Bayesian Models for Network Credential Anomaly Detection
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Solution Overview
Problem
Current cybersecurity solutions inadequately address the detection of user credential theft and misuse within networks, particularly failing to effectively identify anomalous behavior through statistical modeling of event logs.
Innovation Solution
The implementation of Bayesian statistical models derived from computing system event logs to independently analyze time series of user credentials and machine events, flagging anomalous behavior for potential security incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based approaches are used to detect security incidents, then detection capability is provided, but false alarms increase and detection precision deteriorates
Solution Approach 1:
The patent replaces rule-based mechanical detection systems with statistical modeling approaches. Instead of using fixed rules to detect security incidents, the system employs Bayesian statistical models to analyze event log patterns and identify anomalies, thereby improving detection precision while maintaining reliability
Solution Approach 2:
The patent changes the parameters of detection by transitioning from binary rule-based detection to probabilistic statistical modeling. By using p-values and confidence intervals from Bayesian models, the system dynamically adjusts detection thresholds based on learned normal behavior patterns, reducing false alarms while improving anomaly detection precision
2Ease of operation
If single sign-on is implemented to improve user experience, then authentication convenience is improved, but credential security deteriorates as attackers can easily recover credentials from memory
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring authentication events and updating behavioral models in real-time. The system learns normal authentication patterns for each user and provides feedback when deviations occur, enabling dynamic security responses that maintain convenience while detecting credential misuse
Solution Approach 2:
The patent introduces statistical behavioral models as intermediaries between authentication systems and security detection. These models analyze event log patterns and serve as a layer that detects credential theft without interfering with normal single sign-on operations, thereby maintaining user convenience while improving security
3Reliability
If comprehensive event log analysis is performed to improve security detection, then detection capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by analyzing event logs independently for each user credential separately. This divides the complex task of analyzing all network events into manageable per-user segments, reducing computational complexity while maintaining comprehensive detection capability through individualized behavioral models
Data Source
AI summary
A framework is provided for modeling the activity surrounding user credentials and/or machine level activity on a computer network using computer event logs by viewing the logs attributed to each user as a multivariate data stream. The methodology performs well in detecting compromised user credentials at a very low false positive rate. Such a methodology may detect both users of compromised credentials by external actors and otherwise authorized users who have begun engaging in malicious activity.