规则优化方法、装置、电子设备及存储介质
By acquiring the structural and content feature data of the rules and using the K-Means algorithm for cluster optimization, the performance degradation of the static detection engine caused by rule similarity is solved, and the speed and quality of rule optimization are improved.
CN116881751BActive Publication Date: 2026-07-17SHENZHEN SHENXIN INFORMATION SECURITY CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN SHENXIN INFORMATION SECURITY CO LTD
- Filing Date
- 2023-05-10
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
In existing technologies, the similarity between rules leads to a decrease in the performance of static detection engines, making it difficult to effectively optimize similar rules and affecting detection efficiency.
Method used
By acquiring the structural and content feature data of the rules to be optimized, the rules are clustered using the K-Means clustering algorithm to form rule clusters, and then optimized based on the similarity of rules within the clusters.
Benefits of technology
It improves the speed and quality of rule optimization, simplifies the processing of similar rules, and enhances the detection efficiency of the static detection engine.
✦ Generated by Eureka AI based on patent content.
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Figure CN116881751B_ABST
Abstract
本申请涉及数据处理技术领域，本申请实施例提供的规则优化方法、装置、电子设备及存储介质，获取待优化规则的规则特征数据；根据所述规则特征数据对所述待优化规则进行聚类处理，得到聚类结果，所述聚类结果包括多个规则簇，每个所述规则簇分别包括多个所述待优化规则；得到每个规则簇分别对应的聚类后的优化规则；通过规则特征数据充分描述待优化规则的表达式结构以及内容特征，根据规则特征数据进行聚类处理后，同一规则簇内待优化规则的表达式结构基本类似且内容特征相似度亦极高，易于提取同一规则簇内待优化规则的上位特征，有利于快速获取各规则簇的优化规则，具有提高簇内各待优化规则优化速度及优化质量的效果。
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