XDR Event Embeddings for Faster Threat Pattern Detection
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Solution Overview
Problem
Cybersecurity systems face challenges in efficiently analyzing large volumes of heterogeneous event data from sources like EDR, XDR, and SIEM, leading to difficulties in detecting sophisticated threats due to noise, false positives, and complex attack patterns, which traditional methods struggle to address.
Innovation Solution
A computer-implemented method using vector embeddings to process event data, involving event encoders, collection encoders, and processing heads for tasks like similarity detection, anomaly detection, and classification, to generate fixed-length vectors from event collections, reducing dimensionality and enhancing analysis efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional security analysis methods are used to process large volumes of heterogeneous event data, then the system can handle diverse event types, but the analysis efficiency deteriorates due to noise, false positives, and complex attack patterns
Solution Approach 1:
The patent transforms heterogeneous event data into a unified vector representation space, changing the data representation parameters from raw structured events to fixed-length vectors. This enables efficient similarity search and pattern recognition across different event types while maintaining adaptability to diverse security events.
Solution Approach 2:
The patent introduces vector embeddings as an intermediary representation layer between raw event data and security analysis operations. This intermediary transformation consolidates heterogeneous event information into a common representation format, enabling efficient processing while preserving the ability to handle diverse event types.
2Measurement precision
If detailed analysis of individual security events is performed, then detection precision improves, but the time required for analysis increases due to the volume and complexity of event data
Solution Approach 1:
The patent segments the analysis process into two stages: (1) transforming individual events into vector representations, and (2) performing efficient similarity search and pattern recognition in the vector space. This segmentation enables detailed analysis of individual events while reducing overall analysis time through efficient vector-based processing.
Solution Approach 2:
The patent transforms event data from high-dimensional structured formats into fixed-length vector representations. This dimensional transformation enables efficient similarity computation and pattern recognition while reducing the computational burden of analyzing large volumes of detailed event data.
3Reliability
If comprehensive event data collection from multiple sources is implemented, then the holistic view of security posture improves, but computational overhead and memory requirements increase
Solution Approach 1:
The patent extracts essential information from comprehensive event data and encodes it into compact vector representations. This extraction process preserves the holistic security view by capturing meaningful patterns and relationships while significantly reducing computational overhead and memory requirements compared to storing and processing raw event data.
Solution Approach 2:
The patent changes the data representation parameters from detailed structured event records to compressed vector embeddings. This parameter transformation maintains the comprehensive security perspective while reducing the computational resources required for storage and analysis.
Data Source
AI summary
The present disclosure relates to the analysis of computer system and network events for security applications. Some implementations relate to the generation of fixed-length embedding vectors to facilitate efficient analysis of large sets of data collected from monitoring tools such as extended detection and response (XDR) tools and endpoint detection and response (EDR) tools.