{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T09:43:43Z","timestamp":1725615823781},"reference-count":71,"publisher":"Association for Computing Machinery (ACM)","issue":"2s","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"\n In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practise available malware features for IoT devices and lack real-time prediction behaviours. More research is thus required on malware detection to cope with real-time misclassification of the input IoT data. Motivated by this, in this article, we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples of IoT network traffic that may not be labeled. In the SETTI architecture, we design\n three<\/jats:italic>\n self-supervised attack techniques, namely,\n Self-MDS<\/jats:italic>\n ,\n GSelf-MDS,<\/jats:italic>\n and\n ASelf-MDS<\/jats:italic>\n . The Self-MDS method considers the IoT input data and the adversarial sample generation in real-time. The GSelf-MDS builds a generative adversarial network model to generate adversarial samples in the self-supervised structure. Finally, ASelf-MDS utilises\n three<\/jats:italic>\n well-known perturbation sample techniques to develop adversarial malware and inject it over the self-supervised architecture. Also, we apply a defence method to mitigate these attacks, namely,\n adversarial self-supervised training,<\/jats:italic>\n to protect the malware detection architecture against injecting the malicious samples. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the Self-MDS method has the most damaging consequences from the attacker\u2019s point of view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the ASelf-MDS method is the most devastating algorithm that can plunge the accuracy rate from 98% to 77%.\n <\/jats:p>","DOI":"10.1145\/3536425","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T13:29:15Z","timestamp":1652794155000},"page":"1-21","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["SETTI: A\n S<\/u>\n elf-supervised Adv\n E<\/u>\n rsarial Malware De\n T<\/u>\n ection Archi\n T<\/u>\n ecture in an\n I<\/u>\n oT Environment"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-5139-1214","authenticated-orcid":false,"given":"Marjan","family":"Golmaryami","sequence":"first","affiliation":[{"name":"Computer Engineering and Information Technology Department, ShirazUniversity of Technology, Modares Blv, Shiraz, Farse, Iran"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4078-3105","authenticated-orcid":false,"given":"Rahim","family":"Taheri","sequence":"additional","affiliation":[{"name":"King\u2019s Communications, Learning and Information Processing (kclip) lab, King\u2019s College London, London, United Kingdom"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3767-0377","authenticated-orcid":false,"given":"Zahra","family":"Pooranian","sequence":"additional","affiliation":[{"name":"5GIC & 6GIC, Institute for Communication Systems (ICS), University of Surrey, United Kingdom"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3284-5086","authenticated-orcid":false,"given":"Mohammad","family":"Shojafar","sequence":"additional","affiliation":[{"name":"5GIC & 6GIC, Institute for Communication Systems (ICS), University of Surrey, United Kingdom"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7886-5878","authenticated-orcid":false,"given":"Pei","family":"Xiao","sequence":"additional","affiliation":[{"name":"5GIC & 6GIC, Institute for Communication Systems (ICS), University of Surrey, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2022,10,6]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3462635"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2873980"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.23919\/ICITST.2017.8356357"},{"key":"e_1_3_1_5_2","article-title":"Self-supervised learning by cross-modal audio-video clustering","volume":"33","author":"Alwassel Humam","year":"2020","unstructured":"Humam Alwassel, Dhruv Mahajan, Bruno Korbar, Lorenzo Torresani, Bernard Ghanem, and Du Tran. 2020. 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