Convolutional neural network models using metaheuristic based feature selection method for intrusion detection Saldırı tespiti için metasezgisel tabanlı özellik seçim yöntemi kullanan evrişimli sinir ağı modelleri
Journal of the Faculty of Engineering and Architecture of Gazi University, vol.40, no.1, pp.179-188, 2024 (SCI-Expanded, Scopus, TRDizin)
- Publication Type: Article / Article
- Volume: 40 Issue: 1
- Publication Date: 2024
- Doi Number: 10.17341/gazimmfd.1287186
- Journal Name: Journal of the Faculty of Engineering and Architecture of Gazi University
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
- Page Numbers: pp.179-188
- Keywords: convolutional neural network, decision tree, feature selection, intrusion detection, meta-heuristic algorithms
- Open Archive Collection: AVESIS Open Access Collection
- Ankara University Affiliated: Yes
Abstract
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 a decision 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 proposed method can be promising in terms of different criteria. The final results prove that EfficientNet and ResNet50 perform 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 out with a recall rate of 95.1% and a rate of 95.2% for F1score.