Security Event Clustering via Coordinate Vector Transformation
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Solution Overview
Problem
Current security systems struggle to effectively detect and differentiate between malicious activities, including malware and non-malware threats, such as advanced persistent threats (APTs), which often evade detection due to their sophisticated nature and lack of signature-based methods.
Innovation Solution
The implementation of a method that analyzes events on a monitored computing device by generating coordinate vectors from event data, clustering these vectors to identify patterns associated with security violations, and providing a user interface for security analysts to distinguish between clean and malicious activities without requiring specific software detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If signature-based detection methods are used, then detection speed is improved, but detection precision deteriorates because sophisticated threats like APTs and zero-day exploits lack known signatures
Solution Approach 1:
The system transforms security event data into coordinate vectors in a multi-dimensional space, changing the detection parameters from signature matching to spatial clustering. This allows the system to detect threats based on behavioral patterns and relationships between multiple events rather than relying on known signatures, thereby improving detection precision for sophisticated threats while maintaining detection speed through efficient vector operations
Solution Approach 2:
The patent introduces a new dimension by representing security events as coordinate vectors in a multi-dimensional space rather than using traditional signature-based one-dimensional matching. This dimensional transformation enables the system to capture complex relationships between multiple events and detect sophisticated threats through clustering algorithms that operate in this enhanced space
2Measurement precision
If clustering algorithms are applied to detect patterns, then detection precision is improved, but device complexity increases due to the need for coordinate vector generation and clustering computations
Solution Approach 1:
The system replaces complex traditional security analysis mechanisms with coordinate vector-based clustering. Instead of using multiple separate detection rules and heuristics, the patent substitutes a unified mathematical approach where events are transformed into vectors and clustered using algorithms like k-means or DBSCAN, simplifying the overall system architecture while improving detection precision
Solution Approach 2:
The patent changes the fundamental parameters of security event representation from text-based signatures and rule conditions to numerical coordinate vectors. This parameter transformation enables the use of efficient mathematical operations and clustering algorithms, reducing computational complexity compared to traditional rule-based systems while maintaining or improving detection precision
3Ease of operation
If traditional security applications scan for malware signatures, then ease of operation is maintained, but reliability deteriorates because they miss sophisticated non-malware malicious activities
Solution Approach 1:
The coordinate vector clustering system serves multiple detection functions simultaneously - it can detect malware, APTs, zero-day exploits, and other sophisticated threats using the same underlying mechanism. This universal approach improves reliability across different threat types while maintaining ease of operation, as the system requires the same configuration and operation regardless of the specific threat being detected
Data Source
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AI summary
An event can be associated with a monitored computing device and a command-line record. An event vector can be determined for each of a plurality of events based at least in part on at least a portion of the respective command-line record and on a trained representation mapping. A respective reduced event vector can be determined having fewer elements. The reduced event vectors can be clustered to determine cluster identifiers. A first event can be determined to be associated with a security violation based on a corresponding cluster identifier matching a cluster identifier of a second event that is associated with a security violation. In some examples, a cluster can include a relatively larger first group of events and a relatively smaller second group of events. That cluster can be determined to satisfy a criterion based on the numbers of events in at least one of the groups.