Security Alert Similarity Detection Using Vector Embeddings
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Solution Overview
Problem
Existing network security systems face challenges in efficiently and effectively analyzing and responding to cybersecurity threats due to the difficulty in institutionalizing and scaling expertise in recognizing malicious patterns in large volumes of data.
Innovation Solution
The system measures similarity between security alerts and incidents using machine-learned models and locality-sensitive hashes, allowing for efficient grouping and analysis of incidents, and enabling automated response strategies based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of security alerts by seasoned professionals is used, then accuracy in recognizing malicious patterns is improved, but productivity and scalability deteriorate
Solution Approach 1:
The system creates vector representations that copy and encode the expertise patterns recognized by seasoned security professionals. These vector embeddings capture the nuanced understanding of malicious patterns that human analysts develop over time, allowing this expertise to be replicated and applied consistently across all security alert analysis without requiring human analysts to manually examine each alert.
Solution Approach 2:
The patent replaces the mechanical process of manual human analysis with an automated machine learning system. The vector embedding model and similarity comparison algorithms substitute for the cognitive processes of human analysts, enabling automated identification of similar security incidents while maintaining the pattern recognition accuracy that seasoned professionals provide.
2Measurement precision
If comprehensive analysis of all security alerts is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-computing vector embeddings for security alerts and organizing them in a searchable vector database before actual incident analysis is needed. When a new security alert arrives, the system can immediately compare its vector representation against the pre-indexed database using efficient similarity search algorithms, avoiding the need for comprehensive analysis of all historical alerts at the moment of incident response.
Solution Approach 2:
The system extracts only the most relevant features and characteristics of security alerts into compact vector representations. Instead of analyzing all raw data from security alerts comprehensively, the system extracts key patterns and attributes into fixed-dimensional vectors that capture the essential information needed for similarity comparison, significantly reducing the computational time required for analysis.
3Productivity
If automated response systems are implemented, then productivity is improved, but reliability deteriorates due to lack of human expertise
Solution Approach 1:
The system copies the decision-making patterns and expertise of seasoned security professionals into the vector embedding model through training on historical security data. This allows the automated system to replicate human expert judgment and reasoning processes, maintaining reliability while achieving productivity gains through automation. The model learns from numerous examples of how experts classify and respond to different types of security incidents.
Solution Approach 2:
The system implements feedback mechanisms where the results of automated threat classification can be reviewed and validated by human security professionals. This feedback loop allows continuous improvement of the vector embedding model and ensures that automated responses maintain high reliability by incorporating human expertise validation and correction when needed.
Data Source
AI summary
Described are systems and methods for measuring the similarity of security alerts, security incidents, or other complex data structures at scale using machine-learned signature vectors suitable for efficient similarity-based filtering in conjunction with hashes of the security alerts for more detailed comparisons. In some embodiments, similarity measurements between security incidents are used to base the processing of a current security incident on similar prior security incidents.