Evolving convolutional neural networks for intrusion detection system using hybrid multi-strategy aquila optimizer
Wei Sun, Qianmu Li, Pengchuan Wang, Jun Hou · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022
As the attack methods of intruders become more complicated and intelligent, the capability of the intrusion detection system (IDS) should be improved to deal with this situation. In recent years, deep learning has been widely used in the IDS because deep learning has advantages in processing high-dimensional data, obtaining hidden information in data, and solving the problem of data imbalance in the network. In addition, swarm intelligence algorithms (SI) as stochastic optimization methods have been extensively employed to improve various optimization problems. In this paper, we propose a hybrid multi-strategy aquila optimizer (HMAO) to obtain the optimal subset of features extracted by convolutional neural networks to train the IDS classifier. We demonstrate the superiority of HMAO by using standard benchmark function experiment. Then, we evaluate the proposed algorithm for IDS on UNSW-NB15 dataset. The experimental results show that HMAO outperforms the original algorithm in terms of the capacity of finding excellent solutions greatly. Furthermore, in the application of IDS, the proposed algorithm performs better than several feature engineering methods from state-of-the-art related works in Accuracy, TPR, FPR, and F-score.