Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network connections from the Equation Editor process "eqnedt32.exe".
Detects Dllhost.EXE initiating a network connection to a non-local IP address.
Aside from Microsoft own IP range that needs to be excluded. Network communication from Dllhost will depend entirely on the hosted DLL.
An initial baseline is recommended before deployment.
Aside from Microsoft own IP range that needs to be excluded. Network communication from Dllhost will depend entirely on the hosted DLL.
An initial baseline is recommended before deployment.
Detects svchost hosting RDP termsvcs communicating with the loopback address and on TCP port 3389
Detects incoming connections to AnyDesk. This could indicate a potential remote attacker trying to connect to a listening instance of AnyDesk and use it as potential command and control channel.
Detects the execution of rundll32 that leads to an external network connection.
The malware Pikabot has been seen to use this technique to initiate C2-communication through hard-coded Windows binaries.
The malware Pikabot has been seen to use this technique to initiate C2-communication through hard-coded Windows binaries.
Adversaries can migrate cobalt strike/metasploit/C2 beacons on compromised systems to legitimate werfault.exe process to avoid detection.
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
'Looks for file uploads actions to Azure File and Blob Storage from known VPS provider network ranges.
This is not an exhaustive list of VPS provider ranges but covers some of the most prevalent providers observed.'
This is not an exhaustive list of VPS provider ranges but covers some of the most prevalent providers observed.'
Detects programs that connect to uncommon destination ports
Detects programs that connect to known malware callback ports based on statistical analysis from two different sandbox system databases
Microsoft has observed attackers exploiting vulnerabilities associated with Log4J.
Detects a network connection that is initiated by the "notepad.exe" process.
This might be a sign of process injection from a beacon process or something similar.
Notepad rarely initiates a network communication except when printing documents for example.
This might be a sign of process injection from a beacon process or something similar.
Notepad rarely initiates a network communication except when printing documents for example.
Detects programs that connect to known malware callback ports based on statistical analysis from two different sandbox system databases
Detects a network connection initiated by "wordpad.exe" over uncommon destination ports.
This might indicate potential process injection activity from a beacon or similar mechanisms.
This might indicate potential process injection activity from a beacon or similar mechanisms.
Microsoft has observed Bazacall using Cobalt Strike in order to move laterally to other machines on the network.
'This query identifies Microsoft-signed Binaries and Scripts that are not system initiated. This technique is commonly used in phishing attacks'
Detects uncommon outbound network activity via Kerberos default port indicating possible lateral movement or first stage PrivEsc via delegation.
'Query identifies beaconing patterns from Wire Data logs. Uses KQL functions to calculate time delta and find beaconing percentage. Results of beaconing to untrusted public networks can be investigated.'
Detects an initiated network connection over uncommon ports from "dfsvc.exe". A utility used to handled ClickOnce applications.
Detects a network connection initiated by the "hh.exe" process to HTTP destination ports, which could indicate the execution/download of remotely hosted .chm files.
'Identifies a match across various data feeds for hashes and IP IOC related to Europium
Reference: https://www.microsoft.com/security/blog/2022/09/08/microsoft-investigates-iranian-attacks-against-the-albanian-government'
Reference: https://www.microsoft.com/security/blog/2022/09/08/microsoft-investigates-iranian-attacks-against-the-albanian-government'
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