Ibrahim Saud
@tektrixIndiaCompletionist
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11,679 detections
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Detects high-volume file and data access events within Microsoft 365 services (SharePoint, OneDrive, Exchange) originating from Python-based user agents (e.g., 'python-requests', 'python-httpx'). This pattern is indicative of automated scraping, data exfiltration, or bulk activity performed by unauthorized scripts.
Detects a Node.js process spawning a detached, hidden child node.exe process from within a node_modules directory, a technique observed in the indexed-btree npm supply-chain malware to establish a stealthy runtime loader.
Detects the deletion of specific files associated with the indexed-btree malware loader, such as 'sharedLoad.min.js' or files within an 'extended/' directory. This behavior indicates potential anti-forensic cleanup following malicious code execution.
Detects high-volume file access or download activity by a single user in SharePoint or OneDrive over a 15-minute window. This behavior is indicative of bulk cloud data exfiltration often associated with extortion campaigns.
Detects high-volume, automated enumeration of cloud resources (SharePoint, OneDrive, or Exchange) via Graph API or search queries immediately following a user sign-in event. This behavior is indicative of session hijacking (token replay) followed by unauthorized scripted discovery, as opposed to legitimate user browsing activity.
Detects the execution of the AnyDesk installer when triggered by a process spawned from common web browsers. This pattern is often observed when users download and execute remote access software from the internet.
This rule detects unauthorized mailbox access by identifying specific ClientAppId and API ID pairs known to be associated with an Office 365 email exfiltration toolkit. This activity is indicative of an attacker attempting to programmatically access or exfiltrate sensitive email data from the organization's environment.
This rule captures all successful login events to serve as the foundational data source for downstream correlation engines. It is designed to identify suspicious patterns such as credential reuse across disparate IP addresses, anomalous device fingerprints following MFA, or account logins shortly after phishing visits. This filter acts as a raw event provider for more complex logic that cannot be evaluated within a single-event context.
This rule detects potentially unauthorized access or downloading of files from SharePoint and OneDrive, as well as specific MailItemsAccessed events associated with known exfiltration toolkit patterns within Microsoft 365. It flags individual events matching these criteria as a baseline for identifying suspicious data access behaviors.
Detects successful user logins to accounts with names containing support-related terminology such as 'IT Support', 'Help Desk', or 'IT Department'. These accounts are often highly privileged or sensitive targets for credential abuse.
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