Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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
'Shows the rarest processes seen running for the first time. (Performs best over longer time ranges - eg 3+ days rather than 24 hours!)
These new processes could be benign new programs installed on hosts;
However, especially in normally stable environments, these new processes could provide an indication of an unauthorized/malicious binary that has been installed and run.
Reviewing the wider context of the logon sessions in which these binaries ran can provide a good starting point for identifying possible attacks.'
These new processes could be benign new programs installed on hosts;
However, especially in normally stable environments, these new processes could provide an indication of an unauthorized/malicious binary that has been installed and run.
Reviewing the wider context of the logon sessions in which these binaries ran can provide a good starting point for identifying possible attacks.'
This query identifies files that are copied to a device over SMB, then executed within a
specified threshold. Default is 5 seconds, but is configurable by tweaking the value for
ToleranceInSeconds.
specified threshold. Default is 5 seconds, but is configurable by tweaking the value for
ToleranceInSeconds.
Backdoor processes associated with OceanLotus Mac malware backdoor dropper.
References:.
Https://blog.trendmicro.com/trendlabs-security-intelligence/new-macos-backdoor-linked-to-oceanlotus-found/.
OS Platforms: Macintosh.
References:.
Https://blog.trendmicro.com/trendlabs-security-intelligence/new-macos-backdoor-linked-to-oceanlotus-found/.
OS Platforms: Macintosh.
Query 1 shows you any network communication happened from endpoints to a specific Remote IP or Remote URL.
Ensure to update RemoteIP and RemoteURL variable.
For questions @MiladMSFT on Twitter or milad.aslaner@microsoft.com by email.
Ensure to update RemoteIP and RemoteURL variable.
For questions @MiladMSFT on Twitter or milad.aslaner@microsoft.com by email.
Snip3 is a family of related remote access trojans. Although the malware in this family contain numerous small variations, they all exhibit similar behaviors and techniques.
The following query looks for potentially hollowed processes that may be used to facilitate command-and-control or exfiltration by Snip3 malware. This technique has been used in recent cases to exfiltrate data, including credentials.
The query may return additional malware or campaigns not necessarily associated with Snip3. However, Microsoft recommends triaging all non-benign results as potential malware.
The following query looks for potentially hollowed processes that may be used to facilitate command-and-control or exfiltration by Snip3 malware. This technique has been used in recent cases to exfiltrate data, including credentials.
The query may return additional malware or campaigns not necessarily associated with Snip3. However, Microsoft recommends triaging all non-benign results as potential malware.
These queries check telemetry from the Exploit Guard rule: Rule: Block Office applications from creating child processes.
(Rule ID d4f940ab-401b-4efc-aadc-ad5f3c50688a).
Read more about it here: https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/attack-surface-reduction-exploit-guard.
Oftentimes organizations enable this rule in audit mode and check the results before setting block mode.
You can use query #2 to measure the rule impact on your network in audit mode before turning it to block mode.
Query #1 is used after setting it to block mode - to analyze the block stats.
Tags: #ASR.
Query #1: block stats.
(Rule ID d4f940ab-401b-4efc-aadc-ad5f3c50688a).
Read more about it here: https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/attack-surface-reduction-exploit-guard.
Oftentimes organizations enable this rule in audit mode and check the results before setting block mode.
You can use query #2 to measure the rule impact on your network in audit mode before turning it to block mode.
Query #1 is used after setting it to block mode - to analyze the block stats.
Tags: #ASR.
Query #1: block stats.
These queries check telemetry from the Exploit Guard rule: Rule: Block Office applications from creating child processes.
(Rule ID d4f940ab-401b-4efc-aadc-ad5f3c50688a).
Read more about it here: https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/attack-surface-reduction-exploit-guard.
Oftentimes organizations enable this rule in audit mode and check the results before setting block mode.
You can use query #2 to measure the rule impact on your network in audit mode before turning it to block mode.
Query #1 is used after setting it to block mode - to analyze the block stats.
Tags: #ASR.
Query #1: block stats.
(Rule ID d4f940ab-401b-4efc-aadc-ad5f3c50688a).
Read more about it here: https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/attack-surface-reduction-exploit-guard.
Oftentimes organizations enable this rule in audit mode and check the results before setting block mode.
You can use query #2 to measure the rule impact on your network in audit mode before turning it to block mode.
Query #1 is used after setting it to block mode - to analyze the block stats.
Tags: #ASR.
Query #1: block stats.
This is a completely stupid and pointless query that makes Vogon poetry out
of a random FolderPath from the table you pass it. You can change
DeviceProcessEvents for any table as long as it has a column named DeviceName
and a column called FolderPath. Feel free to check in more verses :)
Don't know what Vogon poetry is? You have a research assignment: http://tinyurl.com/y8ueqchl
of a random FolderPath from the table you pass it. You can change
DeviceProcessEvents for any table as long as it has a column named DeviceName
and a column called FolderPath. Feel free to check in more verses :)
Don't know what Vogon poetry is? You have a research assignment: http://tinyurl.com/y8ueqchl
This query checks for attempts to stop at least 10 separate processes using the net stop command. Run query
Dopplepaymer In-Memory Malware Implant.
This query identifies processes with command line launch strings.
Which match the pattern used in Dopplepaymer ransomware attacks.
This query identifies processes with command line launch strings.
Which match the pattern used in Dopplepaymer ransomware attacks.
Original Sigma Rule: https://github.com/Neo23x0/sigma/blob/master/rules/apt/apt_unidentified_nov_18.yml.
Questions via Twitter: @janvonkirchheim.
Questions via Twitter: @janvonkirchheim.