Multi-population Diversity-guided Genetic Algorithm for Feature Selection in Network Intrusion Detection

• URL: http://arxiv.org/abs/2605.19864v1
• Date: Tue, 19 May 2026 13:57:28 GMT
• Title: Multi-population Diversity-guided Genetic Algorithm for Feature Selection in Network Intrusion Detection
• Abstract summary: This study proposes the Multi-Population Diversity-Guided Genetic Algorithm (MPDGGA)<n>It significantly outperforms four other advanced multi-population feature selection models.<n>Across the 11 datasets, it attains the highest accuracy on 10 datasets and at least 2.26% of the features were selected.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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