Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects successful or failed logon attempts originating from IP addresses identified by threat intelligence as associated with botnets, command and control (C2) infrastructure, or proxies. The rule specifically looks for 'LogOn' or 'FailedLogOn' activity types where the source IP's threat intelligence indicator type is 'Botnet', 'C2', or 'Proxy', and where activity insights are available.
This rule detects potential beaconing or re-registration activity associated with the JDY/KV-botnet by monitoring network connections to known Tor control/relay ports (9001 and 9050). The detection identifies endpoints making high-volume, multi-destination connections to external IPs over these specific ports, a pattern indicative of botnet nodes attempting to re-establish C2 communication after infrastructure disruption or actively recruiting new nodes.
Detects high-volume outbound network scanning activity from a single internal source device. The rule identifies anomalous behavior characterized by rapid connection attempts to a large number of distinct destination IPs and ports, consistent with scanning patterns observed in botnets like JDY, which often target edge network infrastructure (Cisco, Ubiquiti, Draytek).
This rule detects successful or failed logon attempts originating from IP addresses identified by threat intelligence as associated with botnets, command and control (C2) infrastructure, or proxies. The rule specifically looks for 'LogOn' or 'FailedLogOn' activity types where the source IP's threat intelligence indicator type is 'Botnet', 'C2', or 'Proxy', and where activity insights are available.
Detects successful or failed logon attempts originating from IP addresses identified as associated with botnets, C2 infrastructure, or proxies by threat intelligence feeds. This indicates potential malicious activity or compromised accounts.
Detects access to botnet command and control panels
Detects bandwidth hijacking through peer-to-peer botnet participation
Botnet C2 Panel Access Detected
Suricata
This rule detects potential access to a botnet command and control (C2) panel. It triggers when HTTP URI requests contain both 'panel' and 'bot' (case-insensitive) within the URI. A threshold of 5 such requests from the same source within 60 seconds is set to reduce false positives.
This rule detects access to websites or services that are commonly associated with botnet rental, stresser, or DDoS-for-hire services. It specifically looks for HTTP requests where the host header contains keywords like 'booter', 'stresser', or 'ddoser' (case-insensitive). This indicates a potential policy violation where an internal user might be attempting to utilize or research such services.
This rule detects successful or failed logon attempts originating from IP addresses identified by threat intelligence as associated with botnets, command and control (C2) infrastructure, or proxies. The rule specifically looks for 'LogOn' or 'FailedLogOn' activity types where the source IP's threat intelligence indicator type is 'Botnet', 'C2', or 'Proxy', and where activity insights are available.
KQL Query
'The query looks for source code files being modified immediately after a build process is started. The purpose of this is to look for malicious code injection during the build process.
More details: https://techcommunity.microsoft.com/t5/azure-sentinel/monitoring-the-software-supply-chain-with-azure-sentinel/ba-p/2176463'
More details: https://techcommunity.microsoft.com/t5/azure-sentinel/monitoring-the-software-supply-chain-with-azure-sentinel/ba-p/2176463'
This query was originally published in the threat analytics report, Sysrv botnet evolution.
Sysrv is a Go-based botnet that targets both Windows and Linux servers, and steals resources to mine cryptocurrency.
The following query finds instances where the attacker commanded the Kinsing miner file to be downloaded on Linux devices.
Sysrv is a Go-based botnet that targets both Windows and Linux servers, and steals resources to mine cryptocurrency.
The following query finds instances where the attacker commanded the Kinsing miner file to be downloaded on Linux devices.
This query was originally published in the threat analytics report, Sysrv botnet evolution.
Sysrv is a Go-based botnet that targets both Windows and Linux servers, and steals resources to mine cryptocurrency.
The following query finds instances of the attacker attempting to stop the AppArmor network security service on devices running Linux.
Sysrv is a Go-based botnet that targets both Windows and Linux servers, and steals resources to mine cryptocurrency.
The following query finds instances of the attacker attempting to stop the AppArmor network security service on devices running Linux.
'This hunting query will identify instances where a single user account has seen a high incidence of failed attempts from highly volatile IP addresses
Changing IP address for every password attempt is becoming a more common technique amongst sophisticated threat groups. Often threat groups will randomise
the user agent they are using as well as IP address. This technique has been enabled by the emergence of services providing huge numbers of residential IP
addresses. These services are often enabled through malicious browser plugins. This query is best executed over longer timeframes.
Reduce the timeRange if you have too much data. Results with the highest "IPs", "Failures" and "DaysWithAttempts" are good candidates for further
investigation. This query intentionally does not cluster on UserAgent, IP etc. This query is clustering on the highly volatile IP behaviour.'
Changing IP address for every password attempt is becoming a more common technique amongst sophisticated threat groups. Often threat groups will randomise
the user agent they are using as well as IP address. This technique has been enabled by the emergence of services providing huge numbers of residential IP
addresses. These services are often enabled through malicious browser plugins. This query is best executed over longer timeframes.
Reduce the timeRange if you have too much data. Results with the highest "IPs", "Failures" and "DaysWithAttempts" are good candidates for further
investigation. This query intentionally does not cluster on UserAgent, IP etc. This query is clustering on the highly volatile IP behaviour.'
This is a query to retrieve last 30 days network connections to known Dofoil NameCoin servers.
The full article is available here: https://cloudblogs.microsoft.com/microsoftsecure/2018/04/04/hunting-down-dofoil-with-windows-defender-atp.
The full article is available here: https://cloudblogs.microsoft.com/microsoftsecure/2018/04/04/hunting-down-dofoil-with-windows-defender-atp.