Hierarchical Behavioral Profiling for Anomaly Detection
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Solution Overview
Problem
Current anomaly detection systems face inefficiencies due to incorrect partitioning of information, leading to loss of valuable data and inaccuracies in identifying system anomalies, such as security breaches or hardware faults.
Innovation Solution
The implementation of a hierarchical behavioral profiling system that analyzes events using a set of hierarchical behavioral profiles, allowing for multi-dimensional partitioning and retention of information, which distinguishes between anomalous and normal behavior by comparing events to aggregate system-wide metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single behavioral profile is used for anomaly detection, then the system complexity is reduced, but information loss occurs and detection accuracy decreases
Solution Approach 1:
The patent segments the single behavioral profile into multiple hierarchical profiles organized in a tree structure. Each profile corresponds to a specific partitioning dimension (e.g., user, device, location, time), allowing the system to analyze events at different granularities simultaneously. This segmentation preserves information that would be lost in a single aggregated profile while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The patent introduces multiple dimensional perspectives for analyzing system events by creating profiles along different partitioning dimensions. Instead of a single flat profile, the system builds multi-dimensional profiles that capture behavior from various angles (user-level, device-level, location-level, time-level), thereby preserving information across dimensions without proportionally increasing complexity.
2Measurement precision
If multiple hierarchical behavioral profiles are used for anomaly detection, then detection accuracy improves by retaining valuable information, but system complexity increases
Solution Approach 1:
The patent segments the anomaly detection task across multiple hierarchical profiles organized in a tree structure. Each profile focuses on a specific partitioning dimension, allowing precise analysis at appropriate granularities. This segmentation improves detection accuracy by preserving dimension-specific information while managing complexity through modular, hierarchical organization of profiles.
Solution Approach 2:
The patent applies local quality by assigning different levels of detail and analysis depth to different parts of the profile hierarchy. Leaf-node profiles provide fine-grained local analysis for specific users or devices, while parent profiles provide broader contextual analysis. This localized approach improves detection precision where needed without uniformly increasing complexity across the entire system.
3Reliability
If events are monitored and recorded during a learning period to generate behavioral profiles, then anomaly detection capability is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary action by pre-processing and partitioning events during the learning period across multiple dimensional profiles. This advance organization of data into hierarchical structures enables faster anomaly detection during operation, as the system can query pre-computed profiles rather than processing raw events in real-time, thereby reducing detection time while maintaining reliability.
Solution Approach 2:
The patent segments the learning and detection process across multiple hierarchical profiles, allowing parallel processing of different event dimensions. By organizing learning data into user-level, device-level, location-level, and time-level profiles simultaneously, the system accelerates the learning process and enables efficient querying during anomaly detection, reducing overall time consumption.
Data Source
AI summary
The flow of events though an event-analysis system is controlled by a number of event throttles which filter events, prioritize events and control the rate at which events are provided to event-processing components of the event-analysis system. Incoming events to the event-analysis system are associated with a profile, and a metrics engine generates metrics based on the incoming events for each profile. The flow of events to the metrics engine is controlled on a per profile basis, so that excessive generation of new metrics and new profiles is limited. If the system from which the events originate is compromised, metrics associated with compromised profiles may be frozen to avoid corrupting existing metrics. Processing of events and anomalies by analysis engines within the event-analysis system may be delayed to allow the accumulation of metrics necessary for accurate analysis.