = 864000
latest_time_offset = 86400

[savedsearch://ESCU - Get Notable Info]
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 = None at this time
earliest_time_offset = 3600
latest_time_offset = 3600

[savedsearch://ESCU - Get Outbound Emails to Hidden Cobra Threat Actors]
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 = None at this time
earliest_time_offset = 86400
latest_time_offset = 0

[savedsearch://ESCU - Get Parent Process Info]
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 = None at this time
earliest_time_offset = 86400
latest_time_offset = 0

[savedsearch://ESCU - Get Process File Activity]
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 = None at this time
earliest_time_offset = 7200
latest_time_offset = 7200

[savedsearch://ESCU - Get Process Info]
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 = None at this time
earliest_time_offset = 7200
latest_time_offset = 7200

[savedsearch://ESCU - Get Process Information For Port Activity]
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 = None at this time
earliest_time_offset = 7200
latest_time_offset = 7200

[savedsearch://ESCU - Get Process Registry Activity]
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 = None at this time
earliest_time_offset = 7200
latest_time_offset = 7200

[savedsearch://ESCU - Get Process Responsible For The DNS Traffic]
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 = None at this time
earliest_time_offset = 3600
latest_time_offset = 86400

[savedsearch://ESCU - Get Registry Activities]
type = investigation
explanation = none
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.
known_false_positives = None at this time
earliest_time_offset = 0
latest_time_offset = 86400

[savedsearch://ESCU - Get Risk Modifiers For Endpoint]
type = investigation
explanation = none
how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework
known_false_positives = None at this time
earliest_time_offset = 604800
latest_time_offset = 0

[savedsearch://ESCU - Get Risk Modifiers For User]
type = investigation
explanation = none
how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework
known_false_positives = None at this time
earliest_time_offset = 604800
latest_time_offset = 0

[savedsearch://ESCU - Get Sysmon WMI Activity for Host]
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 = None at this time
earliest_time_offset = 7200
latest_time_offset = 7200

[savedsearch://ESCU - Get Update Logs For Endpoint]
type = investigation
explanation = none
how_to_implement = You need to be ingesting the update logs from your various systems.
known_false_positives = None at this time
earliest_time_offset = 604800
latest_time_offset = 0

[savedsearch://ESCU - Get User Information from Identity Table]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must have populated the identity table with information about your users.
known_false_positives = None at this time
earliest_time_offset = 864000
latest_time_offset = 86400

[savedsearch://ESCU - Get Vulnerability Logs For Endpoint]
type = investigation
explanation = none
how_to_implement = You need to be ingesting the logs from your vulnerability scanner.
known_false_positives = None at this time
earliest_time_offset = 604800
latest_time_offset = 0

[savedsearch://ESCU - Get Web Session Information via session_id]
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 = None at this time
earliest_time_offset = 3600
latest_time_offset = 3600

[savedsearch://ESCU - Investigate AWS ECR container listing activity]
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.You must also install Cloud Infrastructure Data Model.
known_false_positives = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate AWS User Activities by user field]
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 = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate AWS activities via region name]
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 = None at this time
earliest_time_offset = 14400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Cloud Compute Instance Activities]
type = investigation
explanation = none
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = None at this time
earliest_time_offset = 7200
latest_time_offset = 0

[savedsearch://ESCU - Investigate Failed Logins for Multiple Destinations]
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 = None at this time
earliest_time_offset = -7d
latest_time_offset = now

[savedsearch://ESCU - Investigate Network Traffic From src_ip]
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 = None at this time
earliest_time_offset = 3600
latest_time_offset = 3600

[savedsearch://ESCU - Investigate Okta Activity by IP Address]
type = investigation
explanation = none
how_to_implement = You must be ingesting Okta logs
known_false_positives = None at this time
earliest_time_offset = 86400
latest_time_offset = 14400

[savedsearch://ESCU - Investigate Okta Activity by app]
type = investigation
explanation = none
how_to_implement = You must be ingesting Okta logs
known_false_positives = None at this time
earliest_time_offset = 86400
latest_time_offset = 14400

