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MDPAS: Markov Decision Process based adaptive security for sensors in Internet of things

  • Eric Ke Wang*
  • , Tsu Yang Wu
  • , Chien Ming Chen
  • , Yuming Ye
  • , Zhujin Zhang
  • , Futai Zou
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen
  • Shanghai Jiao Tong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nowadays chipped based sensors and RFID tags are widely employed in Internet of Things; however, for those devices, effective and flexible security mechanisms lack. In this paper we study the security requirement and propose an adaptive security framework for sensors in Internet of things, which provides dynamic confidentiality, authenticity and integrity in the networks with relative suitable overhead by context aware computing, decision making and dynamic enforcement of policies. We employ Markov Decision Process to make the decisions of security actions and adopt aspect-oriented programming technique to enforce the security policies dynamically in the working networks. We made simulations of our framework, and the performance is encouraging.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computing - Proceeding of the Eighth International Conference on Genetic and Evolutionary
EditorsHui Sun, Vaclav Snasel, Chun-Wei Lin, Jeng-Shyang Pan, Ajith Abraham, Ching-Yu Yang, Chun-Wei Lin
PublisherSpringer Verlag
Pages389-397
Number of pages9
ISBN (Electronic)9783319122854
DOIs
StatePublished - 2015
Externally publishedYes
Event8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014 - Nanchang, China
Duration: 18 Oct 201420 Oct 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume329
ISSN (Print)2194-5357

Conference

Conference8th International Conference on Genetic and Evolutionary Computing, ICGEC 2014
Country/TerritoryChina
CityNanchang
Period18/10/1420/10/14

Keywords

  • Adaptive security
  • Aspect-Oriented Programming
  • Markov Decision Process

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