Bayesian Threat Detection With Administrator Feedback to Cut False Alarms
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Solution Overview
Problem
Existing security monitoring tools struggle with high false alarm rates and poor detection rates in cyber threat detection, failing to effectively manage the increasing number of threats and new attack techniques.
Innovation Solution
A Bayesian network model is used to probabilistically combine threat detections, user behavior signals, threat intelligence, and administrator feedback to generate a threat score, allowing for informed decision-making on potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security monitoring tools are used to detect cyber threats, then threat detection capability is provided, but false alarm rate increases and detection accuracy deteriorates
Solution Approach 1:
The patent combines multiple threat detection tools and their outputs into a unified Bayesian network model. This integration allows the system to aggregate evidence from multiple sources, cross-validate findings, and reduce false alarms through probabilistic reasoning that considers the combined likelihood of threats across different detection tools.
Solution Approach 2:
The system incorporates administrator feedback loops where security professionals can validate or refute threat detections. This feedback is used to update the Bayesian network probabilities, improving the system's ability to distinguish true threats from false alarms over time through continuous learning and adaptation.
2Adaptability or versatility
If multiple threat detection tools are deployed to improve coverage, then detection capability increases, but system complexity increases
Solution Approach 1:
The Bayesian network serves as an intermediary layer that standardizes and harmonizes outputs from multiple diverse threat detection tools. It provides a common probabilistic framework that translates various detection formats into unified threat assessments, managing complexity by abstracting away the heterogeneity of individual tools through a standardized mathematical model.
Solution Approach 2:
The system dynamically adjusts probability parameters within the Bayesian network based on incoming evidence and feedback. By changing the probabilistic parameters rather than the structural complexity, the system adapts to new threats and tool performances without requiring fundamental architectural changes, thus managing complexity while maintaining versatility.
3Ease of manufacture
If traditional threat detection methods are used, then simple implementation is maintained, but detection rate deteriorates面对increasing threats
Solution Approach 1:
The patent replaces traditional rule-based or signature-based mechanical detection methods with a probabilistic Bayesian network system. This substitution enables the system to handle increasing complexity of modern threats through mathematical probability theory, improving detection rates while maintaining implementation feasibility through the use of standardized probabilistic algorithms and modular architecture.
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 and activate a subset of nodes associated with the triggered threat detectors from a plurality of nodes in a Bayesian network in response to the detection. The at least one processor is further configured to determine that feedback associated with the triggered threat detectors is available and, if so, accumulate the feedback to a feedback node of the network, the feedback node associated with the triggered threat detectors. The at least one processor is further configured to calculate a probability of malicious action using the network to combine probabilities associated with the activated subset of nodes and the feedback node, determine that the probability exceeds a threshold value, and perform a security action in response to the determination.