Attack classification of an intrusion detection system using deep learning and hyperparameter optimization

A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks on a network. The success of a NIDS depends on the success of its algorithm and the performance of its method in recognizing attacks. We propose a deep learning intrusion detection system (IDS) using a pre...

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Vydáno v:Journal of information security and applications Ročník 58; s. 102804
Hlavní autoři: Kunang, Yesi Novaria, Nurmaini, Siti, Stiawan, Deris, Suprapto, Bhakti Yudho
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2021
Témata:
Deep learning
Hyperparameter optimization
Intrusion detection system
Multiclass classification
Deep learning
Hyperparameter optimization
Intrusion detection system
Multiclass classification
ISSN:2214-2126
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Shrnutí:A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks on a network. The success of a NIDS depends on the success of its algorithm and the performance of its method in recognizing attacks. We propose a deep learning intrusion detection system (IDS) using a pretraining approach with deep autoencoder (PTDAE) combined with a deep neural network (DNN). Models were developed using hyperparameter optimization procedures. This research provides an alternative solution to deep learning structure models through an automatic hyperparameter optimization process that combines grid search and random search techniques. The automated hyperparameter optimization process helps determine the value of hyperparameters and the best categorical hyperparameter configuration to improve detection performance. The proposed model was tested on the NSL-KDD, and CSE-CIC-ID2018 datasets. In the pretraining phase, we present the results of applying our technique to three feature extraction methods: deep autoencoder (DAE), autoencoder (AE), and stack autoencoder (SAE). The best results are obtained for the DAE method. These performance results also successfully outperform previous approaches in terms of performance metrics in multiclass classification.
ISSN:2214-2126
DOI:10.1016/j.jisa.2021.102804