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

Dusmurod Kilichev and Wooseong Kim ()
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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

Mathematics, 2023, vol. 11, issue 17, 1-31

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.

Keywords: intrusion-detection system; NIDS; convolutional neural network; 1D-CNN; hyperparameter optimization; genetic algorithm; GA; particle swarm optimization; PSO (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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