A heuristic intrusion detection system for Internet-of-Things (IoT)

Ayyaz-Ul-Haq Qureshi, Hadi Larijani, Jawad Ahmad, Nhamoinesu Mtetwa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Today, digitally connected devices are involved in every aspect of life due to the advancements in Internet-of-Things (IoT) paradigm. Recently, it has been a driving force for a major technological revolution towards the development of advanced modern computer networks connecting physical objects around us. The emergence of IPv6 and installation of open access public networks is attracting cyber-criminals to compromise the user specific security information. This is why the security breaches in IoT devices are dominating the headlines lately. In this research we have developed a random neural network based heuristic intrusion detection system (RNN-IDS) for IoTs. Upon feature selection, the neurons are trained and further tested at different learning rates with NSL-KDD dataset. Two methods are adopted to analyse the proposed scheme where the accuracy of RNN-IDS increased from 85.5% to 95.25%. Results also suggest that upon comparison with other machine learning algorithms, the proposed intelligent intrusion detection has higher accuracy in recognition of anomalous traffic from normal patterns.
Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the Computing Conference CompCom 2019: Intelligent Computing
PublisherSpringer
Pages86-98
Number of pages13
ISBN (Electronic)9783030228712
ISBN (Print)9783030228705
DOIs
Publication statusPublished - 23 Jun 2019

Publication series

NameAdvances in Intelligent Systems and Computing book series
Volume997

Keywords

  • cyber-Security
  • intrusion detection systems
  • IoT security
  • machine learning
  • NSL-KDD
  • random neural networks

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