Towards moving target defense for IoT malware detection

Simple item page
dc.contributor.authorRyan, Ita
dc.contributor.authorKurlandski, Luke
dc.contributor.authorMathews, Nate
dc.contributor.funderTaighde Éireann - Research Ireland, 23/US/3939
dc.contributor.funderNational Science Foundation, 2422241
dc.date.accessioned2026-02-11T12:50:02Z
dc.date.available2026-02-11T12:50:02Z
dc.date.issued2026-04-12
dc.description.abstractMachine learning (ML) techniques show promise in malware defense for the Internet of Things (IoT), but are vulnerable to tailored adversarial attacks. Moving Target Defense (MTD) is a security strategy that actively raises the cost to the attacker of a potential attack by changing the target’s characteristics, preventing attackers from profiling the target. In this work we explore the potential for using MTD for IoT malware detection. Applying MTD to protect ML malware detection involves continuously changing the malware classification models, defeating attempts to profile the models. We research the state-of-the-art literature that uses an MTD-style strategy to increase ML model security. We identify two techniques: 'Naive MTD', which cycles between static models, and 'Full MTD', which refreshes models at runtime and is therefore more effective. Focusing on the studies in the ML literature that use Full MTD for adversarial robustness, we examine their approach, assessing features such as discard policy, decision-making and model updating schedule. We make a number of recommendations on development of a Full MTD strategy for ML IoT malware detection.en
dc.description.sponsorshipThis publication has emanated from research conducted with the financial support of Research Ireland under Grant number 23/US/3939, and supported in part by the National Science Foundation under Award No. 2422241.
dc.format.extent8
dc.format.mimetypeapplication/pdfen
dc.identifier.authororcidRyan, Ita§0000-0001-6844-6365
dc.identifier.authororcidKurlandski, Luke
dc.identifier.authororcidMathews, Nate
dc.identifier.citationRyan, I, Kurlandski, L & Mathews, N 2026, Towards moving target defense for IoT malware detection. in Proceedings of the 8th International Workshop on Software Engineering Research and Practices for the IoT (SERP4IoT ’26). SERP4IoT, Association for Computing Machinery (ACM), pp. 1-8. https://doi.org/10.1145/3786159.3788477
dc.identifier.doi10.1145/3786159.3788477
dc.identifier.endpage8
dc.identifier.isbn9798400723919
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18513
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofseriesSERP4IoT
dc.relation.urihttps://www.scopus.com/pages/publications/105044589994
dc.rights© 2026, the owner/author(s). This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectInternet of things
dc.subjectMalware
dc.subjectMalware defense
dc.subjectMoving target defense
dc.subject[ComputerScience]
dc.titleTowards moving target defense for IoT malware detectionen
dc.typeConference item
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Serp4IoT_18_TowardsMTD.pdf
Size:
545.28 KB
Format:
Adobe Portable Document Format
Description:
Accepted Version
Download
Collections
School of Computer Science and Information Technology