Azure Sentinel
@AzureSentinel50 followers935 downloads1,416 copies43 likes37,640 views
1,223 detections
Filters
Last updated
All Time
Detection languages
1,223
Categories
220
218
180
171
135
Platforms
766
284
188
78
29
Products / Services
492
297
159
152
93
MITRE Techniques
530
485
467
348
348
Roasting
KQL
Detect Active Directory LDAP queries that search for Kerberoasting (SPNs) or accounts with Kerberos preauthentication not required from Azure ATP, and try to get the process initiated the LDAP query from MDATP.
Replace 389 on line 5 with LDAP port in your environment
Replace true on line 6 to false if you want to include Nt Authority process
This LDAP query cover Rubeus, Kerberoast, BloodHound tools
Replace 389 on line 5 with LDAP port in your environment
Replace true on line 6 to false if you want to include Nt Authority process
This LDAP query cover Rubeus, Kerberoast, BloodHound tools
This query helps hunting for possible device code Phishing attempts
'This hunting query will alert on any Impossible travel activity in correlation with mailbox permission tampering followed by account being added to a PIM managed privileged group.
Ensure this impossible travel incident with increase of privileges is legitimate in your environment.'
Ensure this impossible travel incident with increase of privileges is legitimate in your environment.'
This query visualises the daily amount of emails that had an admin post delivery action, summarizing the data by action type
'The purpose of this content is to identify successful phishing links accessed by users. Once a user clicks on a phishing link, we observe successful network activity originating from non-Microsoft network devices. These devices may include Palo Alto Networks, Fortinet, Check Point, and Zscaler devices.'
This query visualises the daily amount of emails that had a post delivery action from zero-hour auto purge.
This query visualises emails released from quarantine and summarizing the result by the original filter verdict
'This query helps detect new Microsoft Entra ID sign in from a new location correlating with Office Activity data highlighting cases where user mails are being forwarded and shows if it is being forwarded to external domains as well.'
Process executed from binary hidden in Base64 encoded file. Encoding malicious software is a.
Technique to obfuscate files from detection.
The first and second ProcessCommandLine component is looking for Python decoding base64.
The third ProcesssCommandLine component is looking for the Bash/sh commandline base64 decoding tool.
The fourth one is looking for Ruby decoding base64.
Technique to obfuscate files from detection.
The first and second ProcessCommandLine component is looking for Python decoding base64.
The third ProcesssCommandLine component is looking for the Bash/sh commandline base64 decoding tool.
The fourth one is looking for Ruby decoding base64.
'Identifies patterns in the time deltas of contacts between internal and external IPs in Fortinet network data that are consistent with beaconing.
Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.
The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a detection is set to 4.
Increase the lookback period to capture beacons with larger periodicities.
The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with automatically using series_outliers.
Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'
Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.
The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a detection is set to 4.
Increase the lookback period to capture beacons with larger periodicities.
The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with automatically using series_outliers.
Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'
Page 9 of 123