Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a network connection initiated by a binary to "api.mega.co.nz".
Attackers were seen abusing file sharing websites similar to "mega.nz" in order to upload/download additional payloads.
Attackers were seen abusing file sharing websites similar to "mega.nz" in order to upload/download additional payloads.
'Indicates when an anomalous number of resources are created in Azure via AzureActivity log. Resource creation could indicate malicious or spurious use of your Azure Resource allocation.'
RClone Execution
Sigma
Detects execution of RClone utility for exfiltration as used by various ransomwares strains like REvil, Conti, FiveHands, etc
Detects DNS queries for "anonfiles.com", which is an anonymous file upload platform often used for malicious purposes
This query helps hunting for BEC - File sharing tactics - Dropbox
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
The following analytic detects Google Drive or Google Docs files shared externally from an internal domain. It leverages GSuite Drive logs, extracting and comparing the source and destination email domains to identify external sharing. This activity is significant as it may indicate potential data exfiltration by an attacker or insider. If confirmed malicious, this could lead to unauthorized access to sensitive information, data leakage, and potential compliance violations. Monitoring this behavior helps in early detection and mitigation of data breaches.
'Identifies IP addresses performing DNS lookups associated with common ToR proxies.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
'Identifies anamalous spikes in network traffic logs as compared to baseline or normal historical patterns.
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
'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.'
'This query helps identify rare DNS connections and resulting data transfer to/from the associated domain. It can help identify unexpected large data transfers to or from internal systems which may indicate data exfil or malicious tool download.'
Well-known TAP software installation. Possible preparation for data exfiltration using tunneling techniques
One common technique leveraged by attackers is using archiving applications to package up files for exfiltration. In many cases, these archives are usually protected with a password to make analysis more difficult. This query identifies applications which leverage a command line pattern which matches the 7zip and WinRAR command line executables to create or update an archive when a password is specified. By detecting based on the command line we can avert attempts to dodge detection by renaming the application.
Happy hunting!
Happy hunting!
Detects when a user performs data exfiltration by using DataSvcUtil.exe
Detects DNS queries for subdomains related to MEGA sharing website
Detects the execution of the hdiutil utility in order to create a disk image.
Detects the execution of the BCP utility in order to export data from the database.
Attackers were seen saving their malware to a database column or table and then later extracting it via "bcp.exe" into a file.
Attackers were seen saving their malware to a database column or table and then later extracting it via "bcp.exe" into a file.
Detects the export of the target Registry key to a file.
Check all created files.
That does not have extension ps1, bat or cmd to avoid IT Pro scripts.
That are not copied to C:\ to detect all file share, external drive, data partition that are not allowed, etc.
This could help to detect malicious insider/user that has unencrypted data partition and that are using it to exfiltrate data even while removable devices & cloud storage is blocked.
That does not have extension ps1, bat or cmd to avoid IT Pro scripts.
That are not copied to C:\ to detect all file share, external drive, data partition that are not allowed, etc.
This could help to detect malicious insider/user that has unencrypted data partition and that are using it to exfiltrate data even while removable devices & cloud storage is blocked.
'This rule identifies a web request with a user agent header known to belong to a hacking tool. This indicates a hacking tool is used on the host.<br>You can add custom hacking tool indicating User-Agent headers using a watchlist, for more information refer to the [UnusualUserAgents Watchlist](https://aka.ms/ASimUnusualUserAgentsWatchlist).
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
This query looks for mass downloads identified by Microsoft Defender for Cloud Apps. It will require an corresponding app connector in Microsoft Defender for Cloud Apps.
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