A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks

Hassan Khazane, Mohammed Ridouani, Fatima Salahdine () and Naima Kaabouch ()
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Hassan Khazane: RITM Laboratory, CED Engineering Sciences, ENSEM, Hassan II University, Casablanca 20000, Morocco
Mohammed Ridouani: RITM Laboratory, CED Engineering Sciences, ENSEM, Hassan II University, Casablanca 20000, Morocco
Fatima Salahdine: Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Naima Kaabouch: School of Electrical and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA

Future Internet, 2024, vol. 16, issue 1, 1-42

Abstract: With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwart attacks, including intrusion detection systems (IDSs), malware detection systems (MDSs), and device identification systems (DISs). Machine Learning-based (ML-based) IoT security systems can fulfill several security objectives, including detecting attacks, authenticating users before they gain access to the system, and categorizing suspicious activities. Nevertheless, ML faces numerous challenges, such as those resulting from the emergence of adversarial attacks crafted to mislead classifiers. This paper provides a comprehensive review of the body of knowledge about adversarial attacks and defense mechanisms, with a particular focus on three prominent IoT security systems: IDSs, MDSs, and DISs. The paper starts by establishing a taxonomy of adversarial attacks within the context of IoT. Then, various methodologies employed in the generation of adversarial attacks are described and classified within a two-dimensional framework. Additionally, we describe existing countermeasures for enhancing IoT security against adversarial attacks. Finally, we explore the most recent literature on the vulnerability of three ML-based IoT security systems to adversarial attacks.

Keywords: adversarial attacks; adversarial examples; machine learning; deep learning; Internet of Things; intrusion detection system; malware detection system; device identification system (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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