Detection Rule Verification Using Latent Dirichlet Allocation
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Solution Overview
Problem
Current network security systems rely heavily on professional expertise for generating and verifying network traffic detection rules, leading to inefficiencies, high false positive rates, and potential network overload, making it difficult for semi-skilled workers to rapidly and accurately verify detection rules.
Innovation Solution
An apparatus and method for verifying detection rules using a grammar checking unit, false-positive rate calculation unit, similarity checking unit, and overload prediction unit, which applies the latent Dirichlet allocation algorithm to signatures and normal traffic, enabling semi-skilled experts to correct and efficiently generate detection rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If professional experts manually generate and verify detection rules, then detection rule accuracy is improved, but verification time and cost increase significantly
Solution Approach 1:
The system enables self-service verification where the detection rule verification apparatus automatically checks detection rules against multiple criteria (grammar checking, false-positive rate calculation using latent Dirichlet allocation, similarity checking) without requiring manual expert verification for each rule, thereby reducing verification time while maintaining accuracy
Solution Approach 2:
The patent replaces manual expert verification with automated computational methods, including using algorithms (latent Dirichlet allocation) to calculate false-positive rates and automated similarity matching, substituting the mechanical process of manual review with automated digital processing
2Productivity
If detection rules are generated without proper verification, then verification speed is improved, but false positive rate increases
Solution Approach 1:
The system performs preliminary verification actions automatically when detection rules are generated, checking for grammatical errors, calculating false-positive rates using latent Dirichlet allocation algorithms, and comparing similarity with existing rules before the rules are deployed, thereby preventing false positives from entering the system
Solution Approach 2:
The verification apparatus provides feedback on detection rule quality by calculating false-positive rates and identifying grammatical errors, allowing the system to adjust and correct rules before they are activated, thereby maintaining high reliability while enabling rapid deployment
3Reliability
If comprehensive verification checks are performed, then detection rule reliability is improved, but system complexity increases
Solution Approach 1:
The verification system is segmented into distinct functional modules: grammar checking unit, false-positive rate calculation unit (using latent Dirichlet allocation), and similarity checking unit. Each module performs a specific verification function independently, making the overall complex verification process manageable and maintainable
Data Source
AI summary
Disclosed herein are an apparatus and method for verifying a detection rule. The apparatus for verifying a detection rule includes a grammar checking unit for checking for a grammatical error in a loaded detection rule, a false-positive rate calculation unit for calculating a false-positive rate of the loaded detection rule by applying a latent Dirichlet allocation algorithm to a signature used in the detection rule, and a similarity checking unit for checking similarity between the detection rule and an existing pre-stored detection rule.