Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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KQL Query from file: Threat Intelligence Data from Sentinel UEBA
KQL Query from file: Critical Monitor for Critical Assets
'This detection identifies high-severity alerts across various Microsoft security products, including Microsoft Defender XDR and Microsoft Entra ID, and correlates them with instances of Google Cloud VM creation. It focuses on instances where VMs were created within a short timeframe of high-severity alerts, potentially indicating suspicious activity.'
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
Microsoft has observed attackers exploiting vulnerabilities associated with Log4J.
The query for malicious file creations via TOCTOU Vulnerability in Leading Endpoint Detection and Response (EDR) and Antivirus (AV) Solutions.
- Microsoft Defender (CVE-2022-37971)
- Defender for Endpoint (CVE-2022-37971)
- SentinelOne EDR
- TrendMicro Apex One (CVE-2022-45797)
- Avast Antivirus (CVE-2022-4173)
- AVG Antivirus (CVE-2022-4173)
- Microsoft Defender (CVE-2022-37971)
- Defender for Endpoint (CVE-2022-37971)
- SentinelOne EDR
- TrendMicro Apex One (CVE-2022-45797)
- Avast Antivirus (CVE-2022-4173)
- AVG Antivirus (CVE-2022-4173)
This query will provide a report of many of the best practice configurations for Defender ATP deployment. Special Thanks to Gilad Mittelman for the initial inspiration and concept.
Any tests which are reporting "BAD" as a result imply that the associated capability is not configured per best practice recommendation.
Any tests which are reporting "BAD" as a result imply that the associated capability is not configured per best practice recommendation.
'Extracts MDATP Alert for a web shell being placed on the server and then enriches this event with information from W3CIISLog to idnetigy the Attacker that placed the shell'
In this query, we are looking for Url clicks on emails which are generated the alert-A potentially malicious URL click was detected
This query offers daily categorization of ASR rules, helping SOC analysts monitor specific categories like office-related activities or WMI among the 16 rules.
It aids in tracking detection rates and organizational trends.
It aids in tracking detection rates and organizational trends.
'Any Alerts that fired related to a given File during the range of +6h and -3d'
This query was originally published in the threat analytics report, Ransomware continues to hit healthcare, critical services.
It finds all user accounts that have logged on to an endpoint affected by Cobalt Strike, a penetration tool.
Assume that all credentials on endpoints affected by Cobalt Strike were available to attackers and that all associated accounts are compromised. Note that attackers will not only dump credentials for accounts that have logged on to interactive or RDP sessions, but will also dump cached credentials and passwords for service accounts and scheduled tasks that are stored in the LSA Secrets section of the registry.
Reference - https://attack.mitre.org/software/S0154/
It finds all user accounts that have logged on to an endpoint affected by Cobalt Strike, a penetration tool.
Assume that all credentials on endpoints affected by Cobalt Strike were available to attackers and that all associated accounts are compromised. Note that attackers will not only dump credentials for accounts that have logged on to interactive or RDP sessions, but will also dump cached credentials and passwords for service accounts and scheduled tasks that are stored in the LSA Secrets section of the registry.
Reference - https://attack.mitre.org/software/S0154/
'This query will dynamically identify Exchange servers using common web paths used by the application in the csUriStem. The query
will then collect MDE alerts from the SecurityAlert table using the identified Exchange Server hostnames.'
will then collect MDE alerts from the SecurityAlert table using the identified Exchange Server hostnames.'
'This query looks for Microsoft Defender AV detections related to Europium actor.
In Microsoft Sentinel the SecurityAlerts table includes only the Device Name of the affected device, this query joins the DeviceInfo table to clearly connect other information such as Device group, ip, etc. This would allow the Microsoft Sentinel analyst to have more context related to the alert, if available.
Reference: https://www.microsoft.com/security/blog/2022/09/08/microsoft-investigates-iranian-attacks-against-the-albanian-government '
In Microsoft Sentinel the SecurityAlerts table includes only the Device Name of the affected device, this query joins the DeviceInfo table to clearly connect other information such as Device group, ip, etc. This would allow the Microsoft Sentinel analyst to have more context related to the alert, if available.
Reference: https://www.microsoft.com/security/blog/2022/09/08/microsoft-investigates-iranian-attacks-against-the-albanian-government '
'This query looks for Microsoft Defender AV detections related to Hive Ransomware.
In Microsoft Sentinel the SecurityAlerts table includes only the Device Name of the affected device, this query joins the DeviceInfo table to clearly connect other information such as Device group, ip, logged on users etc. This would allow the Microsoft Sentinel analyst to have more context related to the alert, if available.'
In Microsoft Sentinel the SecurityAlerts table includes only the Device Name of the affected device, this query joins the DeviceInfo table to clearly connect other information such as Device group, ip, logged on users etc. This would allow the Microsoft Sentinel analyst to have more context related to the alert, if available.'
