Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

Papadopoulos, Pavlos; Thornewill Von Essen, Oliver; Pitropakis, Nikolaos; Chrysoulas, Christos; Mylonas, Alexios; Buchanan, William J.

Abstract

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models’ robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.

• Journal Article Type: Article
• Acceptance Date: Apr 20, 2021
• Online Publication Date: Apr 23, 2021
• Publication Date: 2021-04
• Deposit Date: Apr 26, 2021
• Publicly Available Date: Apr 26, 2021
• Journal: Journal of Cybersecurity and Privacy
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Issue: 2
• Pages: 252-273
• Series Title: Intelligent Security and Privacy Approaches against Cyber Threats
• Series ISSN: 2624-800X
• DOI: https://doi.org/10.3390/jcp1020014
• Keywords: adversarial; machine learning; network IDS; Internet of Things
• Public URL: http://researchrepository.napier.ac.uk/Output/2764796
• Publisher URL: https://www.mdpi.com/2624-800X/1/2/14