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

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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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.

In: IEEE Transactions on Network and Service Management, Vol. 15, No. 2, 2018, p. 761-773.

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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@article{1f595d7cb09841d7a030ac8f2dfa3165,
title = "Towards Bayesian-based Trust Management for Insider Attacks in Healthcare Software-Defined Networks",
abstract = "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.",
keywords = "Intrusion Detection, Software-Defined Networking, Trust Computation and Management, Healthcare Network, Bayesian Inference.",
author = "Weizhi Meng and Choo, {Kim-Kwang Raymond} and Steven Furnell and Vasilakos, {Athanasios V.} and Probst, {Christian W.}",
year = "2018",
doi = "10.1109/TNSM.2018.2815280",
language = "English",
volume = "15",
pages = "761--773",
journal = "IEEE Transactions on Network and Service Management",
issn = "1932-4537",
publisher = "Institute of Electrical and Electronics Engineers",
number = "2",

}

RIS

TY - JOUR

T1 - Towards Bayesian-based Trust Management for Insider Attacks in Healthcare Software-Defined Networks

AU - Meng, Weizhi

AU - Choo, Kim-Kwang Raymond

AU - Furnell, Steven

AU - Vasilakos, Athanasios V.

AU - Probst, Christian W.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Intrusion Detection

KW - Software-Defined Networking

KW - Trust Computation and Management

KW - Healthcare Network

KW - Bayesian Inference.

U2 - 10.1109/TNSM.2018.2815280

DO - 10.1109/TNSM.2018.2815280

M3 - Journal article

VL - 15

SP - 761

EP - 773

JO - IEEE Transactions on Network and Service Management

JF - IEEE Transactions on Network and Service Management

SN - 1932-4537

IS - 2

ER -