SALT: transfer learning-based threat model for attack detection in smart home.

Autor: Anand P; Department of Computer Science and Information Technology, Central University of Jammu, Rahya Suchani, Jammu and Kashmir, 181143, India., Singh Y; Department of Computer Science and Information Technology, Central University of Jammu, Rahya Suchani, Jammu and Kashmir, 181143, India., Singh H; Leaders Institute, Woolloongabba, Brisbane, QLD, -4102, Australia. harvinder_07@yahoo.com.; Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, QLD, 4006, Australia. harvinder_07@yahoo.com., Alshehri MD; Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia., Tanwar S; Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, 382481, India. sudeep.tanwar@nirmauni.ac.in.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Jul 18; Vol. 12 (1), pp. 12247. Date of Electronic Publication: 2022 Jul 18.
DOI: 10.1038/s41598-022-16261-9
Abstrakt: The next whooping revolution after the Internet is its scion, the Internet of Things (IoT), which has facilitated every entity the power to connect to the web. However, this magnifying depth of the digital pool oil the wheels for the attackers to penetrate. Thus, these threats and attacks have become a prime concern among researchers. With promising features, Machine Learning (ML) has been the solution throughout to detect these threats. But, the general ML-based solutions have been declining with the practical implementation to detect unknown threats due to changes in domains, different distributions, long training time, and lack of labelled data. To tackle the aforementioned issues, Transfer Learning (TL) has emerged as a viable solution. Motivated by the facts, this article aims to leverage TL-based strategies to get better the learning classifiers to detect known and unknown threats targeting IoT systems. TL transfers the knowledge attained while learning a task to expedite the learning of new similar tasks/problems. This article proposes a learning-based threat model for attack detection in the Smart Home environment (SALT). It uses the knowledge of known threats in the source domain (labelled data) to detect the unknown threats in the target domain (unlabelled data). The proposed scheme addresses the workable differences in feature space distribution or the ratio of attack instances to a normal one, or both. The proposed threat model would show the implying competence of ML with the TL scheme to improve the robustness of learning classifiers besides the threat variants to detect known and unknown threats. The performance analysis shows that traditional schemes underperform for unknown threat variants with accuracy dropping to 39% and recall to 56.
(© 2022. The Author(s).)
Databáze: MEDLINE
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