Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection

• URL: http://arxiv.org/abs/2207.09902v1
• Date: Thu, 7 Jul 2022 20:08:38 GMT
• Title: Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection
• Authors: Mohammad Masum, Hossain Shahriar, Hisham Haddad, Md Jobair Hossain Faruk, Maria Valero, Md Abdullah Khan, Mohammad A. Rahman, Muhaiminul I. Adnan, Alfredo Cuzzocrea
• Abstract summary: Deep neural networks (DNN) have been successfully applied for intrusion detection problems. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyper parameters. We show that the proposed framework demonstrates significantly higher intrusion detection performance than the random search optimization-based approach.
• Score: 2.304713283039168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework's effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score.

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