Enhancing Trust Management for Wireless Intrusion Detection via Traffic Sampling in the Era of Big Data

Weizhi Meng, Wenjuan Li, Chunhua Su, Jianying Zhou, Rongxing Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

62 Citations (Scopus)


Internet of Things (IoT) has been widely used in our daily life, which enables various objects to be interconnected for data exchange, including physical devices, vehicles, and other items embedded with network connectivity. Wireless sensor network (WSN) is a vital application of IoT, providing many kinds of information among sensors, whereas such network is vulnerable to a wide range of attacks, especially insider attacks, due to its natural environment and inherent unreliable transmission. To safeguard its security, intrusion detection systems (IDSs) are widely adopted in a WSN to defend against insider attacks through implementing proper trust-based mechanisms. However, in the era of big data, sensors may generate excessive information and data, which could degrade the effectiveness of trust computation. In this paper, we focus on this challenge and propose a way of combining Bayesian-based trust management with traffic sampling for wireless intrusion detection under a hierarchical structure. In the evaluation, we investigate the performance of our approach in both a simulated and a real network environment. Experimental results demonstrate that packet-based trust management would become ineffective in a heavy traffic environment, and that our approach can help lighten the burden of IDSs in handling traffic, while maintaining the detection of insider attacks.

Original languageEnglish
Pages (from-to)7234-7243
Number of pages10
JournalIEEE Access
Publication statusPublished - 10 Nov 2017
Externally publishedYes


  • Bayesian model
  • big data
  • Intrusion detection
  • traffic sampling
  • trust computation
  • wireless sensor network

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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