BRIoT: Behavior Rune Specification-Based Misbehavior Detection for IoT-Embedded Cyber-Physical Systems

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dc.contributor.authorSharma, Vishalen
dc.contributor.authorYou, Ilsunen
dc.contributor.authorVim, Kangbinen
dc.contributor.authorChen, Ing-Rayen
dc.contributor.authorCho, Jin-Heeen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2019-11-13T15:15:26Zen
dc.date.available2019-11-13T15:15:26Zen
dc.date.issued2019en
dc.description.abstractThe identification of vulnerabilities in a mission-critical system is one of the challenges faced by a cyber-physical system (CPS). The incorporation of embedded Internet of Things (IoT) devices makes it tedious to identify vulnerability and difficult to control the service-interruptions and manage the operations losses. Rule-based mechanisms have been considered as a solution in the past. However, rule-based solutions operate on the goodwill of the generated rules and perform assumption-based detection. Such a solution often is far from the actual realization of the IoT runtime performance and can be fooled by zero-day attacks. Thus, this paper takes this issue as motivation and proposes better lightweight behavior rule specification-based misbehavior detection for the IoT-embedded cyber-physical systems (BRIoT). The key concept of our approach is to model a system with which misbehavior of an IoT device manifested as a result of attacks exploiting the vulnerability exposed may be detected through automatic model checking and formal verification, regardless of whether the attack is known or unknown. Automatic model checking and formal verification are achieved through a 2-layer Fuzzy-based hierarchical context-aware aspect-oriented Petri net (HCAPN) model, while effective misbehavior detection to avoid false alarms is achieved through a Barycentric-coordinated-based center of mass calculation method. The proposed approach is verified by an unmanned aerial vehicle (UAV) embedded in a UAV system. The feasibility of the proposed model is demonstrated with high reliability, low operational cost, low false-positives, low false-negatives, and high true positives in comparison with existing rule-based solutions.en
dc.description.notesThis work was supported in part by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean Government (MSIT) (Rule Specification-Based Misbehavior Detection for IoT-Embedded Cyber-Physical Systems) under Grant 2017-0-00664, and in part by the U.S. AFOSR under Grant FA2386-17-1-4076.en
dc.description.sponsorshipInstitute for Information & Communications Technology Promotion (IITP) - Korean Government (MSIT) [2017-0-00664]; U.S. AFOSRUnited States Department of DefenseAir Force Office of Scientific Research (AFOSR) [FA2386-17-1-4076]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2019.2917135en
dc.identifier.eissn2169-3536en
dc.identifier.urihttp://hdl.handle.net/10919/95533en
dc.identifier.volume7en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBehavior rulesen
dc.subjectcyber-physical systemsen
dc.subjectIoTen
dc.subjectspecification-based intrusion detectionen
dc.subjectzero-day attacksen
dc.titleBRIoT: Behavior Rune Specification-Based Misbehavior Detection for IoT-Embedded Cyber-Physical Systemsen
dc.title.serialIEEE Accessen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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Destination Area: Integrated Security (IS)