Probabilistic Insider Threat Detection via Dynamic Behavior Learning
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Solution Overview
Problem
Existing insider threat detection systems are inflexible, require pre-defined rules and models, and struggle to adapt to uncertainties and fluctuations in normal behavior, leading to false alarms and inability to detect unknown threats.
Innovation Solution
A probabilistic programming-based system that learns normal behavior patterns automatically, adapts to changes, and computes anomaly scores without pre-defined rules, using dynamic models to identify deviations in user activities across multiple attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined rules and models are used for insider threat detection, then the detection process is simple and straightforward, but the system cannot adapt to uncertainties and fluctuations in normal behavior, leading to false alarms and inability to detect unknown threats
Solution Approach 1:
The patent implements dynamic probabilistic programming models that continuously learn and adapt to changing normal behavior patterns. The system transitions from static pre-defined rules to dynamic models that evolve with organizational behavior, allowing it to accommodate fluctuations and uncertainties in user activities while maintaining detection effectiveness.
Solution Approach 2:
The system automatically learns and adjusts probability distributions and parameters from observed behavior data. Instead of using fixed thresholds and rules, the model dynamically updates its understanding of normal behavior by changing its internal parameters based on accumulated evidence, enabling adaptation to new patterns without manual reconfiguration.
2Extent of automation
If pre-defined rules and models are used for insider threat detection, then the deployment process is straightforward, but the system requires manual configuration and cannot learn organizational-specific patterns automatically
Solution Approach 1:
The system performs self-configuration by automatically learning organizational behavior patterns from observed data. The probabilistic programming model autonomously adapts to the specific organizational context without requiring manual setup or customization, enabling the organization to deploy the system immediately and have it learn its unique patterns over time.
Solution Approach 2:
The system is designed with pre-configured probabilistic programming frameworks and detection methodologies that are ready to deploy. These preliminary configurations include the structural model for behavior analysis, which then automatically populates organization-specific parameters through learning, eliminating the need for time-consuming custom model building.
3Stability of the object's composition
If static Bayesian networks are used for insider threat detection, then the model structure is stable and easy to maintain, but the model cannot reflect fluctuations and changes in normal activities
Solution Approach 1:
The patent transforms the static Bayesian network into a dynamic probabilistic programming model that continuously updates its probability distributions and parameters. The model maintains structural stability through its Bayesian framework while allowing its internal representations of normal behavior to evolve dynamically, reflecting both model stability and behavioral adaptability.
Solution Approach 2:
The system implements continuous feedback loops where observed behavior data is constantly fed back into the probabilistic model to update its understanding of normal patterns. This feedback mechanism allows the model to maintain stability through controlled adaptation, adjusting its parameters based on accumulated evidence while preserving the core detection framework.
Data Source
AI summary
The main enabler of the technology is the probabilistic programming based computation strategies that have an outstanding ability to handle uncertainties in discovering anomalous patterns. This new capability to handle uncertainties is particularly relevant for detecting insider threats, as there are usually no clearly defined rules and policies. An insider's behaviors would deviate from normally distributed events and actions in various aspects. The probabilistic model detects this deviation and explains the computational path of the deviation. The result interpretation unit enables organizations to investigate the causes with high accuracy, avoiding costly and embarrassing false alarms. The technology detects and ranks abnormal events. This ranking capability helps prioritize follow-up manual investigation.