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<p>Amid the intensifying network threats fueled by the swift advancement of information technology, we want to find a new way to ensure network security. The cornerstone of this methodology is the integration of the CatBoost algorithm with a model composed of two Inception V1 modules, each enhanced with three depthwise separable convolutions. The process entails meticulous data preprocessing, judicious feature selection via CatBoost, and exhaustive training and evaluation of the enhanced model. Stringent testing on select datasets has substantiated the exceptional prowess of this approach. The multi-class evaluations conducted on the CICIDS2017 dataset and the latest CICIoT2023 dataset achieved accuracies of 99.85% and 99.13%, and precisions of 99.84% and 99.13%, respectively. Meanwhile, the binary classification experiments on these datasets recorded accuracies and precisions of 99.95%, 99.40% and 99.94%, 99.77%, respectively. These results represent a performance improvement of 1% to 5% in related research contributions using the same datasets, demonstrating the advantages of our method.</p>

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Intrusion detection using a hybrid approach based on CatBoost and an enhanced inception V1

  • 期刊论文

  • 发表日期：

    2025年1月22日

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  • Lieqing Lin,
  • Qi Zhong,
  • Jiasheng Qiu,
  • Zhenyu Liang,
  • Yuerong Yang,
  • Suxiang Hu,
  • Langcheng Chen

摘要

Amid the intensifying network threats fueled by the swift advancement of information technology, we want to find a new way to ensure network security. The cornerstone of this methodology is the integration of the CatBoost algorithm with a model composed of two Inception V1 modules, each enhanced with three depthwise separable convolutions. The process entails meticulous data preprocessing, judicious feature selection via CatBoost, and exhaustive training and evaluation of the enhanced model. Stringent testing on select datasets has substantiated the exceptional prowess of this approach. The multi-class evaluations conducted on the CICIDS2017 dataset and the latest CICIoT2023 dataset achieved accuracies of 99.85% and 99.13%, and precisions of 99.84% and 99.13%, respectively. Meanwhile, the binary classification experiments on these datasets recorded accuracies and precisions of 99.95%, 99.40% and 99.94%, 99.77%, respectively. These results represent a performance improvement of 1% to 5% in related research contributions using the same datasets, demonstrating the advantages of our method.