A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems

Intrusion detection systems are an essential part of any cybersecurity architecture. These systems are critical in defending networks against a variety of security threats. In recent years, deep neural networks have proved their performance and efficiency in various machine learning tasks, including intrusion detection. However, it is shown that deep learning models are highly vulnerable to adver-sarial attacks. This paper proposes a new approach for performing an adversarial attack against deep learning-based malicious network activity classification. We use the Jacobian Saliency Map to find the best group of features, with different features and perturbation magnitude, to generate adversarial examples. We evaluate our method on three CIC-IDS2017, CIC-IDS2018, and CIC-DDoS2019 datasets. Our experiments show that our proposed method can achieve better performance while using fewer features in adversarial sample generation than other attacks that depend on a higher number of features. Our technique can generate adversarial samples for more than 18% of samples in CIC-IDS2017, 15% of samples in CIC-IDS2018, and 14% of samples in CIC-DDoS2019, using only three features and 0.1 as the perturbation magnitude. We do a deeper analysis of the attack based on its parameters, distance metrics, and the target model performance. Also, an evaluation model with three criteria, including success rates of the best feature sets, average confidence of the adversarial class, and adversarial samples transferability, is used in our analysis.(c) 2023 Elsevier B.V. All rights reserved.