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SALT: transfer learning-based threat model for attack detection in smart home
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 becom...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293907/ https://www.ncbi.nlm.nih.gov/pubmed/35851092 http://dx.doi.org/10.1038/s41598-022-16261-9 |
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author | Anand, Pooja Singh, Yashwant Singh, Harvinder Alshehri, Mohammad Dahman Tanwar, Sudeep |
author_facet | Anand, Pooja Singh, Yashwant Singh, Harvinder Alshehri, Mohammad Dahman Tanwar, Sudeep |
author_sort | Anand, Pooja |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9293907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92939072022-07-20 SALT: transfer learning-based threat model for attack detection in smart home Anand, Pooja Singh, Yashwant Singh, Harvinder Alshehri, Mohammad Dahman Tanwar, Sudeep Sci Rep Article 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. Nature Publishing Group UK 2022-07-18 /pmc/articles/PMC9293907/ /pubmed/35851092 http://dx.doi.org/10.1038/s41598-022-16261-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Anand, Pooja Singh, Yashwant Singh, Harvinder Alshehri, Mohammad Dahman Tanwar, Sudeep SALT: transfer learning-based threat model for attack detection in smart home |
title | SALT: transfer learning-based threat model for attack detection in smart home |
title_full | SALT: transfer learning-based threat model for attack detection in smart home |
title_fullStr | SALT: transfer learning-based threat model for attack detection in smart home |
title_full_unstemmed | SALT: transfer learning-based threat model for attack detection in smart home |
title_short | SALT: transfer learning-based threat model for attack detection in smart home |
title_sort | salt: transfer learning-based threat model for attack detection in smart home |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293907/ https://www.ncbi.nlm.nih.gov/pubmed/35851092 http://dx.doi.org/10.1038/s41598-022-16261-9 |
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