Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks

Meng, Weizhi; Choo, Kim Kwang Raymond; Furnell, Steven; Vasilakos, Athanasios V.; Probst, Christian W.

Abstract

© 2004-2012 IEEE. 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 (SDNs) 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.

Citation

Meng, W., Choo, K. K. R., Furnell, S., Vasilakos, A. V., & Probst, C. W. (2018). Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks. IEEE Transactions on Network and Service Management, 15(2), 761-773. https://doi.org/10.1109/TNSM.2018.2815280

• Journal Article Type: Article
• Acceptance Date: Feb 25, 2018
• Online Publication Date: Mar 13, 2018
• Publication Date: Jun 1, 2018
• Deposit Date: Sep 14, 2020
• Publicly Available Date: Sep 14, 2020
• Journal: IEEE Transactions on Network and Service Management
• Print ISSN: 1932-4537
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 2
• Pages: 761-773
• DOI: https://doi.org/10.1109/TNSM.2018.2815280
• Public URL: https://nottingham-repository.worktribe.com/output/4867991
• Publisher URL: https://ieeexplore.ieee.org/document/8315151
• Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works