Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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KQL Query from file: DefenderXDR Weekly OSINT Indicators Scan 12052025
KQL Query
KQL Query
KQL Query from file: Defending against dot zip domain phishing attack
KQL Query from file: Golden Chickens TerraStealerV2 Malware Detection
KQL Query from file: Identity Blast Radius
KQL Query
KQL Query
KQL Query
KQL Query
KQL Query
KQL Query
Detects the execution of rundll32.exe with the oledb32.dll library to open a UDL file.
Threat actors can abuse this technique as a phishing vector to capture authentication credentials or other sensitive data.
Threat actors can abuse this technique as a phishing vector to capture authentication credentials or other sensitive data.
Detects the execution of rundll32.exe with the oledb32.dll library to open a UDL file.
Threat actors can abuse this technique as a phishing vector to capture authentication credentials or other sensitive data.
Threat actors can abuse this technique as a phishing vector to capture authentication credentials or other sensitive data.
The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches.
The analytic identifies anomalous inbound network traffic volumes from processes within containerized workloads. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days. This activity is significant as it may indicate unauthorized data reception, potential breaches, vulnerability exploitation, or malware propagation. If confirmed malicious, it could lead to command and control installation, data integrity damage, container escape, and further environment compromise.
The PowerShell implementation of DNSCat2 calls nslookup to craft queries. Counting nslookup processes spawned by PowerShell will show hundreds or thousands of instances if PS DNSCat2 is active locally.
'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.'
'Malware authors will sometimes hardcode user agent string values when writing the network communication component of their malware. Malformed user agents can be an indication of such malware.'
GALLIUM Artefacts
Sigma
Detects artefacts associated with activity group GALLIUM - Microsoft Threat Intelligence Center indicators released in December 2019.
'This content is utilized to identify instances of successful login by risky users, who have been observed engaging in potentially suspicious network activity on non-Microsoft network devices.'
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