Insider Threat Detection Using Markov Models and Reinforcement Learning
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Solution Overview
Problem
Existing security systems are inadequate in detecting and mitigating insider threats proactively, often reacting only after anomalous behavior has occurred, failing to predict potential future threats and requiring extensive resources for investigation and remediation.
Innovation Solution
A method utilizing a Markov model and reinforcement learning (RL) to predict an employee's next action based on historical data and attributes, adjusting threat mitigation controls to proactively identify and manage potential insider threats, incorporating a visualization dashboard for investigation and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems are used to detect anomalous behavior, then some threats can be identified, but the system cannot predict future threats and only detects after behavior has occurred
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical employee data to predict future anomalous behavior patterns. The Markov model pre-calculates transition probabilities between behavioral states, and the reinforcement learning model pre-identifies high-risk employees before they actually commit insider threats, enabling proactive rather than reactive detection.
Solution Approach 2:
The system implements feedback mechanisms where detected anomalous behaviors and insider threats are fed back into the training data for continuous model improvement. The reinforcement learning model receives feedback from actual threat incidents to refine its predictions, and the system continuously monitors prediction accuracy to adjust detection thresholds and improve future detection reliability.
2Reliability
If comprehensive employee monitoring is implemented to improve threat detection, then detection capability increases, but resource consumption and investigation costs increase
Solution Approach 1:
Instead of uniformly monitoring all employees equally, the system applies local quality by using machine learning models to identify and focus monitoring resources on specific high-risk employees or departments. The reinforcement learning model assigns different risk scores to different employees, allowing the organization to concentrate investigation resources where the probability of insider threats is highest, rather than spreading resources thin across the entire organization.
Solution Approach 2:
The system changes parameters by dynamically adjusting monitoring intensity and detection thresholds based on predicted risk levels. Employees with higher predicted risk scores trigger more intensive monitoring and lower detection thresholds, while low-risk employees receive standard monitoring. This parameter-based approach optimizes the balance between detection capability and resource consumption.
Data Source
AI summary
Insider threats to a company can be detected and possibly mitigated by: receiving employee activity data comprising one or more activities or alerts each associated with a respective employee of a plurality of employees. The activities or alerts can be applied to a respective Markov model transition matrix to determine a next possible action of the employee. The one or more activities or alerts for the employee may also be applied to a reinforcement learning model to predict an employee risk that the employee may be an insider threat. Based on at least one of the determined next possible action and the predicted employee risk, threat mitigation controls, such as monitoring or controlling employee access to systems, can be adjusted.