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.
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