Optimizing Deep Learning Based Intrusion Detection Systems Defense Against White-Box and Backdoor Adversarial Attacks Through a Genetic Algorithm
Khaled Alrawashdeh, Stephen Goldsmith · 2020
Recent years have witnessed rapid progress and significant success in the use of deep learning neural networks (DLNNs) in a wide range of applications. Recently, DLNN has been integrated with intrusion detection system (IDS) to enhance network security to detect zero-day attacks. However, DLNNs themselves have been recently found vulnerable to attacks called adversarial examples and backdoor attacks for image recognition applications. In this work, we present an effective defense method for DLNN based IDS by using Genetic Algorithm (GA) to optimize the generation of triggers neurons selected based on their response to the features to produce the output. We embed the GA-Trigger-Detection neurons within the model to detect and prevent white-box advertorial examples and backdoor attacks against two DLNNs based IDS: Deep Belief Network (DBN) and Stacked Sparse AutoEncoder Based Extreme Learning Machine (SSAELM). We implement two white-box adversarial examples and backdoor attacks from prior work and use them to investigate the proposed defense method. We show that the defense method is sufficient to defend against sophisticated attackers with 99% success rate and only 1% degrade in accuracy. We then show that it successfully weakens backdoor attacks on the two DNN architectures using two benchmark datasets: KDDCUP'99 and Kyoto. Our work provides an important step toward defenses against white-box advertorial examples and backdoor attacks in DLNNs based IDS.