Clustering-Based Insider Threat Detection
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Solution Overview
Problem
Current approaches for detecting malicious user behavior in networks, such as rule-based, statistical, and machine learning methods, are inflexible and prone to generating false positive alerts, failing to adapt to new technologies and practices, and unable to effectively identify anomalous behavior in peer groups of users.
Innovation Solution
A computer-implemented method that logs user activity, converts it into numerical representations, and uses clustering processes to identify peer groups of users with similar activity patterns within defined time intervals, detecting anomalous behavior by analyzing changes within these groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based approaches are used to identify malicious user behavior, then known patterns can be detected, but the system is rigid and unable to adapt to new technologies and practices
Solution Approach 1:
The system dynamically adapts by continuously learning from user behavior patterns and automatically updating its detection models. Instead of using static rule-based approaches, the system employs machine learning algorithms that evolve with new technologies and practices, allowing it to maintain high detection accuracy while adapting to emerging threats.
Solution Approach 2:
The system changes its detection parameters adaptively by adjusting detection thresholds, model weights, and analysis criteria based on learned patterns from historical data. This allows the system to optimize its sensitivity and specificity dynamically, improving reliability while accommodating new behavior patterns without requiring manual rule updates.
2Measurement precision
If statistical and machine learning approaches are used to detect behavior shifts, then individual user behavior changes can be identified, but a large number of false positive alerts are generated
Solution Approach 1:
The system merges multiple detection approaches by combining statistical analysis with machine learning models and contextual information. By integrating these different methods, the system achieves more precise behavior pattern detection while reducing false positives through cross-validation and ensemble decision-making.
Solution Approach 2:
The system applies different detection strategies and thresholds to different user groups, time periods, and activity types. By customizing detection parameters locally rather than applying uniform rules globally, the system improves measurement precision for specific contexts while reducing false positives that arise from one-size-fits-all approaches.
3Reliability
If traditional detection methods are used, then individual user behavior can be monitored, but the system cannot effectively identify anomalous behavior in peer groups of users
Solution Approach 1:
The system implements a multi-functional detection framework that can simultaneously analyze individual user behavior and peer group patterns. The same underlying infrastructure supports both granular individual monitoring and aggregate group analysis, making the system versatile enough to handle multiple detection objectives without requiring separate systems.
Solution Approach 2:
The system adds a new dimension of analysis by examining user behavior in the context of peer groups and organizational hierarchies. Instead of only analyzing individual users in isolation, the system incorporates group-level patterns and relationships, enabling detection of anomalous behavior that manifests at the collective level rather than the individual level.
Data Source
AI summary
Detecting anomalous user behavior is provided. User activity is logged for a set of users. The user activity is divided into distinct time intervals. For each distinct time interval, logged user activity is converted to a numerical representation of each user's activities for that distinct time interval. A clustering process is used on the numerical representations of user activities to determine which users have similar activity patterns in each distinct time interval. A plurality of peer groups of users is generated based on determining the similar activity patterns in each distinct time interval. Anomalous user behavior is detected based on a user activity change in a respective peer group of users within a distinct time interval.