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A practical model based on anomaly detection for protecting medical IoT control services against external attacks


Fang, L and Li, Y and Liu, Z and Yin, C and Li, M and Cao, Z, A practical model based on anomaly detection for protecting medical IoT control services against external attacks, IEEE Transactions on Industrial Informatics ISSN 1551-3203 (In Press) [Refereed Article]

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DOI: doi:10.1109/TII.2020.3011444


The application of the Internet of Things (IoT) in medical field has brought unprecedented convenience to human beings. However, attackers can use device configuration vulnerabilities to hijack devices, control services, steal medical data, or make devices operate illegally. These restrictions have led to huge security risks for IoT, and have challenged the management of critical infrastructure services. Based on these problems, this paper proposes an anomaly detection system for detecting illegal behavior (DIB) in medical IoT environment. The DIB system can analyze data packets transmitted by medical IoT devices, learn operation rules by itself, and remind management personnel that the device is in an abnormal operation state to ensure the safety of control service. We further propose a model which is based on rough set (RS) theory and fuzzy core vector machine (FCVM) to improve the accuracy of DIB classification anomalies. Experimental results show that the R-FCVM is effective.

Item Details

Item Type:Refereed Article
Keywords:smart healthcare, Internet of Things, anomaly detection for protecting medical IoT control services,
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:139943
Year Published:In Press
Deposited By:Information and Communication Technology
Deposited On:2020-07-17
Last Modified:2020-08-24
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