review · Future Internet
Machine learning plays a crucial role in securing Internet of Things networks by identifying threats, authenticating users, and categorising suspicious activity through systems such as intrusion detection, malware detection, and device identification. However, these systems remain vulnerable to adversarial attacks designed deliberately to mislead learning classifiers. A comprehensive review examines the current landscape of these adversarial threats and the defensive measures developed to counter them across Internet of Things environments. The work establishes a clear taxonomy of adversarial attacks specifically tailored to connected devices and structures attack generation methods within a two-dimensional classification framework. In addition, it details existing defensive countermeasures and analyses recent research regarding the specific vulnerabilities present within machine learning-based intrusion detection, malware detection, and device identification systems.
As connected devices proliferate in homes and critical industries, relying on machine learning to defend them introduces new risks if algorithms can be easily tricked. Understanding how attackers manipulate security models, alongside the mechanisms available to reinforce them, helps network defenders ensure that automated detection tools remain reliable in the face of deliberate interference.
The review provides analytical foundations to assist Internet of Things security developers and network administrators in hardening threat detection tools. Because the work focuses on taxonomies, vulnerability analysis, and categorising countermeasures, it sits at the early-stage research level, serving as guidance for future defensive software development rather than presenting a near-market commercial solution.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/fi16010032
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
