Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic

• 作者: Adel Ammar
• 来源期刊: 数据分析和信息处理(英文)
• 学科: 工学
• 关键词: INTRUSION Detection Network Security FEATURE Selection KYOTO DATASET Neural Networks PCA PLS
• 年，卷（期）: 2015,（2）
• 页码范围: 11-19
• 页数: 9页
• 分类号: TP39

摘要

This paper tests various scenarios of feature selection and feature reduction, with the objective of building a real-time anomaly-based intrusion detection system. These scenarios are evaluated on the realistic Kyoto 2006+ dataset. The influence of reducing the number of features on the classification performance and the execution time is measured for each scenario. The so-called HVS feature selection technique detailed in this paper reveals many advantages in terms of consistency, classification performance and execution time.