Preventing Adversarial Attacks Against Deep Learning-Based Intrusion Detection System

Abstract

AbstractDeep learning (DL) applications in network intrusion detection systems (NIDS) are increasingly popular in protecting IoT networks against cyber threats. However, these systems are threatened by adversarial attacks that can evade detection and disrupt the network. Preventing such attacks is highly challenging due to their variation and the resource-constrained nature of IoT devices. Therefore, in this paper, we first evaluate the impact of adversarial attacks on a novel DL-based NIDS designed for IoT networks. Then, we propose an adversarial detector powered by a light gradient boosted algorithm against adversarial attacks. The superiority of our proposal is to detect several types of adversarial attacks with high accuracy while ensuring low additional latency. The evaluation results on practical datasets show that our model effectively detects adversarial attacks, with an overall F-score of 99.66% and much higher than that of its competitors.KeywordsAdversarial attack detectionNetwork intrusion detection systemMachine learning