Convolutional neural network models using metaheuristic based feature selection method for intrusion detection

This paper proposes a novel approach for intrusion detection using a metaheuristic-based feature selection method combined with convolutional neural networks (CNNs). The feature selection method employs adecision tree and a metaheuristic algorithm to select the most important features from different datasets. The selected features are then feed into CNNs, including ResNet50, VGG16, and EfficientNet, to improve the accuracy of intrusion detection. Experimental results on several benchmark datasets show that the proposedmethod can be promising in terms of different criteria. The final results prove that EfficientNet and ResNet50perform much better than VGG16. When EfficientNet and ResNet50 algorithms are applied to NSL-KDD, DEFCON and CDX datasets, the best accuracy rates are 96.2% and 81.3% correspondingly. In addition,while EfficientNet has the highest rate of 98.6% according to the specificity criterion, ResNet50 stands outwith a recall rate of 95.1% and a rate of 95.2% for F1score