Host Behavior Anomaly Detection via Latent Space Embeddings
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Solution Overview
Problem
Enterprise networks face challenges in detecting anomalous behavior in hosts due to the complexity and dynamic nature of their systems, which existing monitoring methods struggle to address effectively.
Innovation Solution
A method and system for host-level anomaly detection that models a target host's behavior based on historical events and identifies peer hosts with similar behavior profiles by embedding events into a latent space, allowing for unsupervised anomaly scoring and security management actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect anomalous behavior in enterprise networks, then the monitoring process is simple to implement, but the detection precision and ability to identify subtle anomalies is insufficient
Solution Approach 1:
The patent introduces latent space embeddings as an intermediary representation layer between raw host behavior events and anomaly detection. Historical events are transformed into embedded vectors in a latent space, where semantic similarities between different types of events are captured. This intermediary representation enables more precise anomaly detection by comparing behavioral patterns in the embedded space rather than raw event data, resolving the contradiction between detection precision and system complexity.
Solution Approach 2:
The patent transforms host behavior data from traditional monitoring parameters into embedding vectors in a latent space. By changing the parameter representation from discrete event logs to continuous vector embeddings, the system achieves higher detection precision while managing complexity through learned representations that capture behavioral semantics automatically.
2Measurement precision
If behavior-based community detection is implemented to identify peer hosts, then the anomaly detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary embedding of historical events into latent space representations during offline processing. By pre-computing the embedding space and storing peer host relationships in advance, the system reduces online processing time while maintaining high detection accuracy. The actual anomaly detection then becomes a comparison operation in the pre-established embedding space rather than complex real-time analysis.
3Reliability
If detailed host-level behavior analysis is performed on all hosts, then the detection capability improves, but the energy consumption and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential behavioral features of hosts and embeds them into a compact latent space representation. By taking out and representing only the critical behavioral characteristics rather than analyzing all raw events in detail, the system maintains high detection reliability while significantly reducing computational energy consumption. The embedding process captures the essential behavioral patterns in a compressed form.
Data Source
AI summary
Methods and systems for detecting host community include modeling a target host's behavior based on historical events recorded at the target host. One or more original peer hosts having behavior similar to the target host's behavior are found by determining a distance in a latent space that embeds the historical events between events of the target host and events of the one or more original peer hosts. A security management action is performed based on behavior of the target host and the determined one or more original peer hosts.