[savedsearch://ESCU - Investigate Pass the Hash Attempts]
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 = None at this time
earliest_time_offset = -24h
latest_time_offset = now

[savedsearch://ESCU - Investigate Pass the Ticket Attempts]
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 = None at this time
earliest_time_offset = -24h
latest_time_offset = now

[savedsearch://ESCU - Investigate Previous Unseen User]
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 = None at this time
earliest_time_offset = -60d
latest_time_offset = now

[savedsearch://ESCU - Investigate Successful Remote Desktop Authentications]
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 = None at this time
earliest_time_offset = 86400
latest_time_offset = 0

[savedsearch://ESCU - Investigate Suspicious Strings in HTTP Header]
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 = None at this time
earliest_time_offset = 3600
latest_time_offset = 3600

[savedsearch://ESCU - Investigate User Activities In All Cloud Regions]
type = investigation
explanation = none
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = None at this time
earliest_time_offset = 86400
latest_time_offset = 14400

[savedsearch://ESCU - Investigate User Activities In Okta]
type = investigation
explanation = none
how_to_implement = You must be ingesting Okta logs
known_false_positives = None at this time
earliest_time_offset = 86400
latest_time_offset = 14400

[savedsearch://ESCU - Investigate User Activities In Single Cloud Region]
type = investigation
explanation = none
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = None at this time
earliest_time_offset = 86400
latest_time_offset = 14400

[savedsearch://ESCU - Investigate Web Activity From Host]
type = investigation
explanation = none
how_to_implement = To successfully implement this search you must be ingesting your web traffic and populating the Web data model.
known_false_positives = None at this time
earliest_time_offset = 3600
latest_time_offset = 3600

[savedsearch://ESCU - Investigate Web Activity From src_ip]
type = investigation
explanation = none
how_to_implement = To successfully implement this search, you must be ingesting your web traffic and populating the web data model.
known_false_positives = None at this time
earliest_time_offset = 3600
latest_time_offset = 3600

[savedsearch://ESCU - Investigate Web POSTs From src]
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 = None at this time
earliest_time_offset = 3600
latest_time_offset = 3600

[savedsearch://ESCU - Suspicious Email Attachment Investigate and Delete]
type = investigation
explanation = none
how_to_implement = Synchronize the community playbook repository in Phantom, then open the playbook and follow the deployment notes to configure it for your environment.
known_false_positives = None at this time
earliest_time_offset = 0
latest_time_offset = 86400

### END INVESTIGATIONS ###

### BASELINES ###
[savedsearch://ESCU - Add Prohibited Processes to Enterprise Security]
type = support
explanation = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries.
how_to_implement = This search should be run on each new install of ESCU.
known_false_positives = 
providing_technologies = ["Splunk Enterprise Security"]

[savedsearch://ESCU - Baseline of API Calls per User ARN]
type = support
explanation = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis.  It also includes the number of data points each ARN had. This table is then stored in 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.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Baseline of Command Line Length - MLTK]
type = support
explanation = Create a machine-learning (ML) model to characterize the length of the command lines used in your environment. This can help you identify unusually long ones that may indicate that attackers are executing commands on yout systems.
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 = 
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Baseline of DNS Query Length - MLTK]
type = support
explanation = Create a machine-learning (ML) model to characterize the length of DNS requests seen in your environment to help identify unusually long ones that may be indicative of attacker infrastrucutre or the use of DNS as a command-and-control channel 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, 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 = 
providing_technologies = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK]
type = support
explanation = Create a machine-learning (ML) model to establish a baseline for how many RunInstances users do in the environment. This can help you identify excessive numbers of RunInstances which may warrant further investigation to determine if there is misuse or abuse.
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 = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK]
type = support
explanation = Create a machine-learning (ML) model to establish a baseline for how many TerminateInstances users do in the environment. This can help you identify excessive numbers of TerminateInstances which may warrant further investigation to determine if there is misuse or abuse.
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 = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Baseline of Network ACL Activity by ARN]
type = support
explanation = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in 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. To add or remove API event names for network ACLs, edit the macro `network_acl_events`.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Baseline of S3 Bucket deletion activity by ARN]
type = support
explanation = Use this search to create a baseline for API calls related to deleting an S3 bucket, grouped by the users who initiated this activity. It returns all logged API calls for S3 bucket-deletion activity and then pulls out the ARN that initiated each call. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in 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.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Baseline of SMB Traffic - MLTK]
type = support
explanation = Create a machine-learning (ML) model to characterize the number of SMB connections observed in your environment. This may help identify spikes in SMB traffic that may be indicative of attackers scanning or attempting to propagate to other systems in your environment. By default, this model is built over 30 days of data and profiles the number of SMB connections in your environment by the hour of day/day of week that the connections occur.
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 = 
providing_technologies = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Baseline of Security Group Activity by ARN]
type = support
explanation = Use this search to create a baseline for API calls related to security groups by the users who initiated this activity. It returns all logged API calls for all security-group-related activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in 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. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Baseline of blocked outbound traffic from AWS]
type = support
explanation = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis.  It also includes the number of data points each source IP had. This table is then stored in 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 `VPC flow logs.`.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Count of Unique IPs Connecting to Ports]
type = support
explanation = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections.
how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model.
known_false_positives = 
providing_technologies = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Count of assets by category]
type = support
explanation = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count.
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 = 
providing_technologies = ["Splunk Enterprise Security"]

