Evaluation of detecting malicious nodes using Bayesian model in wireless intrusion detection

Yuxin Meng, Wenjuan Li, Lam For Kwok

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

26 Citations (Scopus)


Wireless sensor network (WSN) is vulnerable to a wide range of attacks due to its natural environment and inherent unreliable transmission. To protect its security, intrusion detection systems (IDSs) have been widely deployed in such a wireless environment. In addition, trust-based mechanism is a promising method in detecting insider attacks (e.g., malicious nodes) in a WSN. In this paper, we thus attempt to develop a trust-based intrusion detection mechanism by means of Bayesian model and evaluate it in the aspect of detecting malicious nodes in a WSN. This Bayesian model enables a hierarchical wireless sensor network to establish a map of trust values among different sensor nodes. The hierarchical structure can reduce network traffic caused by node-to-node communications. To evaluate the performance of the trust-based mechanism, we analyze the impact of a fixed and a dynamic trust threshold on identifying malicious nodes respectively and further conduct an evaluation in a wireless sensor environment. The experimental results indicate that the Bayesian model is encouraging in detecting malicious sensor nodes, and that the trust threshold in a wireless sensor network is more dynamic than that in a wired network.

Original languageEnglish
Title of host publicationNetwork and System Security - 7th International Conference, NSS 2013, Proceedings
Number of pages14
Publication statusPublished - 2013
Externally publishedYes
Event7th International Conference on Network and System Security, NSS 2013 - Madrid, Spain
Duration: 3 Jun 20134 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7873 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Network and System Security, NSS 2013


  • Bayesian Model
  • Intrusion Detection
  • Network Security
  • Trust Computation
  • Wireless Sensor Network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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