文献类型：期刊文献

英文题名：Multi-population Diversity-guided Genetic Algorithm for Feature Selection in Network Intrusion Detection

作者：Li, Chunzhen[1]; Tang, Yueyong[2]; Lai, Jianyu[2]; Li, Chuantao[2,3]; Li, Sheng[2]

机构：[1] School of Electronic and Information Engineering, Guangdong Ocean University, Guangdong, Zhanjiang, 524088, China; [2] School of Mathematics and Computer, Guangdong Ocean University, Guangdong, Zhanjiang, 524088, China; [3] School of Automation Engineering, University of Electronic Science and Technology of China, Sichuan, Chengdu, 611731, China

年份：2026

外文期刊名：arXiv

收录：EI(收录号：20260342257)

语种：英文

外文关键词：Cybersecurity - Feature extraction - Intrusion detection - Network intrusion - Population statistics

外文摘要：Network Intrusion Detection System is a critical means of ensuring cybersecurity. However, existing Genetic Algorithm-based feature selection methods face several limitations when dealing with high-dimensional redundant traffic features. For example, population diversity is difficult to maintain, and evolutionary operators lack guidance. To solve these problems, this study proposes the Multi-Population Diversity-Guided Genetic Algorithm (MPDGGA). First, we build a chained multi-population evolutionary structure. Second, we introduce a diversity-guided operator based on information gain ratio. Experiments on NSL-KDD, UNSW-NB15, and 9 UCI datasets show that the proposed model significantly outperforms four other advanced multi-population feature selection models. Across the 11 datasets, it attains the highest accuracy on 10 datasets and at least 2.26% of the features were selected. Copyright ? 2026, The Authors. All rights reserved.