Adversarial machine learning in IoT from an insider point of view

Aloraini, Fatimah, Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X 2022. Adversarial machine learning in IoT from an insider point of view. Journal of Information Security and Applications 70 , 103341. 10.1016/j.jisa.2022.103341

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Abstract

With the rapid progress and significant successes in various applications, machine learning has been considered a crucial component in the Internet of Things ecosystem. However, machine learning models have recently been vulnerable to carefully crafted perturbations, so-called adversarial attacks. A capable insider adversary can subvert the machine learning model at either the training or testing phase, causing them to behave differently. The vulnerability of machine learning to adversarial attacks becomes one of the significant risks. Therefore, there is a need to secure machine learning models enabling the safe adoption in malicious insider cases. This paper reviews and organizes the body of knowledge in adversarial attacks and defense presented in IoT literature from an insider adversary point of view. We proposed a taxonomy of adversarial methods against machine learning models that an insider can exploit. Under the taxonomy, we discuss how these methods can be applied in real-life IoT applications. Finally, we explore defensive methods against adversarial attacks. We believe this can draw a comprehensive overview of the scattered research works to raise awareness of the existing insider threats landscape and encourages others to safeguard machine learning models against insider threats in the IoT ecosystem.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Uncontrolled Keywords:	Adversarial machine learning, Insider, IoT, Cybersecurity, Machine learning, Deep learning
Publisher:	Elsevier
ISSN:	2214-2126
Date of First Compliant Deposit:	1 October 2022
Date of Acceptance:	18 September 2022
Last Modified:	03 May 2023 15:20
URI:	https://orca.cardiff.ac.uk/id/eprint/152837

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