Network Intrusion Detection Based on Dynamic Intuitionistic Fuzzy Sets
Jialiang Xie, Honghui Wang, Jonathan M. Garibaldi, Dongrui Wu
Abstract
Network security requires effective detection and proper analysis of abnormal network behavior. To address the uncertainty associated with the process of network intrusion detection, this article proposes a network intrusion-detection algorithm based on dynamic intuitionistic fuzzy sets (IFSs). We use the classic network intrusion datasets KDD 99, NSL-KDD, and the massive, high-dimensional dataset UNSW-NB15 to evaluate the performance of our proposed algorithm. First, we perform data preprocessing on these three datasets and select features based on the results of a chi-square test. Second, using time-series processing, we construct dynamic intuitionistic fuzzy patterns from the feature-selected datasets. At last, we use a proposed distance measure for the dynamic IFSs to generate a classifier that facilitates the detection of network intrusion. Experimental results show that the classification performance of the proposed algorithm is superior to that of other state-of-the-art algorithms on the three aforementioned datasets. The achieved improvement in classification performance is particularly significant for large datasets.