An Intrusion Feature Selection Method Based on Feature Distribution and Gini Impurity

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Abstract
Intrusion detection systems (IDS) can effectively monitor network traffic and accurately detect malicious behaviors. In Internet of Things (IoT) environments, the massive influx of heterogeneous, resource-constrained devices introduces more complex security challenges, making IDS even more crucial for maintaining network security. However, the presence of redundant or irrelevant features in network traffic can significantly degrade the detection performance of IDS. To address this issue, this paper proposes a Feature distribution and Gini Impurity Filter-based intrusion feature selection method (FGIF). It combines the cardinality and Gini impurity distributions of features within a dataset to construct a multi-parameter evaluation framework, which is used to define efficient feature filtering rules that eliminate redundant and irrelevant features. Theoretical analysis demonstrates that, compared to entropy-based methods, FGIF mitigates selection bias during the feature selection process and significantly reduces computational overhead. Experiments conducted on six widely used IDS benchmark datasets and five commonly adopted classification models further confirm its effectiveness. FGIF significantly reduces feature dimensionality while maintaining detection performance comparable to that of the full feature set. Moreover, compared to existing state-of-the-art methods, FGIF achieves a superior balance between dimensionality reduction and model performance.
Funding Information
  • Fundamental Research Funds for the Central Universities, Southwest Minzu University (ZYN2025005)
  • National Natural Science Foundation of China (62572101)
  • Natural Science Foundation of Sichuan Province (2026NSFSC0432)

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