[savedsearch://ESCU - Create a list of approved AWS service accounts]
type = support
explanation = We first look for all successful CloudTrail API activity caused by types of user accounts and then remove all the events caused by users in the Identity table. This generates a list of accounts--typically service accounts--configured in your AWS environment. We output this list of service accounts to `aws_service_accounts.csv`.
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 = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - DNSTwist Domain Names]
type = support
explanation = This search starts with the dnstwist command consuming domains from a file called domains.csv in the DA-ESS-SOC/lookups directory. This search then adds a domain\_abuse=true term to each permutation, removes all the valid domain names and stores all that information into a lookup file that is used in the associated detection search. Alternatively domain dnstwist permutations can be calculated from domains in the `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` lookups located in **Splunk\_SA\_CIM** using argument `populate_from_cim=true`. Also an individual domain can be passed using argument `domain=<domain>`
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 = 
providing_technologies = ["Splunk Enterprise"]

[savedsearch://ESCU - Discover DNS records]
type = support
explanation = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv`
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 = Please vet the lookup created by this baseline search 
providing_technologies = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Identify Systems Creating Remote Desktop Traffic]
type = support
explanation = This search counts the numbers of times the system has tried to connect to another system on TCP/3389, the default port used for RDP traffic.
how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model.
known_false_positives = 
providing_technologies = ["Splunk Stream", "Bro"]

[savedsearch://ESCU - Identify Systems Receiving Remote Desktop Traffic]
type = support
explanation = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP 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 = 
providing_technologies = ["Splunk Stream", "Bro"]

[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. It does this by looking for the process name in the Endpoint data model.
how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity.
known_false_positives = 
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Monitor Successful Backups]
type = support
explanation = This search gives you the count and the hostname of all the systems that had a successful backup each day.
how_to_implement = To successfully implement this search you must be ingesting your backup logs.
known_false_positives = 
providing_technologies = ["Netbackup"]

[savedsearch://ESCU - Monitor Unsuccessful Backups]
type = support
explanation = This search gives you the count and hostname of all the systems that had a backup failure each day
how_to_implement = To successfully implement this search you must be ingesting your backup logs.
known_false_positives = 
providing_technologies = ["Netbackup"]

