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Towards Bayesian-based Trust Management for Insider Attacks in Healthcare Software-Defined Networks
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
Department of Information Systems and Cyber Security and the Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, United States.
School of Computing, Electronics and Mathematics, Plymouth University, United Kindom.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
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2018 (English)In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 15, no 2, p. 761-773Article in journal (Refereed) Published
Abstract [en]

The medical industry is increasingly digitalized and Internet-connected (e.g., Internet of Medical Things), and when deployed in an Internet of Medical Things environment, software-defined networks (SDN) allow the decoupling of network control from the data plane. There is no debate among security experts that the security of Internet-enabled medical devices is crucial, and an ongoing threat vector is insider attacks. In this paper, we focus on the identification of insider attacks in healthcare SDNs. Specifically, we survey stakeholders from 12 healthcare organizations (i.e., two hospitals and two clinics in Hong Kong, two hospitals and two clinics in Singapore, and two hospitals and two clinics in China). Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment. Experimental results in either a simulated and a real-world network environment demonstrate the feasibility and effectiveness of our proposed approach regarding the detection of malicious healthcare devices, i.e., our approach could decrease the trust values of malicious devices faster than similar approaches.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 15, no 2, p. 761-773
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-67941DOI: 10.1109/TNSM.2018.2815280ISI: 000435177300020Scopus ID: 2-s2.0-85043786981OAI: oai:DiVA.org:ltu-67941DiVA, id: diva2:1190649
Note

Validerad;2018;Nivå 2;2018-06-15 (andbra)

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-07-26Bibliographically approved

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Vasilakos, Athanasios

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