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

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Salati M., ASKERBEYLİ İ., BOSTANCI G. E.

Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.40, sa.1, ss.179-188, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.17341/gazimmfd.1287186
  • Dergi Adı: Journal of the Faculty of Engineering and Architecture of Gazi University
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.179-188
  • Anahtar Kelimeler: convolutional neural network, decision tree, feature selection, intrusion detection, meta-heuristic algorithms
  • Ankara Üniversitesi Adresli: Evet

Özet

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.