Automated Rule-Based Detection Strategy Selection
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Solution Overview
Problem
Current rule-based strategies for identifying bad actors and attacks are resource-intensive, inefficient, and often untargeted, as they require significant human effort and time to understand emerging attack vectors, leading to suboptimal detection solutions.
Innovation Solution
A system utilizing a machine learning model to generate strategy trees with rule-based strategies, ranked by a precision-recall-stability (PRS) score, which automatically selects and deploys the most effective strategies for detecting malicious interactions, reducing reliance on human operators and improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators manually identify and create rule-based strategies for detecting attacks, then the strategies can be tailored to specific attack vectors, but the process requires significant time and resource investment and produces suboptimal detection solutions
Solution Approach 1:
The system enables automated self-service by allowing the data and algorithms to automatically generate, optimize, and deploy detection rules without human intervention. The rule generation process is autonomous, taking security events and interaction data to automatically create optimized detection strategies that improve both accuracy and efficiency simultaneously
Solution Approach 2:
The patent replaces the mechanical human operator process with an automated computational system. Instead of human analysts manually analyzing attack vectors and creating rules, machine learning algorithms and automated processing systems perform the same function with superior speed and accuracy, eliminating the trade-off between precision and time investment
2Productivity
If human operators create rule-based strategies without complete understanding of attack mechanisms, then the detection strategies may be less than optimal, but manual creation is still resource-intensive
Solution Approach 1:
The system implements continuous feedback loops where detection results, false positives, and emerging attack patterns are automatically fed back into the rule optimization process. This allows the system to learn from its performance and continuously improve detection effectiveness while maintaining high productivity through automated iteration and refinement
Solution Approach 2:
The system performs preliminary analysis and rule generation automatically before human operators would need to intervene. By pre-processing security data and generating initial detection rules autonomously, the system establishes a foundation of effective detection strategies that can be further refined without manual resource investment
Data Source
AI summary
Systems and methods for generating, selecting, and implementing rule-based strategies are disclosed. An input data set representing a plurality of interactions that may be classified as malicious or non-malicious is received and at least one strategy tree including a plurality of rule-based strategies is generated. The at least one strategy trees is generated by a machine learning model configured to generate a tree structure. The rule-based strategies are ranked based on a precision-recall-stability (PRS) score generated for each of the rule-based strategies and at least a first rule-based strategy having a highest PRS score is extracted. One or more interactions are evaluated using the first rule-based strategy to determine when the one or more interactions are malicious.