Journal Article2020

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

Electronics

Multidisciplinary Digital Publishing Institute, Vol. 9, Issue 7, pp. 1120, ISBN: 2079-9292

DOI: 10.3390/electronics9071120URL

Abstract

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.

CCDS Authors

Ashraful Islam

Ashraful Islam, PhD

Center DirectorDirector, Human-Computer Interaction Wing

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