Knowledge-based cyber physical security at smart home: A review

Alsufyani, Azhar, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2024. Knowledge-based cyber physical security at smart home: A review. ACM Computing Surveys 57 (3) , 53. 10.1145/3698768

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Abstract

Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility towards securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim to overcome these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery (ACM)
ISSN:	0360-0300
Date of First Compliant Deposit:	22 October 2024
Date of Acceptance:	29 September 2024
Last Modified:	17 Dec 2024 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/173063

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