Lightweight Misbehavior Detection Management of Embedded IoT Devices in Medical Cyber Physical Systems
- Authors
- Choudhary, Gaurav; Astillo, Philip Virgil; You, Ilsun; Yim, Kangbin; Chen, Ing-Ray; Cho, Jin-Hee
- Issue Date
- Dec-2020
- Publisher
- Institute of Electrical and Electronics Engineers
- Keywords
- Principal component analysis; Monitoring; Biomedical monitoring; Security; Performance evaluation; Support vector machines; Personnel; Medical cyber physical systems; IoT; misbehavior detection; behavior rules; zero-day attacks; false positives; false negatives
- Citation
- IEEE Transactions on Network and Service Management, v.17, no.4, pp 2496 - 2510
- Pages
- 15
- Journal Title
- IEEE Transactions on Network and Service Management
- Volume
- 17
- Number
- 4
- Start Page
- 2496
- End Page
- 2510
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2292
- DOI
- 10.1109/TNSM.2020.3007535
- ISSN
- 1932-4537
- 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.
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- Appears in
Collections - College of Engineering > Department of Information Security Engineering > 1. Journal Articles
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