Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
3,989 detections
Filters
Last updated
All Time
Detection languages
1,774
868
397
195
188
Contributors
1,070
924
756
221
96
Categories
2,448
1,307
1,164
785
713
Platforms
2,415
1,310
658
528
133
Products / Services
1,307
886
808
561
337
MITRE Techniques
18,128
15,651
12,599
8,410
6,235
3,989
CVEs
6
5
5
5
5
IDS Classtypes
564
275
11
8
6
IDS Protocols
537
174
64
64
12
This rule monitors for known malicious file hashes, IP addresses, C2 domains, and specific URL markers associated with identified threat activity. It consolidates multiple detection vectors including file activity, process execution, network connections, and DNS queries to identify compromised devices communicating with attacker-controlled infrastructure.
Detects outbound network connections from devices to specific suspicious domains identified in the detection logic. This rule monitors for connections to 'ns2.asiainfo.it.com' and 'www.wordcheck.info', which may be indicative of malware communication, command and control, or malicious web activity.
Detects the CastleRAT intermediate loader, which acts as a secondary stage before deploying SloppyRAT. The rule identifies the malicious payload by scanning for unique embedded strings, specific user-agent strings, and command-and-control (C2) communication patterns (domains and URI paths) within executable files.
This rule performs a hunting activity across multiple telemetry sources (Network, Email, Endpoint) to detect known indicators of compromise (IOCs) associated with Anthropic threat intelligence reporting (September 2026). It looks for specific malware file names, command-line patterns, service installations, phishing email addresses, and C2 communication URLs.
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
Detects network traffic directed to the Telegram Bot API (api.telegram.org) characterized by TLS SNI indicators or specific HTTP URI structures (e.g., /bot). This behavior is indicative of malware or automated tools using the Telegram platform as a command-and-control (C2) channel for beaconing, command delivery, or data exfiltration.
This rule detects potential malicious activity by monitoring for known malicious file hashes (MD5) across file and process events, and correlating them with known Command and Control (C2) infrastructure communication patterns, including specific IP addresses and URI paths.
This rule detects potential malicious activity by monitoring for known malicious file hashes (MD5) across file and process events, and correlating them with known Command and Control (C2) infrastructure communication patterns, including specific IP addresses and URI paths.
This rule detects potential malicious activity by monitoring for known malicious file hashes (MD5) across file and process events, and correlating them with known Command and Control (C2) infrastructure communication patterns, including specific IP addresses and URI paths.
This rule detects potential malicious activity by monitoring for known malicious file hashes (MD5) across file and process events, and correlating them with known Command and Control (C2) infrastructure communication patterns, including specific IP addresses and URI paths.
This rule detects a correlation between host-level reconnaissance activity using WMI (via PowerShell or WMIC) to query system information, followed by network exfiltration to a known C2 IP address over a specific URI within a 10-minute window.
This rule detects a correlation between host-level reconnaissance activity using WMI (via PowerShell or WMIC) to query system information, followed by network exfiltration to a known C2 IP address over a specific URI within a 10-minute window.
Detects high-volume cloud storage access or download activity originating from the python-httpx library within a short window (2 hours). This behavior is often indicative of automated data scraping, exfiltration, or bulk content synchronization.
Detects outbound network connections to known command-and-control IP addresses associated with the SpiceRAT malware family. The rule monitors DeviceNetworkEvents for successful connections or connection attempts over ports 80 and 443.
Detects outbound network connections to known command-and-control IP addresses associated with the SpiceRAT malware family. The rule monitors DeviceNetworkEvents for successful connections or connection attempts over ports 80 and 443.
Detects DNS resolutions of known SpiceRAT command-and-control hostnames to multiple distinct C2 IP addresses within a 30-day window. This behavior is consistent with automated C2 infrastructure rotation or the use of multiple redundant IP addresses for persistence.
This rule detects HTTP GET requests to a specific URI path ('/wp-content/uploads/init/') on the domain 'wasmer.app', which is associated with Kimsuky (APT43) C2 beaconing activity. The presence of a token parameter in the URI and the structure of the request indicates an established connection for command and control purposes.
Detects network connection attempts from endpoints to known proxy service domains (IPRoyal, LightningProxies) that have been observed acting as infrastructure for CHOSEN BRICK Telegram-based C2 and exfiltration traffic.
Page 62 of 190