一种适用于IDS的多次模糊迭代特征选择算法

A Multi-time Fuzzy Iterating Feature Selection Algorithm Adapting to IDS

本文针对入侵检测系统(IDS)被检测数据的特点,对适用于IDS的特征选择算法进行了研究,提出了一种基于分类的多次模糊迭代特征选择算法。该算法包括在属性空间中搜索特征子集、评估每个候选特征子集和分类这3个步骤,设计了与之相应的搜索算法和评估函数;算法通过多次迭代去除特征值集的冗余特征,得到精确度较高的特征值集;使用模糊逻辑得到与精确度要求相应的取值范围;由于单纯对数据进行操作,能比依赖于领域知识的算法更客观地分析数据。文内还对所提出的算法做了测试实验;并将实验结果与用可视化工具产生的特征可视化结果进行了比较。结果表明:该算法在IDS数据集上可取得良好的特征选择效果。

Based on the characteristics of detected data in IDS, feature selection algorithms adapting to IDS are studied in this paper, and a Multi-time Fuzzy Iterating Feature Selection Algorithm is proposed. This algorithm includes three steps, one is searching feature subsets from feature space, the other is valuating every candidate feature subset, and the last is classification. Corresponding search algorithm and valuation function are designed in the algorithm. The algorithm eliminates redundant features through multi-time iterating to get high precision feature value set, uses fuzzy logic to get the value range meeting the need of precision. This algorithm can analyze data more objectively than the algorithm with field knowledge for it only operates datasets. The paper also does some test experiments on the algorithm, and compares experiment results with feature visualization results from visualization tools. The results indicate., this algorithm can get good feature selection effect on IDS datasets.