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

TL;DR

This paper introduces a Bayesian optimization framework for automatically tuning hyperparameters of deep neural networks to improve network intrusion detection performance, outperforming random search methods on benchmark data.

Contribution

It presents a novel Bayesian hyperparameter optimization method specifically designed for DNNs in intrusion detection, enhancing detection accuracy and efficiency.

Findings

The proposed framework achieves higher accuracy, precision, recall, and F1-score.

It outperforms random search in hyperparameter tuning.

The approach is validated on the NSL-KDD dataset.

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.