A Multi-time Fuzzy Iterating Feature Selection Algorithm Adapting to IDS
一种适用于IDS的多次模糊迭代特征选择算法

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.