Utilizing Autoencoder to Improve the Robustness of Intrusion Detection Systems Against Adversarial Attacks

Due to the escalating utilization of communication networks and the prevailing occurrence of cyber attacks, intrusion detection systems (IDSs) have emerged as imperative components in network security. Machine learning (ML) and deep learning (DL) based IDSs have gained popularity due to their detection capability and adaptability. However, this type of schemes are susceptible to adversarial attacks, which involve minor perturbations to attack features causing misclassification. Autoencoders (AEs) have proven effective in mitigating adversarial attacks in computer vision, but their capacity for enhancing IDSs remains relatively unexplored. In this paper, we focus on the use of AEs to detect adversarial network flows. Specifically, we propose an AE-enhanced IDS (AE-IDS) that leverages the power of AEs to improve the robustness of IDSs against adversarial attacks. Our experimental results indicate that AE-IDS outperforms the baseline schemes under investigation in terms of accuracy and detection rate. We believe that AE-IDS showcases the potential of using AEs to enhance the robustness of IDSs, providing improved security against sophisticated and evolving cyber threats.