Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74952
Title: Detecting malicious nodes in medical smartphone networks through euclidean distance-based behavioral profiling
Authors: Meng, W
Li, W
Wang, Y
Au, MH 
Keywords: Collaborative network
Insider attack
Intrusion detection
Malicious node
Medical Smartphone Network
Trust computation and management
Issue Date: 2017
Publisher: Springer Verlag
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10581 LNCS, p. 163-175 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: With the increasing digitization of the healthcare industry, a wide range of medical devices are Internet- and inter-connected. Mobile devices (e.g., smartphones) are one common facility used in the healthcare industry to improve the quality of service and experience for both patients and healthcare personnel. The underlying network architecture to support such devices is also referred to as medical smartphone networks (MSNs). Similar to other networks, MSNs also suffer from various attacks like insider attacks (e.g., leakage of sensitive patient information by a malicious insider). In this work, we focus on MSNs and design a trust-based intrusion detection approach through Euclidean distance-based behavioral profiling to detect malicious devices (or called nodes). In the evaluation, we collaborate with healthcare organizations and implement our approach in a real simulated MSN environment. Experimental results demonstrate that our approach is promising in effectively identifying malicious MSN nodes.
Description: 9th International Symposium on Cyberspace Safety and Security, CSS 2017, Xi'an, China, 23-25 October, 2017
URI: http://hdl.handle.net/10397/74952
ISBN: 9783319694702
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-69471-9_12
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