Adaptive Behavior Profiling for Anomaly Detection
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Solution Overview
Problem
Current security systems face challenges in effectively assessing the risk of fraudulent access requests by relying solely on traditional methods, such as IP addresses and malware detection, which do not adaptively learn and update user behavior profiles in real-time, leading to potential false positives or negatives in detecting anomalous behavior.
Innovation Solution
A computer-implemented method that calculates a newness score for access requests, aggregates scores to generate a behavior profile, and updates it continuously using machine learning, enabling adaptive profiling and anomaly scoring to assess the risk of access requests by comparing current requests against historical contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security methods (IP address, malware detection) are used to assess risk, then the system is simple to implement, but the accuracy of detecting fraudulent requests is insufficient
Solution Approach 1:
The patent transforms static security parameters (IP address, malware presence) into dynamic behavioral parameters by continuously monitoring and scoring user actions. The system calculates newness scores based on deviations from established behavior patterns, enabling adaptive detection that improves accuracy without requiring complete system redesign
Solution Approach 2:
The system implements continuous feedback loops where user behavior is monitored, scored, and used to update behavior profiles in real-time. The newness score mechanism provides immediate feedback on anomalous actions, allowing the system to adapt and improve detection accuracy dynamically based on observed patterns
2Adaptability or versatility
If static behavior profiles are used, then the system is computationally efficient, but the system cannot adapt to changing user behavior patterns
Solution Approach 1:
The patent transitions from static behavior profiles to dynamic, continuously updating profiles. The system calculates newness scores that reflect real-time deviations from baseline behavior, allowing the profiles to adapt to changing user patterns while maintaining computational efficiency through incremental updates rather than complete recalculations
Solution Approach 2:
The system performs partial updates to behavior profiles by only recalculating scores for specific users or behavior patterns that show anomalies. Rather than continuously updating all profiles system-wide, the newness score mechanism triggers selective updates only where needed, reducing overall computational resource consumption
3Measurement precision
If continuous monitoring of all user actions is performed, then the detection accuracy improves, but the processing time and system load increase
Solution Approach 1:
The system monitors all user actions but applies full analytical processing only to actions that exceed thresholds or show anomalous patterns. The newness score mechanism filters out normal, expected behaviors from intensive processing, focusing computational efforts only on potentially fraudulent actions while maintaining comprehensive monitoring coverage
Solution Approach 2:
The patent segments the monitoring system into multiple scoring layers: immediate newness score calculation for all actions, followed by deeper behavioral analysis only for actions exceeding certain thresholds. This segmentation allows continuous monitoring of all user actions while applying intensive processing only where necessary, balancing detection accuracy with processing time
Data Source
AI summary
Generating a behavior profile is provided. A newness score is calculated for a data point corresponding to a context of an access request to a resource made by a user of a client device. Newness scores for a plurality of data points corresponding to contexts of a plurality of access requests are aggregated to form an aggregated newness score. In response to determining that the aggregated newness score is greater than or equal to a pre-defined newness score threshold, data points stored in a data point cache and a long-term storage are used to generate a new behavior profile for the user or update an existing behavior profile for the user.