Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network connections to Google Cloud services (calendar.google.com, googleapis.com) over port 443 originating from processes other than known web browsers or standard Google background applications. This behavior may indicate unauthorized tools, custom scripts, or malware using Google services for command and control or data exfiltration.
Detects wscript.exe executing VBScript that uses the MSXML2.XMLHTTP component to perform an HTTP GET request to a specific Kimsuky C2 URL (acnms.dmdoc.dynv6.net). This activity is associated with a multi-stage infection chain where a VBScript file retrieves and executes a subsequent stage from the specified remote endpoint.
Detects potential data exfiltration from Microsoft 365 Copilot sessions where the Bing 'Search by Image' functionality is abused as an SSRF proxy. The rule identifies network requests containing external URLs within the 'imgurl' parameter, occurring shortly after Copilot activity, suggesting that internal or sensitive data (such as emails, calendar items, or MFA context) is being transmitted to an unauthorized external endpoint.
Detects network communication associated with BeepRAT malware, specifically targeting hardcoded TCP connections to destination port 18848 and ICMP packets containing the string 'BEEP', which are used for C2 communication and keepalive signals.
Detects potentially malicious child process creation or unauthorized outbound network connections initiated by AI-assisted coding tools (e.g., Claude, Cursor, Windsurf) through their integrated Node.js or Python environments. This rule identifies common post-exploitation behaviors such as shell execution, command-line reconnaissance, and connections to external non-internal IP addresses.
This rule identifies suspicious outbound network beaconing by analyzing flow statistics for consistency in interval and payload size. It flags processes that demonstrate periodic communication (heartbeats) to external endpoints on non-standard ports. The detection logic calculates a 'beacon_score' based on the consistency of the communication interval, small average payload sizes, low variance in payload sizes, and the number of connections. Additional weighting is applied to connections made by typically non-network processes (e.g., svchost, explorer, wscript) or those connecting to ports associated with known malware like SystemBC.
Detects evidence of Djinn Stealer malware activity, including unauthorized access to web browser credential stores, suspicious child process creation by the SimpleHelp RMM daemon, and network communication with a known command-and-control server (96.126.130.126:58942) using a specific telemetry client user agent.
This rule detects potential persistence mechanisms by monitoring the creation of scheduled tasks using the 'schtasks' utility or modifications to Windows Registry 'Run' keys, specifically targeting suspicious paths (AppData, Temp, or ProgramData). The detection logic correlates these suspicious persistence artifacts with network activity by joining the events with Sysmon Event ID 3 (Network connection) to identify hosts communicating over common C2-associated ports (4000, 4001, 4002, 443) to non-private IP addresses. A risk score is calculated based on the presence of these persistence markers and outbound network connections.
This rule detects a multi-stage activity chain where a user performs a git clone, followed by Visual Studio Code, Node, or Python spawning a shell or interpreter, which then initiates a network connection to known suspicious staging infrastructure. This behavior is indicative of a developer machine being compromised to exfiltrate code or download malicious payloads.
Detects suspicious network activity from common scripting and runtime environments (Python, Node.js) that communicate with known blockchain-related infrastructure (e.g., Tron, Aptos). The rule flags processes that establish connections to multiple distinct external IP addresses while querying these specific domains, which may indicate cryptocurrency mining, wallet-stealing malware, or unauthorized distributed computing activity.
This rule detects repeated DNS queries to known Dynamic DNS (DDNS) providers within a 24-hour window, which may indicate an infected host beaconing to a command-and-control (C2) server utilizing dynamic infrastructure. Such rotation is commonly used by malware to maintain connectivity if primary infrastructure is blocked.
This rule monitors network traffic for specific command-and-control (C2) communication patterns associated with the Ousaban banking trojan. It detects response packets from an external server to a client machine containing JSON-like structural indicators such as 'inicio{', 'host', and 'porta' followed by '}fim'.
Detects frequent, persistent outbound TLS connections to api.telegram.org. This behavior is indicative of a malware sample or backdoor polling the Telegram Bot API for command and control (C2) instructions, effectively using the legitimate Telegram infrastructure to tunnel malicious communication.
High-velocity ransomware-as-a-service with aggressive leak-site extortion
Form-grabbing and credential stealing malware-as-a-service
Rust-based ransomware family targeting enterprise environments
Rust-based ransomware family targeting enterprise environments
Detects recurring network connections (beaconing) to domains ending in 'azurewebsites.net' over HTTPS (port 443). The rule identifies potential command and control (C2) behavior by aggregating counts of connections and distinct destination IPs per process and host over time, flagging activity that exhibits regular, repeating patterns.
This rule monitors for a combination of suspicious behaviors including C2 network activity to known malicious IPs, potential credential theft targeting browser data files, suspicious library loading (such as keyloggers or potential sideloading) from temp or appdata directories, and beaconing activity using non-standard or suspicious processes. The rule calculates a risk score based on the number and type of indicators observed on a single host.
This rule detects the BQTlock ransomware behavior of capturing desktop screenshots using suspected malicious processes ('BQT.exe', 'start.exe') and attempting to exfiltrate the captured data via the Telegram API using 'sendPhoto' or 'sendDocument' commands.
macOS malware used by JINX-0164 against cryptocurrency developers
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