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dc.contributor.authorIslam, Shafkat
dc.contributor.authorBadsha, Shahriar
dc.contributor.authorKhalil, Ibrahim
dc.contributor.authorAtiquzzaman, Mohammed
dc.contributor.authorKonstantinou, Charalambos
dc.date.accessioned2022-05-08T12:05:12Z
dc.date.available2022-05-08T12:05:12Z
dc.date.issued2022-05-06
dc.identifier.citationIslam, S., Badsha, S., Khalil, I., Atiquzzaman, M., & Konstantinou, C. (2022). A Triggerless Backdoor Attack and Defense Mechanism for Intelligent Task Offloading in Multi-UAV Systems. IEEE Internet of Things Journal, 1–1. https://doi.org/10.1109/jiot.2022.3172936
dc.identifier.issn2327-4662
dc.identifier.issn2372-2541
dc.identifier.doi10.1109/jiot.2022.3172936
dc.identifier.urihttp://hdl.handle.net/10754/676667
dc.description.abstractIn recent years, multi-unmanned aerial vehicular systems (MUAV) have become prevalent in divergent applications: agriculture, spectrum utilization, transportation, forest fire monitoring, among others, due to their flexible, robust, and autonomous operational maneuver. Battery-powered multi-UAV systems possess limited computation and communication resources, significantly reducing their functional dimension by limiting mission time and range. To address this issue, we propose a federated deep reinforcement learning (FDRL) based intelligent and decentralized task offloading scheme for resource-constrained UAVs that can enhance the operational capability of the MUAV systems. Moreover, the proposed FDRL scheme can improve offloading policy quality while preserving data privacy in MUAV. However, such intelligent systems may fall prey to backdoor attacks that can intervene in the system’s regular operation causing rapid degradation of its performance. We introduce a novel triggerless backdoor attack scheme on intelligent task offloading UAVs and analyze its impact to gauge the resiliency of the offloading policy in the presence of an adversary. Then, we propose lightweight agnostic defense mechanisms to combat such backdoors in multi-UAV settings. The extensive simulation results show that the proposed attack and defense strategies are practical and efficient.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9770193/
dc.rights(c) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectMulti-UAV System
dc.subjectEdge Computing
dc.subjectBackdoor Attack
dc.subjectComputation Offloading
dc.subjectResource Drain-out.
dc.titleA Triggerless Backdoor Attack and Defense Mechanism for Intelligent Task Offloading in Multi-UAV Systems
dc.typeArticle
dc.contributor.departmentDivision of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalIEEE Internet of Things Journal
dc.eprint.versionPost-print
dc.contributor.institutionPurdue University, MI 48331 USA.
dc.contributor.institutionBosch Engineering-North America, MI 48331.
dc.contributor.institutionRMIT University, Melbourne, VIC, 3000.
dc.contributor.institutionSchool of Computer Science, University of Oklahoma, Norman, OK 73019 USA.
dc.identifier.pages1-1
kaust.personKonstantinou, Charalambos
refterms.dateFOA2022-05-08T12:07:45Z


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