  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.
