Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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'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.'
'This will check for Rare User Agent strings over the last 3 days. This can indicate potential probing of your IIS servers.'
The following queries pivot from an IP address assigned to a machine to the relevant machine or logged-on users.
To read more about it, check out this post: https://techcommunity.microsoft.com/t5/What-s-New/Advanced-hunting-now-includes-network-adapters-information/m-p/224402#M74.
Query #1: get machines that have used a given local IP address at a given time - as configured on their network adapters.
To read more about it, check out this post: https://techcommunity.microsoft.com/t5/What-s-New/Advanced-hunting-now-includes-network-adapters-information/m-p/224402#M74.
Query #1: get machines that have used a given local IP address at a given time - as configured on their network adapters.
The following analytic identifies network traffic where the higher layer protocol does not match the expected port, such as non-HTTP traffic on TCP port 80. It leverages data from network traffic inspection technologies like Bro or Palo Alto Networks firewalls. This activity is significant because it may indicate attempts to bypass firewall restrictions or conceal malicious communications. If confirmed malicious, this behavior could allow attackers to evade detection, maintain persistence, or exfiltrate data through commonly allowed ports, posing a significant threat to network security.
The analytic identifies ICMP traffic to external IP addresses with total bytes (sum of bytes in and bytes out) greater than 1,000 bytes. It leverages network traffic data to detect large ICMP packets that aren't blocked and are directed toward external networks. This can indicate information smuggling, covert communication, or command-and-control (C2) activities, potentially signaling ICMP tunneling, unauthorized data transfer, or compromised endpoints.
This analytic detects the presence of SIGRed (CVE-2020-1350), a critical DNS vulnerability, using Zeek DNS and Zeek Conn data. It identifies specific DNS query types (SIG and KEY) and checks for high data transfer within a flow (over 65KB). This detection is significant because SIGRed allows attackers to execute remote code on Windows DNS servers, potentially leading to unauthorized access and control. If confirmed malicious, this activity could result in data exfiltration, service disruption, or further network compromise. Immediate investigation and mitigation, such as patching or isolating the affected server, are crucial.
The analytic detects spikes in Server Message Block (SMB) traffic connections, which are used for sharing files and resources between computers. It leverages network traffic logs to monitor connections on ports 139 and 445, and SMB application usage. By calculating the average and standard deviation of SMB connections over the past 70 minutes, it identifies sources exceeding two standard deviations from the average. This activity is significant as it may indicate potential SMB-based attacks, such as ransomware or data theft. If confirmed malicious, attackers could exfiltrate data or spread malware within the network.
This analytic identifies significant changes in network communication behavior within Kubernetes containers by examining the inbound to outbound network IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. Anomalies are detected using a lookup table containing average and standard deviation values for network IO, triggering an event if the anomaly persists for over an hour. This activity is significant as it may indicate data exfiltration, command and control communication, or compromised container behavior. If confirmed malicious, it could lead to data breaches, service outages, and unauthorized access within the Kubernetes cluster.
This rule identifies activities related to Google Cloud Platform (GCP) Packet Mirroring. Packet Mirroring allows capturing full network traffic, which can be abused by adversaries to intercept and collect sensitive data from unencrypted internal network communications. The rule specifically looks for various API calls related to the creation, modification, deletion, or listing of Packet Mirroring configurations.
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