Anomaly Detection System Using Behavioral Baseline Comparison
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Solution Overview
Problem
Traditional access control systems fail to detect abnormal behavior by authorized users, especially when accessing sensitive information from non-traditional locations, as they rely solely on credentials and do not differentiate between authentic and forged access.
Innovation Solution
A system that breaks down information access requests into basic elements and uses bitmap tables and counters to model historical behavior, comparing these elements against a database to issue alerts when access patterns exceed thresholds, allowing for the detection of anomalous behavior and adjusting alerts based on user patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional access control systems use credentials and passwords to control access, then access security is maintained through authentication, but the system cannot detect abnormal behavior by authorized users accessing information from non-traditional locations
Solution Approach 1:
The patent segments the access control system into multiple independent monitoring components: credential verification module, behavior analysis module, and alert generation module. Each module independently analyzes specific aspects of access behavior (authentication, location, timing, pattern) and feeds findings to the central decision-making component, enabling comprehensive security without over-relying on any single authentication mechanism
Solution Approach 2:
The system continuously monitors access behavior and compares it against established user profiles and historical data, generating feedback signals that trigger alerts when abnormal patterns are detected. The feedback loop operates by: collecting access data, comparing against baseline behavior, detecting deviations, and generating alerts for manual review, thereby enabling dynamic adaptation to normal behavior changes while detecting anomalies
2Measurement precision
If the system monitors and analyzes information access requests in detail, then detection precision of abnormal behavior improves, but system complexity and processing overhead increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing user behavior profiles and access baseline data during the learning phase, before actual anomaly detection is needed. Historical access patterns are collected and stored as reference models, enabling rapid comparison against current access requests without requiring complex real-time analysis of every access event from scratch
Solution Approach 2:
The patent applies partial action by monitoring only the most critical access parameters (authentication method, location, time, data type) rather than analyzing every conceivable access detail. This selective monitoring approach achieves sufficient detection precision for security purposes while significantly reducing processing complexity and system overhead
Data Source
AI summary
A system and method for identifying anomalies in information requests. The information requests are modeled into a plurality of basic elements and association among the basic elements are tracked. The association of one information request is compared with a plurality of bitmap tables and counters representing a baseline information from a historical behavior information. If the association of this information request differs from the baseline information, an alert is issued. The system responds dynamically to changing baselines in assessment of which behaviors constitute an anomaly.