Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects suspicious registry modifications for IFEO entries which can be used by attackers for persistence.
This is based off of testing with Atomic Red Team tests - https://www.atomicredteam.io/atomic-red-team/atomics/T1546.012
This is based off of testing with Atomic Red Team tests - https://www.atomicredteam.io/atomic-red-team/atomics/T1546.012
Baseline Comparison.
Author: miflower.
The purpose of this query is to perform a comparison between "known good" machines and suspected bad machines.
The original concept for this query was born due to reapplying the same 'whitelist' filters over and over.
It brings deltas between a baseline and another machine quickly to the analyst's view.
This query supports multiple suspected bad machines and multiple "known good" machines.
It also supports providing a timeframe for how far back in time to build a baseline as well as how far back in time to evaluate the suspected bad machines.
Each of the links provided by DeviceId/DeviceName will go to the most recent entry for whatever entity is listed.
Average results for the pre-defined settings below with a single good host and a single bad host on a 'huge' tenant (300k+ machines):.
Compute Time: ~10-20 seconds.
Result Set Size: ~500 rows.
The workflow is as follows:.
1. Establish Variables that are editable on a per-query basis.
2. Define functions for reuse.
3. Calculate DeviceIds for all machines in scope.
4. Derive deltas using the aforementioned functions.
5. Union together all results into a single view.
The following datasets are returned:.
1. Alerts on the suspected bad machines (ignores known good machines, because...they're alerts, additional data has the triggered file).
2. Connected Networks (from DeviceNetworkInfo table, additional data has full Connected Network details).
3. File Creations (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
4. Image Loads (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
5. Logon (derived from DeviceLogonEvents for the unique users logged on, additional data has logon types).
6. Network communication (grouped by 2nd level-domain, ie 'microsoft.com' in 'www.microsoft.com' and 'web.microsoft.com', additional data has the full list of URLs).
7. Process creation (additional da
Author: miflower.
The purpose of this query is to perform a comparison between "known good" machines and suspected bad machines.
The original concept for this query was born due to reapplying the same 'whitelist' filters over and over.
It brings deltas between a baseline and another machine quickly to the analyst's view.
This query supports multiple suspected bad machines and multiple "known good" machines.
It also supports providing a timeframe for how far back in time to build a baseline as well as how far back in time to evaluate the suspected bad machines.
Each of the links provided by DeviceId/DeviceName will go to the most recent entry for whatever entity is listed.
Average results for the pre-defined settings below with a single good host and a single bad host on a 'huge' tenant (300k+ machines):.
Compute Time: ~10-20 seconds.
Result Set Size: ~500 rows.
The workflow is as follows:.
1. Establish Variables that are editable on a per-query basis.
2. Define functions for reuse.
3. Calculate DeviceIds for all machines in scope.
4. Derive deltas using the aforementioned functions.
5. Union together all results into a single view.
The following datasets are returned:.
1. Alerts on the suspected bad machines (ignores known good machines, because...they're alerts, additional data has the triggered file).
2. Connected Networks (from DeviceNetworkInfo table, additional data has full Connected Network details).
3. File Creations (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
4. Image Loads (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
5. Logon (derived from DeviceLogonEvents for the unique users logged on, additional data has logon types).
6. Network communication (grouped by 2nd level-domain, ie 'microsoft.com' in 'www.microsoft.com' and 'web.microsoft.com', additional data has the full list of URLs).
7. Process creation (additional da
This query is looking for regsvr32.exe or rundll32.exe loading DLL images with other extensions than .dll.
Joins the data to public network events.
References:
https://threathunt.blog/running-live-malware-for-threat-hunting-purposes/
Joins the data to public network events.
References:
https://threathunt.blog/running-live-malware-for-threat-hunting-purposes/
This query will hunt for files matching the current abuse.ch recent threat feed based on Sha256. Currently the query is set up to analyze the last day worth of events, but this is configurable using the MaxAge variable.
Query #1 - Find the machines on which this file was seen.
TODO - set file hash to be a SHA1 hash of your choice...
TODO - set file hash to be a SHA1 hash of your choice...
This query was originally published in the threat analytics report, RDP ransomware persists as Wadhrama.
The ransomware known as Wadhrama has been used in human-operated attacks that follow a particular pattern. The attackers often use Remote Desktop Protocol (RDP) to gain initial access to a device or network, exfiltrate credentials, and maintain persistance.
The following query checks for possible Wadhrama-related activity, by detecting the technique these attackers have used in the past to dump credentials.
Other techniques used by the group associated with Wadhrama are listed under See also.
Reference - https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?Name=Ransom:Win32/Wadhrama
The ransomware known as Wadhrama has been used in human-operated attacks that follow a particular pattern. The attackers often use Remote Desktop Protocol (RDP) to gain initial access to a device or network, exfiltrate credentials, and maintain persistance.
The following query checks for possible Wadhrama-related activity, by detecting the technique these attackers have used in the past to dump credentials.
Other techniques used by the group associated with Wadhrama are listed under See also.
Reference - https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?Name=Ransom:Win32/Wadhrama
This query was originally published in a threat analytics report about the group known to other security researchers as APT32 or OceanLotus
This tracked activity group uses a wide array of malicious documents to conduct attacks. Some of their favorite techniques include sideloading dynamic link libraries, and disguising payloads as image files. The group has weaponized files with exploits for the following vulnerabilities:
1. CVE-2017-11882 - Software update
2. CVE-2017-0199 - Software update
The following query detects known malicious files associated with the group's campaigns.
See Detect malicious network activity associated with group known as "OceanLotus" for another query related to this group's activity.
This tracked activity group uses a wide array of malicious documents to conduct attacks. Some of their favorite techniques include sideloading dynamic link libraries, and disguising payloads as image files. The group has weaponized files with exploits for the following vulnerabilities:
1. CVE-2017-11882 - Software update
2. CVE-2017-0199 - Software update
The following query detects known malicious files associated with the group's campaigns.
See Detect malicious network activity associated with group known as "OceanLotus" for another query related to this group's activity.
This rule detects when a Google Cloud Platform (GCP) Compute Engine image's access policy is modified to include 'allAuthenticatedUsers'. This action effectively makes the image publicly accessible to any authenticated Google user, which could lead to unauthorized access or data exfiltration if sensitive data is present in the image.
Detects cmstp loading "dll" or "ocx" files from suspicious locations, which is a known technique for defense evasion and proxy execution. Adversaries can abuse CMSTP.exe, a legitimate Microsoft binary, to load and execute malicious DLLs or OCX files from non-standard directories, potentially bypassing application whitelisting and other security controls.
Detects processes loading modules related to the PCRE.NET package, specifically looking for a unique temporary path often associated with its deployment. This could indicate the execution of code leveraging this package, potentially for malicious purposes like regular expression-based obfuscation or payload delivery.