Detecting Malicious Nodes in Medical Smartphone Networks Through Euclidean Distance-Based Behavioral Profiling

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

3 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)163-175
Journal / PublicationLecture Notes in Computer Science
Volume10581
Publication statusPublished - Oct 2017

Conference

Title9th International Symposium on Cyberspace Safety and Security(CSS 2017)
PlaceChina
CityXi'an
Period23 - 25 October 2017

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.

Research Area(s)

  • Collaborative network, Insider attack, Intrusion detection, Malicious node, Medical Smartphone Network, Trust computation and management