[savedsearch://ESCU - Previously Seen AWS Cross Account Activity]
type = support
explanation = In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written 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 = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously Seen AWS Provisioning Activity Sources]
type = support
explanation = This search includes any event name that begins with "run" or "create," and then determines the first and last time these events were seen for each IP address that initiated the action. The search then consults a **GeoIP** database to determine the physical location of this IP address. This table outputs to a 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.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously Seen AWS Regions]
type = support
explanation = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the 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.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously Seen Cloud Compute Creations By User]
type = support
explanation = In this support search, we create a table of the earliest and latest time for each user that has created a cloud compute instance.
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = 
providing_technologies = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Previously Seen Cloud Compute Images]
type = support
explanation = In this support search, we create a table of the earliest and latest time for each image id that has been seen. This table is then outputted to a csv file.
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = 
providing_technologies = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Previously Seen Cloud Compute Instance Types]
type = support
explanation = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that the compute type has been seen in our dataset. We only look for those events where an instance has been created. All of these entries will be added to the `previously_seen_cloud_compute_instance_types` lookup file, which will act as a baseline for detections.
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = 
providing_technologies = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Previously Seen Cloud Regions]
type = support
explanation = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that this region has been seen in our dataset, grouped by the region. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_cloud_regions` lookup file, which will act like a baseline for detections.
how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.
known_false_positives = 
providing_technologies = ["AWS", "Azure", "GCP"]

[savedsearch://ESCU - Previously Seen EC2 AMIs]
type = support
explanation = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv 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.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously Seen EC2 Instance Types]
type = support
explanation = In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. This table is then outputted to a csv 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.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously Seen EC2 Launches By User]
type = support
explanation = In this support search, we create a table of the earliest and latest times that an ARN has launched a EC2 instance. This table is then outputted to a csv 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.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously Seen EC2 Modifications By User]
type = support
explanation = In this support search, we create a table of the earliest and latest times that an ARN has modified a EC2 instance. The list of APIs that modify an EC2 are defined in the `ec2_modification_api_calls` macro for ease of use. This table is then outputted to a 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. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
known_false_positives = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously Seen Running Windows Services]
type = support
explanation = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. In this specific log event, the `param1` field represents the "service_name" and the `param2` represents the action/status of the service. This search will create a table of the first and last time as particular Windows service was seen to be in the `running` status.
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 5.0.0 or above.
known_false_positives = 
providing_technologies = ["Microsoft Windows"]

[savedsearch://ESCU - Previously seen API call per user roles in CloudTrail]
type = support
explanation = In this support search, we are looking for successful API calls made by user roles within your AWS infrastructure. The intent is to create an initial baseline cache of names of the API calls per security role for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of user role and the name of the API call. It is also worth noting that the role of a particular user is parsed as "userName" in the CloudTrail logs.
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 = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously seen S3 bucket access by remote IP]
type = support
explanation = In this support search, we are looking for successful S3 bucket-access attempts made from remote IPs. The intent is to create an initial baseline cache of remote IP addresses per bucket name for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of remote IP and the name of the 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. 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 = 
providing_technologies = ["AWS"]

[savedsearch://ESCU - Previously seen command line arguments]
type = support
explanation = In this support search, we look for command-line arguments using the parameter `/c` to execute processes and create an initial baseline cache for the previous 30 days. This will include the earliest and latest times a particular command-line argument is seen in our dataset, grouped by the command-line value.
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 = 
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Previously seen users in CloudTrail]
type = support
explanation = In this support search, we look for console login events by a particular user and create an initial baseline cache for the previous 30 days, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values.
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.csv`, which is a lookup file created as a result of running this support search.
known_false_positives = n/a
providing_technologies = ["AWS"]

[savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch]
type = support
explanation = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it.
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 = 
providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"]

[savedsearch://ESCU - Update previously seen users in CloudTrail]
type = support
explanation = In this support search, we look for console login events by a particular user to update the baseline cache of users/arns making the accesses, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values.
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.csv`, which is a lookup file created as a result of running this support search.
known_false_positives = n/a
providing_technologies = ["AWS"]

[savedsearch://ESCU - Windows Updates Install Failures]
type = support
explanation = This search gives you the count of the number of systems that attempted and failed to install a Windows update each day.
how_to_implement = You must be ingesting your Windows Update Logs
known_false_positives = 
providing_technologies = ["Microsoft Windows"]

[savedsearch://ESCU - Windows Updates Install Successes]
type = support
explanation = This search gives you the count and name of all the systems that had a successful update applied each day
how_to_implement = You must be ingesting your Windows Update Logs
known_false_positives = 
providing_technologies = ["Microsoft Windows"]

### END ESCU BASELINES ###