Lightweight Misbehavior Detection Management of Embedded IoT Devices in Medical Cyber Physical Systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choudhary, Gaurav | - |
dc.contributor.author | Astillo, Philip Virgil | - |
dc.contributor.author | You, Ilsun | - |
dc.contributor.author | Yim, Kangbin | - |
dc.contributor.author | Chen, Ing-Ray | - |
dc.contributor.author | Cho, Jin-Hee | - |
dc.date.accessioned | 2021-08-11T08:31:30Z | - |
dc.date.available | 2021-08-11T08:31:30Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 1932-4537 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2292 | - |
dc.description.abstract | We propose a lightweight specification-based misbehavior detection management technique to efficiently and effectively detect misbehavior of an IoT device embedded in a medical cyber physical system through automatic model checking and formal verification. We verify our specification-based misbehavior detection technique with a patient-controlled analgesia (PCA) device embedded in a medical health monitoring system. Through extensive ns3 simulation, we verify its superior performance over popular machine learning anomaly detection methods based on support vector machine (SVM) and k-nearest neighbors (KNN) techniques in both effectiveness and efficiency performance metrics. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | Lightweight Misbehavior Detection Management of Embedded IoT Devices in Medical Cyber Physical Systems | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/TNSM.2020.3007535 | - |
dc.identifier.scopusid | 2-s2.0-85089291797 | - |
dc.identifier.wosid | 000597226700036 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Network and Service Management, v.17, no.4, pp 2496 - 2510 | - |
dc.citation.title | IEEE Transactions on Network and Service Management | - |
dc.citation.volume | 17 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 2496 | - |
dc.citation.endPage | 2510 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | INTRUSION DETECTION | - |
dc.subject.keywordPlus | ATTACKS | - |
dc.subject.keywordPlus | BEHAVIOR | - |
dc.subject.keywordPlus | SECURITY | - |
dc.subject.keywordPlus | INTERNET | - |
dc.subject.keywordAuthor | Principal component analysis | - |
dc.subject.keywordAuthor | Monitoring | - |
dc.subject.keywordAuthor | Biomedical monitoring | - |
dc.subject.keywordAuthor | Security | - |
dc.subject.keywordAuthor | Performance evaluation | - |
dc.subject.keywordAuthor | Support vector machines | - |
dc.subject.keywordAuthor | Personnel | - |
dc.subject.keywordAuthor | Medical cyber physical systems | - |
dc.subject.keywordAuthor | IoT | - |
dc.subject.keywordAuthor | misbehavior detection | - |
dc.subject.keywordAuthor | behavior rules | - |
dc.subject.keywordAuthor | zero-day attacks | - |
dc.subject.keywordAuthor | false positives | - |
dc.subject.keywordAuthor | false negatives | - |
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