User Behavior Analyzer for Anomaly Detection
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Solution Overview
Problem
Existing systems fail to effectively identify abnormal client behavior in communications between client devices and servers, particularly in detecting cheating or excessive spending, as they rely on signature-based approaches that are limited by known patterns and cannot detect stealthy or variant behaviors.
Innovation Solution
A user behavior analyzer system that uses a learn module to record normal client behavior patterns and time intervals, and a detect module to compare real-time sequences against these records, flagging deviations beyond a predetermined threshold as abnormal behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based approaches are used to detect abnormal behavior, then detection can be performed using known patterns, but the system cannot detect stealthy or variant behaviors and has high false negative rates
Solution Approach 1:
The system performs preliminary learning of normal behavior patterns during a training phase before actual detection begins. The learn module collects and analyzes communication data to establish baseline sequences and time intervals of normal client behavior, which are then used as reference for detecting anomalies. This preliminary characterization enables the system to identify deviations from normal patterns without relying on pre-defined signatures.
Solution Approach 2:
The system continuously compares real-time communication sequences against the learned normal behavior patterns and provides feedback when deviations are detected. The detect module monitors incoming messages, compares them against stored sequences and time intervals, and identifies abnormal behavior when discrepancies exceed predetermined thresholds. This feedback mechanism enables adaptive detection of both known and unknown anomaly types.
2Measurement precision
If comprehensive behavior analysis is performed to improve detection accuracy, then more abnormal behaviors can be identified, but system complexity and computational resources increase
Solution Approach 1:
The system segments the behavior analysis process into distinct functional modules: the learn module for collecting and analyzing normal behavior patterns, and the detect module for comparing real-time communications against learned patterns. Each module handles specific aspects of the analysis independently, processing message sequences and time intervals separately. This segmentation reduces overall system complexity while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The system creates simplified representations or copies of normal behavior patterns during the learning phase, storing characteristic sequences and time intervals as reference models. Instead of analyzing all communication data in real-time, the system compares incoming messages against these pre-computed copies of normal behavior. This copying approach enables precise anomaly detection with reduced computational complexity during the detection phase.
Data Source
AI summary
A system and method is shown for identifying abnormal client behavior with respect to communications between one or more servers and one or more client devices communicatively coupled to the one or more servers. Messages are received at a server from one or more client devices communicatively coupled to the server. The plurality of messages are grouped into subsets of messages using a learn module of the server. Each subset of messages is associated with a unique client identifier, and all messages within a subset are associated with the same unique client identifier. Each message within a subset of messages is identified as belonging to a defined type of message. Sequences of the defined types of messages within each of said subsets of messages are recorded using the learn module. Time intervals between the defined types of messages are measured using the learn module. The recorded sequences of defined types of messages and the measured time intervals between the defined types of messages are designated as constituting normal client behavior.