Bayesian Network for Cyber Threat Detection Correlation
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Solution Overview
Problem
Existing cybersecurity systems face challenges in effectively detecting cyber threats due to high false alarm rates and the inability to detect new attack techniques, leading to alert fatigue and undetected threats.
Innovation Solution
A system and method utilizing a Bayesian network to combine threat detections from multiple sources, incorporating user properties, threat intelligence, and expert knowledge to generate a probabilistic score for identifying potential threats, allowing for a tradeoff between detection and false alarm probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple threat detectors are deployed to improve detection coverage, then the detection rate improves, but the false alarm rate increases
Solution Approach 1:
The patent combines multiple threat detectors into a unified system that processes detections collectively. The Bayesian network integrates outputs from multiple detectors, correlating their findings to distinguish true threats from false alarms. This merging approach maintains high detection coverage while reducing false positives through probabilistic reasoning.
Solution Approach 2:
The Bayesian network acts as an intermediary between individual threat detectors and the final alerting system. It processes and correlates detector outputs, using probabilistic models to determine whether multiple detections represent a genuine threat or coincidental false alarms. This intermediary layer resolves the contradiction by filtering combined detector signals.
2Ease of manufacture
If traditional rule-based systems are used to detect threats, then implementation is simple, but the system cannot detect new attack techniques
Solution Approach 1:
The system transitions from fixed rule-based parameters to dynamic probabilistic parameters. The Bayesian network uses probability values that can be updated as new threat intelligence becomes available, allowing the system to adapt to new attack techniques while maintaining a structured analytical framework. This parameter flexibility enables detection of novel threats without complete system redesign.
Solution Approach 2:
The patent implements a dynamic detection system where the Bayesian network continuously updates probability assessments based on new detections and threat intelligence. Unlike static rule-based systems, this dynamic approach allows the system to evolve its detection capabilities, adapting to new attack patterns while maintaining operational simplicity through automated probabilistic reasoning.
3Reliability
If threat detectors process large quantities of threat data, then detection completeness improves, but processing complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: individual threat detectors process specific threat types independently, then their outputs are fed into the Bayesian network for correlated analysis. This segmentation allows comprehensive processing of large threat data volumes while managing complexity through modular architecture and probabilistic aggregation.
Data Source
AI summary
A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory and configured to detect triggering of one or more threat detectors. The at least one processor is further configured to activate a subset of nodes from a plurality of nodes in a Bayesian network in response to the detection, the activated subset of nodes associated with the triggered threat detectors. The at least one processor is further configured to calculate a probability of malicious action using the Bayesian network to combine probabilities associated with the activated subset of nodes. The at least one processor is further configured to determine that the probability exceeds a threshold value. The at least one processor is further configured to perform a security action in response to the determination.