An Intelligent Two-Layer Intrusion Detection System for the Internet of Things

Mohammed M. Alani, Ali Ismail Awad

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

The Internet of Things (IoT) has become an enabler paradigm for different applications, such as healthcare, education, agriculture, smart homes, and recently, enterprise systems. Significant advances in IoT networks have been hindered by security vulnerabilities and threats, which, if not addressed, can negatively impact the deployment and operation of IoT-enabled systems. This article addresses IoT security and presents an intelligent two-layer intrusion detection system for IoT. The system's intelligence is driven by machine learning techniques for intrusion detection, with the two-layer architecture handling flow-based and packet-based features. By selecting significant features, the time overhead is minimized without affecting detection accuracy. The uniqueness and novelty of the proposed system emerge from combining machine learning and selection modules for flow-based and packet-based features. The proposed intrusion detection works at the network layer, and hence, it is device and application transparent. In our experiments, the proposed system had an accuracy of 99.15% for packet-based features with a testing time of 0.357 μs. The flow-based classifier had an accuracy of 99.66% with a testing time of 0.410 μs. A comparison demonstrated that the proposed system outperformed other methods described in the literature. Thus, it is an accurate and lightweight tool for detecting intrusions in IoT systems.

Original languageEnglish
Pages (from-to)683-692
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume19
Issue number1
DOIs
Publication statusPublished - Jan 1 2023

Keywords

  • Efficiency
  • flow-based features
  • Internet of Things (IoT)
  • intrusion detection
  • machine learning
  • packet-based features

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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