Intrusion detection system for the Internet of Things based on blockchain and multi-agent systems

Chao Liang, Bharanidharan Shanmugam*, Sami Azam, Asif Karim, Ashraful Islam, Mazdak Zamani, Sanaz Kavianpour, Norbik Bashah Idris

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)
168 Downloads (Pure)


With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.
Original languageEnglish
Pages (from-to)1-27
Number of pages27
Issue number7
Early online date10 Jul 2020
Publication statusPublished - 10 Jul 2020


  • Blockchain
  • Internet of Things
  • Intrusion detection system
  • Multi-agent system


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