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

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