Towards Statistical Trust Computation for Medical Smartphone Networks Based on Behavioral Profiling

Weizhi Meng, Man Ho Au

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Due to the popularity of mobile devices, medical smartphone networks (MSNs) have been evolved, which become an emerging network architecture in healthcare domain to improve the quality of service. There is no debate among security experts that the security of Internet-enabled medical devices is woefully inadequate. Although MSNs are mostly internally used, they still can leak sensitive information under insider attacks. In this case, there is a need to evaluate a node’s trustworthiness in MSNs based on the network characteristics. In this paper, we focus on MSNs and propose a statistical trust-based intrusion detection mechanism
to detect malicious nodes in terms of behavioral profiling (e.g., camera usage, visited websites, etc.). Experimental results indicate that our proposed mechanism is feasible and promising in detecting malicious nodes under medical environments.
Original languageEnglish
Title of host publicationIFIPTM 2017: Trust Management XI
PublisherSpringer
Publication date2017
Pages152–159
DOIs
Publication statusPublished - 2017
Event11th IFIP WG 11.11 International Conference on Trust Management - Gothenburg, Sweden
Duration: 12 Jun 201716 Jun 2017

Conference

Conference11th IFIP WG 11.11 International Conference on Trust Management
CountrySweden
CityGothenburg
Period12/06/201716/06/2017
SeriesIFIP Advances in Information and Communication Technology
Volume505
ISSN1868-4238

Keywords

  • Emerging network
  • Medical smartphone network
  • Intrusion detection
  • Insider attack
  • Statistical trust computation

Cite this

Meng, W., & Au, M. H. (2017). Towards Statistical Trust Computation for Medical Smartphone Networks Based on Behavioral Profiling. In IFIPTM 2017: Trust Management XI (pp. 152–159). Springer. IFIP Advances in Information and Communication Technology, Vol.. 505 https://doi.org/10.1007/978-3-319-59171-1_12