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Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO

Author

Listed:
  • Dusmurod Kilichev

    (Department of Computer Engineering, Gachon University, Seongnam 1342, Gyeonggi, Republic of Korea)

  • Wooseong Kim

    (Department of Computer Engineering, Gachon University, Seongnam 1342, Gyeonggi, Republic of Korea)

Abstract

This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity of cyberattacks have prompted an urgent need for effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within a 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm (GA) and particle swarm optimization (PSO). The performances of these methods are assessed using three major datasets—UNSW-NB15, CIC-IDS2017, and NSL-KDD. The key performance metrics considered in this study include the accuracy, loss, precision, recall, and F1-score. The results demonstrate considerable improvements in all metrics across all datasets, for both GA- and PSO-optimized models, when compared to those of the original nonoptimized 1D-CNN model. For instance, on the UNSW-NB15 dataset, GA and PSO achieve accuracies of 99.31 and 99.28%, respectively. Both algorithms yield equivalent results in terms of the precision, recall, and F1-score. Similarly, the performances of GA and PSO vary on the CIC-IDS2017 and NSL-KDD datasets, indicating that the efficacy of the optimization algorithm is context-specific and dependent on the nature of the dataset. The findings of this study demonstrate the importance and effects of efficient hyperparameter optimization, greatly contributing to the field of network security. This study serves as a crucial step toward developing advanced, robust, and adaptable IDSs capable of addressing the evolving landscape of cyber threats.

Suggested Citation

  • Dusmurod Kilichev & Wooseong Kim, 2023. "Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO," Mathematics, MDPI, vol. 11(17), pages 1-31, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3724-:d:1228376
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    References listed on IDEAS

    as
    1. Xin Zhang & Dexuan Zou & Xin Shen, 2018. "A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 6(12), pages 1-34, November.
    2. Xuejian Zhao & Huiying Su & Zhixin Sun, 2022. "An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks," Mathematics, MDPI, vol. 10(21), pages 1-15, October.
    3. Iftikhar Ahmad & Qazi Emad Ul Haq & Muhammad Imran & Madini O. Alassafi & Rayed A. AlGhamdi, 2022. "An Efficient Network Intrusion Detection and Classification System," Mathematics, MDPI, vol. 10(3), pages 1-15, February.
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    Cited by:

    1. Dusmurod Kilichev & Dilmurod Turimov & Wooseong Kim, 2024. "Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models," Mathematics, MDPI, vol. 12(4), pages 1-26, February.

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