Identify accounts that have logged on to affected endpoints.
Check for specific alerts.
Check for specific alerts.
This rule detects when the ASR rule AsrRansomwareBlocked or AsrRansomwareAudited is triggered.
No alert is generated by default. This could be the start of a ransomware attack.
Additional information available by Microsoft:
https://docs.microsoft.com/microsoft-365/security/defender-endpoint/attack-surface-reduction-rules-reference?view=o365-worldwide#use-advanced-protection-against-ransomware
No alert is generated by default. This could be the start of a ransomware attack.
Additional information available by Microsoft:
https://docs.microsoft.com/microsoft-365/security/defender-endpoint/attack-surface-reduction-rules-reference?view=o365-worldwide#use-advanced-protection-against-ransomware
'This query looks for Microsoft Defender for Endpoint detections related to the remote command execution attempts on Azure IR with Managed VNet or SHIR.
In Microsoft Sentinel, the SecurityAlerts table includes the name of the impacted device. Additionally, this query joins the DeviceInfo table to connect other information such as device group, IP address, signed in users, and others allowing analysts using Microsoft Sentinel to have more context related to the alert.
Reference: https://msrc.microsoft.com/update-guide/vulnerability/CVE-2022-29972 ,
https://msrc-blog.microsoft.com/2022/05/09/vulnerability-mitigated-in-the-third-party-data-connector-used-in-azure-synapse-pipelines-and-azure-data-factory-cve-2022-29972'
In Microsoft Sentinel, the SecurityAlerts table includes the name of the impacted device. Additionally, this query joins the DeviceInfo table to connect other information such as device group, IP address, signed in users, and others allowing analysts using Microsoft Sentinel to have more context related to the alert.
Reference: https://msrc.microsoft.com/update-guide/vulnerability/CVE-2022-29972 ,
https://msrc-blog.microsoft.com/2022/05/09/vulnerability-mitigated-in-the-third-party-data-connector-used-in-azure-synapse-pipelines-and-azure-data-factory-cve-2022-29972'
Sticky Keys
KQL
A technique used in numerous ransomware attacks is a Sticky Keys hijack for privilege escalation/persistence. Surface realted alerts with this query.
This query was originally published in the threat analytics report, Ransomware continues to hit healthcare, critical services. There is also a related blog.
In April of 2020, security researchers observed multiple ransomware campaigns using the same set of techniques. The attackers would compromise a web-facing endpoint and employ tools such as Cobalt Strike to steal users' credentials.
Cobalt Strike is commercial software used to conduct simulated threat campaigns against a target; however, malicious actors also use Cobalt Strike in real attacks. The software has a large range of capabilities, including credential theft.
The following query identifies accounts that have logged on to compromised endpoints and have potentially had their credentials stolen.
References:
https://www.microsoft.com/security/blog/2020/04/28/ransomware-groups-continue-to-target-healthcare-critical-services-heres-how-to-reduce-risk/
https://www.cobaltstrike.com/
https://attack.mitre.org/software/S0154/
In April of 2020, security researchers observed multiple ransomware campaigns using the same set of techniques. The attackers would compromise a web-facing endpoint and employ tools such as Cobalt Strike to steal users' credentials.
Cobalt Strike is commercial software used to conduct simulated threat campaigns against a target; however, malicious actors also use Cobalt Strike in real attacks. The software has a large range of capabilities, including credential theft.
The following query identifies accounts that have logged on to compromised endpoints and have potentially had their credentials stolen.
References:
https://www.microsoft.com/security/blog/2020/04/28/ransomware-groups-continue-to-target-healthcare-critical-services-heres-how-to-reduce-risk/
https://www.cobaltstrike.com/
https://attack.mitre.org/software/S0154/
Query for links opened from mail apps - if a detection occurred right afterwards.
As there are many links opened from mails, to have a successful hunt we should have some filter or join with some other signal,.
Such as suspicious processes, network connections, etc.
Therefore, in this example, we query for alerts that might be related to links sent via email.
This could be indicative of a phishing or spear-phishing attacks.
Tags: #EmailLink, #Phishing, #GetNearbyAlerts.
Explaining the underlying data:.
This query uses the BrowserLaunchedToOpenUrl event, that includes clicks on http:// or https:// links (clicks outside of browsers), or on .lnk files.
For this event, RemoteUrl contains the opened URL.
As there are many links opened from mails, to have a successful hunt we should have some filter or join with some other signal,.
Such as suspicious processes, network connections, etc.
Therefore, in this example, we query for alerts that might be related to links sent via email.
This could be indicative of a phishing or spear-phishing attacks.
Tags: #EmailLink, #Phishing, #GetNearbyAlerts.
Explaining the underlying data:.
This query uses the BrowserLaunchedToOpenUrl event, that includes clicks on http:// or https:// links (clicks outside of browsers), or on .lnk files.
For this event, RemoteUrl contains the opened URL.
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