Detections

Explore public detection logic contributed by the community across SIEM and rule languages.

2,686 detections

Detects bulk file reading from network shares for data collection
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Darshan Thummar@Mighty
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Detections.ai Community
5 months ago
003
This rule detects Bluetooth Object Exchange (OBEX) Protocol data transfer activity on TCP port 3232. The rule is configured to alert when five such data transfers are observed from the same source IP within a 60-second window, indicating potential exfiltration or unauthorized data movement over Bluetooth.
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Darshan Thummar@Mighty
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Detections.ai Community
5 months ago
0011
This rule detects an unusually high number of downloads of files identified as 'octet-stream' or 'executable' from a single IP address within a one-hour window, based on W3CIISLog data. This behavior could indicate various malicious activities, including the distribution of malware, data exfiltration, or the use of a compromised web server for command and control.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
007
This rule detects successful network connections to known paste sites like Pastebin or Gist. Adversaries often use these services to host malicious code, exfiltrate data, or for command and control (C2) communications. Monitoring such connections can help identify potential data exfiltration attempts or the download of malicious content.
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Ankit Mehta@Secvyn
Bharat Cyber Guardians
5 months ago
007
This rule detects users uploading a high volume of files (50 or more within an hour) to SharePoint or OneDrive. This activity could indicate data exfiltration or unauthorized mass data transfer.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
007
Detects upload of backdoored container images to internal registries
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Darshan Thummar@Mighty
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Detections.ai Community
5 months ago
005
Detects bulk downloading of SharePoint document libraries
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Darshan Thummar@Mighty
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Detections.ai Community
5 months ago
005
This rule detects an unusually high volume of object close operations (Event ID 4658) by a single user on a single computer within a one-hour window. This activity could indicate various malicious behaviors, including data collection for exfiltration, attempts to delete numerous files, or resource exhaustion attacks.
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Ankit Mehta@Secvyn
Bharat Cyber Guardians
5 months ago
009
This rule detects devices making a high volume of successful network connections to GitHub's API endpoints (api.github.com or api.releases). A count of 5 or more successful connections within an hour from a single device is considered suspicious and could indicate data exfiltration, automated tooling, or unusual development activity.
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Ankit Mehta@Secvyn
Bharat Cyber Guardians
5 months ago
009
This rule identifies an anomalous surge in network connection attempts to a particular remote IP address. It establishes a baseline of average daily connection counts to each remote IP over the past week. It then compares the current hourly connection count to this baseline, triggering an alert if the current count is more than five times the baseline and surpasses a total of 500 connections within that hour. This could indicate various malicious activities such as data exfiltration, command and control communication, or a denial-of-service attack originating from the monitored environment.
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Ankit Mehta@Secvyn
Bharat Cyber Guardians
5 months ago
109
This rule detects suspicious activity where a Python process initiates network communication to known command and control (C2) domains, immediately followed by accessing SQLite database files (e.g., 'places.sqlite' or 'Cookies'). This pattern is indicative of malware or adversary activity attempting to exfiltrate browser history or session cookies after establishing C2.
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Ibrahim Saud@tektrix
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Detections.ai Community
4 months ago
001
Detects large data transfers to removable storage media for physical exfiltration
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Darshan Thummar@Mighty
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Detections.ai Community
5 months ago
006
Detects PowerShell iterating directories for known cryptocurrency wallet extensions and associated strings, indicative of PS1Bot malware activity attempting to harvest wallet data.
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Ibrahim Saud@tektrix
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Detections.ai Community
5 months ago
002
This rule detects successful network connections to known paste sites like Pastebin or Gist. Adversaries often use these services to host malicious code, exfiltrate data, or for command and control (C2) communications. Monitoring such connections can help identify potential data exfiltration attempts or the download of malicious content.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
006
This rule detects when a single user downloads 100 or more files from SharePoint within a one-hour period. This activity could indicate data exfiltration or unauthorized bulk access to sensitive documents.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
006
This rule detects unusually large network uploads from devices. It identifies successful network connections where individual transfers exceed 100MB and aggregates these transfers by account name and hour. The rule then flags any account that has uploaded a total of 1TB or more within a single hour. This pattern could indicate data exfiltration, unauthorized data synchronization, or other malicious activities involving significant data transfer volumes.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
006
This rule detects an unusually high volume of file sharing events from a single user in OneDrive within a one-hour window. Specifically, it looks for five or more 'SharingSet' or 'AnonymousLinkCreated' operations by the same UserId within an hour, which could indicate data exfiltration or unauthorized sharing.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
006
Detects high volume Google Cloud Storage (GCS) object listing and downloads, which can indicate reconnaissance of cloud storage contents or data exfiltration. The rule aggregates operations by identity, bucket, and project ID over a one-hour period and flags activity exceeding 50 total operations.
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Lacey Cochrane@NullVectorX
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XQL Threat Forge
5 months ago
5010
Detects external users accessing internal Slack canvases and suspicious canvas deletion or access level changes. Canvases can contain sensitive runbooks, credentials, and organizational data, making these actions critical to monitor for potential data exfiltration or unauthorized access.
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Lacey Cochrane@NullVectorX
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XQL Threat Forge
5 months ago
507
This rule detects anomalous network connections originating from common communication applications (Teams, Slack, Discord, Skype) to non-standard ports (not 80, 443, 8080, 8443) and to IP addresses not typically associated with Microsoft or other major cloud providers (e.g., not starting with 13. or 51.). It specifically looks for a high volume of such connections (10 or more within an hour) from a single process on a device, which could indicate protocol tunneling or the use of these applications for command and control.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
002
This rule detects high volume data exfiltration (over 1GB) from devices to a list of known malicious IP addresses within a one-hour window. It specifically looks for network events where a significant amount of data (SentBytes > 100MB initially, then summed to over 1GB) is sent to hardcoded remote IP addresses associated with potential command and control or data exfiltration infrastructure.
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Ankit Mehta@Secvyn
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Detections.ai Community
5 months ago
002
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