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

Year 2025, Volume: 40 Issue: 1, 179 - 188, 16.08.2024

Maryam Salati , İman Askerzade , Gazi Erkan Bostancı

https://doi.org/10.17341/gazimmfd.1287186

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 proposed method is suitable for online and real-time intrusion detection as feature selection is performed during the pre-training phase. The findings of this study demonstrate the potential of the proposed method to effectively identify and classify intrusions in network traffic.

Keywords

Intrusion detection, Convolutional Neural Network, Meta-heuristic algorithms, Feature selection, Decision Tree

Saldırı tespiti için metasezgisel tabanlı özellik seçim yöntemi kullanan evrişimli sinir ağı modelleri

Abstract

Bu çalışma, Evrişimsel sinir ağları (CNN'ler) ile birleştirilmiş bir meta-sezgisel tabanlı özellik seçim yöntemi kullanarak izinsiz giriş tespiti için yeni bir yaklaşım önermektedir. Önerilen seçme yöntemi, farklı veri kümelerinden en önemli özellikleri seçmek için bir karar ağacı ve metasezgisel bir algoritma kullanır. Seçilen özellikler daha sonra izinsiz giriş tespitinin doğruluğunu artırmak için sırasıyla ResNet50, VGG16 ve EfficientNet modelleri için veri girişi sağlarlar. Veri setindeki deneysel sonuçlar, önerilen yöntemin farklı kriterler açısından referans olabileceğini göstermektedir. Ön eğitim aşamasında özellik seçimi yapıldığından, önerilen yöntemin çevrimiçi ve gerçek zamanlı saldırı tespiti için uygun olduğu belirlenmiştir. Bu çalışmanın bulguları, önerilen yöntemin ağ trafiğindeki izinsiz girişleri etkili bir şekilde tanımlama ve sınıflandırma potansiyeli olduğunu göstermektedir

Keywords

İzinsiz giriş tespiti, Konvolüsyonel Sinir Ağı, Meta-sezgisel algoritmalar, Özellik seçimi, Karar Ağacı.

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