Genetic Algorithm SOC Rule Customization
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Solution Overview
Problem
The customization of Security Operations Center (SOC) rules is labor-intensive and requires skilled analysts, often leading to false positives and false negatives due to the need for manual customization of default rules, which are not tailored to specific organization-system-architectures.
Innovation Solution
The use of genetic techniques to automatically generate customized SOC rule sets by applying a fitness function that maximizes sub-rule deviation, coverage, simplicity, and accuracy, with mutation and crossover methods, and harvesting IP addresses and user names to refine whitelists and blacklists, ensuring 'availability-friendly' responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual customization of SOC rules is performed, then rules can be tailored to specific organization-system-architecture, but the process becomes labor intensive and requires highly skilled security analysts
Solution Approach 1:
The system performs self-service by automatically generating customized SOC rules through genetic algorithms without requiring manual intervention from security analysts. The automated rule generation process uses fitness functions to evaluate and select rules, mutates them to adapt to specific organization architectures, and eliminates the need for human expertise in rule customization.
Solution Approach 2:
The patent replaces the mechanical manual customization process with an automated computational system. Instead of security analysts manually creating and tuning rules, the system uses genetic algorithms, fitness functions, and automated mutation operations to generate optimized rules, substituting human cognitive work with computational processes.
2Productivity
If default SOC rules are used without customization, then rule generation becomes simple and fast, but false positives and false negatives increase due to lack of organization-specific tailoring
Solution Approach 1:
The system changes parameters of the default rules through automated mutation processes. It modifies rule thresholds, time windows, and detection criteria based on fitness function evaluations against organization-specific datasets, transforming generic rules into optimized rules that balance speed and accuracy for the specific organization's architecture.
Solution Approach 2:
The patent implements feedback mechanisms where the fitness function continuously evaluates rule performance against labeled security event data. The system uses this feedback to select, mutate, and refine rules iteratively, ensuring that detection accuracy improves while maintaining generation speed through automated optimization cycles.
3Reliability
If highly customized SOC rules are created, then detection accuracy improves, but the complexity of rule management and maintenance increases
Solution Approach 1:
The system segments the rule generation process into modular components: fitness function evaluation, mutation operations, selection mechanisms, and rule optimization. This segmentation allows complex customized rules to be generated through systematic combination of simpler rule elements, making the overall rule set more manageable despite high detection accuracy.
Data Source
AI summary
Embodiments may provide techniques that that may automatically generate a customized SOC rule set for an organization. For example, in an embodiment, a method may be implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise simulating operation of a security incident and event management system by running a plurality of rules of the system on labeled data, determining fitness metrics of the plurality of rules, selecting at least one rule of the plurality of rules based on the determined fitness metrics; modifying the selected rule to form an updated rule, and repeating running the updated rule on the labeled data, determining fitness metrics of the updated rule, and mutating the updated rule.