Detecting malicious nodes in medical smartphone networks through euclidean distance-based behavioral profiling

Weizhi Meng, Wenjuan Li, Yu Wang, Man Ho Au

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationCyberspace Safety and Security - 9th International Symposium, CSS 2017, Proceedings
EditorsWei Wu, Aniello Castiglione, Sheng Wen
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319694702
Publication statusPublished - 2017
Event9th International Symposium on Cyberspace Safety and Security, CSS 2017 - Xi'an, China
Duration: 23 Oct 201725 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10581 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Symposium on Cyberspace Safety and Security, CSS 2017


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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