Multi-population Diversity-guided Genetic Algorithm for Feature Selection in Network Intrusion Detection
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918511665741824 |
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| author | Li, Chunzhen |
| author_facet | Li, Chunzhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19864 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-population Diversity-guided Genetic Algorithm for Feature Selection in Network Intrusion Detection Li, Chunzhen Neural and Evolutionary Computing 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. |
| title | Multi-population Diversity-guided Genetic Algorithm for Feature Selection in Network Intrusion Detection |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2605.19864 |