Graph Incident Thumbprints for Scalable Threat Attribution
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Solution Overview
Problem
Existing network security systems face challenges in managing large volumes of security alerts, often resulting in overwhelming human analysts and unnecessary machine actions due to false positives, which can distract from real threats and leave networks vulnerable.
Innovation Solution
A system that represents security incidents as graphs, using graph embeddings to generate thumbprint representations, enabling automated threat attribution and mitigation by leveraging contextual data and known threat patterns, and facilitating automated responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If security alerts are aggregated and clustered into security incidents, then the number of incidents is reduced from tens or hundreds of thousands to hundreds or thousands per day, but the incidents still consume substantial machine resources and overwhelm human analysts
Solution Approach 1:
The patent creates simplified copies of security incidents in the form of graph embeddings—compact vector representations that capture the essential characteristics of complex security incidents. These embeddings serve as lightweight proxies that enable efficient comparison, clustering, and analysis without requiring full processing of the original incident data, thereby reducing computational resources while maintaining analytical capability.
Solution Approach 2:
The patent transforms security incidents from complex, high-dimensional data structures into fixed-length vector representations (embeddings) with controlled dimensionality. This parameter transformation converts unmanageable quantities of incident data into a standardized format that can be efficiently processed, stored, and compared, directly addressing the resource consumption problem while maintaining the essential information needed for security analysis.
2Measurement precision
If human analysts review security alerts manually, then detailed assessment is possible, but the volume of alerts (tens or hundreds of thousands per day) exceeds human handling capacity
Solution Approach 1:
The patent performs preliminary grouping and embedding of security incidents before human analysis. By pre-processing incidents into graph embeddings and organizing them into clusters, the system prepares the data in advance, so that when human analysts do review incidents, they are working with pre-organized, condensed information rather than raw alert volumes, significantly reducing the time required for manual assessment while maintaining accuracy.
Solution Approach 2:
The patent introduces graph embeddings as an intermediary representation between raw security alerts and human analysis. These embeddings serve as a bridge that condenses complex incident data into manageable vector forms, allowing automated systems to perform initial processing and filtering, thereby reducing the burden on human analysts while preserving the essential information needed for accurate threat assessment.
3Reliability
If security incidents are analyzed in detail, then false positives can be identified, but the analysis process consumes substantial machine resources
Solution Approach 1:
The patent segments the analysis process into multiple stages: initial embedding generation, clustering-based grouping, and then targeted detailed analysis of specific clusters. This segmentation allows the system to perform lightweight operations on all incidents (embedding and clustering) and reserve resource-intensive detailed analysis only for incidents that require it, thereby reducing overall machine resource consumption while maintaining the ability to identify false positives through selective deep analysis.
Data Source
AI summary
In network security systems, graph-based techniques may be employed to generate “thumbprints” of security incidents, which may thereafter be used, e.g., for threat actor attribution or the identification of similar incidents. In various embodiments, each security incident is represented by a graph in which security events correspond to nodes, and which encodes associated metadata in additional nodes and/or node/edge attributes. Graph representation learning may be used to compute node and/or edge embeddings, which can then be aggregated into the thumbprint of the incident.