Contextual Anomaly Detection for Dynamic Actor Behavior
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Solution Overview
Problem
Existing anomaly detection techniques lack contextual awareness, fail to model temporal variations, operate as black-box models with limited interpretability, and do not adapt to real-time feedback, leading to false positives and undetected anomalies in dynamic environments.
Innovation Solution
A system and method for automated anomaly detection that identifies characteristics of entities accessed by actors, assigns task-specific ranks, contextualizes behavior over sessions, models context variations, predicts expected behavior, and detects anomalies based on deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional anomaly detection techniques use predefined thresholds or behavioral baselines, then the detection process is simple and fast, but the detection accuracy deteriorates due to lack of contextual awareness and inability to account for dynamic behavior
Solution Approach 1:
The system dynamically adapts behavioral baselines by continuously learning from historical data and updating contextual profiles. Instead of using static thresholds, the system adjusts detection parameters based on evolving actor behaviors, session contexts, and environmental factors, enabling accurate detection of anomalies in dynamic environments while maintaining operational efficiency
Solution Approach 2:
The patent replaces traditional mechanical threshold-based detection with an AI-driven contextual analysis system. Machine learning models analyze multi-dimensional behavioral patterns, session contexts, and entity relationships to dynamically determine anomaly significance, substituting rigid predefined rules with adaptive intelligent decision-making that improves accuracy without sacrificing speed
2Device complexity
If traditional techniques monitor isolated events or fixed sequences, then the system complexity is low, but the contextual awareness deteriorates leading to false positives or undetected anomalies
Solution Approach 1:
The system segments behavioral analysis into multiple contextual dimensions including actor profiles, session contexts, entity characteristics, and environmental factors. Each dimension is analyzed separately and then integrated to form a comprehensive contextual understanding, enabling the system to capture nuanced behavioral patterns without overwhelming system complexity
Solution Approach 2:
The patent implements nested contextual layers where actor behaviors are contextualized within sessions, sessions within time periods, and all within environmental contexts. This hierarchical nesting allows the system to maintain rich contextual information at multiple levels while organizing complexity in a manageable structured manner
3Productivity
If existing techniques treat each actor session in isolation or assume static references, then the processing is simpler and faster, but the ability to detect stealthy behavioral deviations deteriorates
Solution Approach 1:
The system maintains continuous behavioral monitoring across sessions by persisting contextual profiles and updating them incrementally. Instead of isolating sessions, the system continuously learns from ongoing actor behaviors, maintaining an ever-evolving baseline that enables detection of subtle deviations while processing each session efficiently without full re-analysis
Solution Approach 2:
The patent implements feedback mechanisms where detection results and contextual updates feed back into the learning system. Anomaly detections and normal behavior patterns both contribute to refining contextual profiles and adjusting detection thresholds, creating a self-improving system that increases reliability over time while maintaining processing efficiency through learned patterns
4Device complexity
If anomaly detection systems operate as black-box models, then the model complexity is reduced, but the interpretability and explainability deteriorate reducing operational trust
Solution Approach 1:
The system introduces contextual profiles and behavioral baselines as intermediary representations between raw data and anomaly decisions. These intermediaries provide interpretable explanations by showing how actual behaviors deviate from established contextual norms, bridging the gap between complex AI models and human-understandable reasoning without requiring model simplification
Data Source
AI summary
A system and method for automated anomaly detection is described. The method includes identifying inherent characteristics or tags associated with the one or more entities. The characteristics or tags may be ranked or contextualized based on one or more global factors or actor-based factors. The method further includes contextualize actor behaviour considered over a period of time or sessions. The method further includes measuring context changes and context overlaps and quantifying the dynamics of the actor behaviour using one or more Al/ML models. Further, the method includes performing dynamic patching and dynamically modeling the changes in actor behaviour over time in order to detect anomalies.