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Optimising user security recommendations for AI-powered smart-homes

Optimising user security recommendations for AI-powered smart-homes

Scott, Emma, Panda, Sakshyam, Loukas, George ORCID: 0000-0003-3559-5182 and Panaousis, Emmanouil ORCID: 0000-0001-7306-4062 (2022) Optimising user security recommendations for AI-powered smart-homes. In: 2022 IEEE Conference on Dependable and Secure Computing (DSC). IEEE. ISBN 9781665421416 (doi:

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Research in the context of user awareness has shown that smart-home occupants often lack cybersecurity awareness even when it comes to frequently used technologies such as online social networks and email. To cope with the risks, smart-homes must be equipped with adequate cybersecurity measures besides the knowledge and time required by smart-home occupants to implement security measures. In this paper, we explore potential threats in AI-powered smart-homes and identify a list of cybersecurity controls required to mitigate their potential impact considering attack vectors, as well as the time and knowledge required to implement a control. We use optimisation to identify the best set of controls to minimise the risk exposure considering these metrics. Our comparative analysis against a random selection approach highlight that our approach is at least 25% better at minimising risk. Finally, we show how improved knowledge or time impacts the risk.

Item Type: Conference Proceedings
Title of Proceedings: 2022 IEEE Conference on Dependable and Secure Computing (DSC)
Uncontrolled Keywords: cybersecurity, smart-home, threats, control optimisation, risk assessment
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Internet of Things and Security Research Centre (ISEC)
Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified: 06 Oct 2022 14:13

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