Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks before they enter the system and classifying them as malicious activities. However, the IDS approaches have shortcomings in misclassifying novel attacks or adapting to emerging environments, affecting their accuracy and increasing false alarms. To solve this problem, researchers have recommended using machine learning approaches as engines for IDSs to increase their efficacy. Machine-learning techniques are supposed to automatically detect the main distinctions between normal and malicious data, even novel attacks, with high accuracy. However, carefully designed adversarial input perturbations during the training or testing phases can significantly affect their predictions and classifications. Adversarial machine learning (AML) poses many cybersecurity threats in numerous sectors that use machine-learning-based classification systems, such as deceiving IDS to misclassify network packets. Thus, this paper presents a survey of adversarial machine-learning strategies and defenses. It starts by highlighting various types of adversarial attacks that can affect the IDS and then presents the defense strategies to decrease or eliminate the influence of these attacks. Finally, the gaps in the existing literature and future research directions are presented.
An intrusion detection system (IDS) is an effective tool for securing networks and a dependable technique for improving a user’s internet security. It informs the administration whenever strange conduct occurs. An IDS fundamentally depends on the classification of network packets as benign or attack. Moreover, IDSs can achieve better results when built with machine learning (ML)/deep learning (DL) techniques, such as convolutional neural networks (CNNs). However, there is a limitation when building a reliable IDS using ML/DL techniques, which is their vulnerability to adversarial attacks. Such attacks are crafted by attackers to compromise the ML/DL models, which affects their accuracy. Thus, this paper describes the construction of a sustainable IDS based on the CNN technique, and it presents a method for defense against adversarial attacks that enhances the IDS’s accuracy and ensures it is more reliable in performing classification. To achieve this goal, first, two IDS models with a convolutional neural network (CNN) were built to enhance the IDS accuracy. Second, seven adversarial attack scenarios were designed against the aforementioned CNN-based IDS models to test their reliability and efficiency. The experimental results show that the CNN-based IDS models achieved significant increases in the intrusion detection system accuracy of 97.51% and 95.43% compared with the scores before the adversarial scenarios were applied. Furthermore, it was revealed that the adversarial attacks caused the models’ accuracy to significantly decrease from one attack scenario to another. The Auto-PGD and BIM attacks had the strongest effect against the CNN-based IDS models, with accuracy drops of 2.92% and 3.46%, respectively. Third, this research applied the adversarial perturbation elimination with generative adversarial nets (APE_GAN++) defense method to enhance the accuracy of the CNN-based IDS models after they were affected by adversarial attacks, which was shown to increase after the adversarial attacks in an intelligible way, with accuracy scores ranging between 78.12% and 89.40%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.