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Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms

Nasr Abosata1, Saba Al-Rubaye1, Gokhan Inalhan1

  • 1School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.

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|January 8, 2023
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Summary
This summary is machine-generated.

This study introduces a novel intrusion detection system (IDS) for the Routing Protocol for Low-Power and Lossy Networks (RPL) in the Internet of Things (IoT). The federated transfer learning-assisted customized distributed IDS (FT-CID) model enhances security by detecting both known and novel RPL intrusions.

Keywords:
AMIInternet of Things (IoT)applicationattacksdistributed sensorsintrusion detectionmachine learningsecurity

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Area of Science:

  • Computer Science
  • Network Security
  • Internet of Things

Background:

  • The Internet of Things (IoT) relies on the Routing Protocol for Low-Power and Lossy Networks (RPL) for connectivity.
  • RPL security is critical due to increasing IoT reliance, but existing intrusion detection systems (IDS) struggle with heterogeneous IoT environments and novel threats.
  • Unified IDS models are insufficient for detecting new RPL intrusions in diverse IoT networks.

Purpose of the Study:

  • To propose a novel federated transfer learning-assisted customized distributed IDS (FT-CID) model for detecting RPL intrusions in heterogeneous IoT environments.
  • To enhance the security of the RPL protocol by enabling the detection of both existing and novel intrusions.
  • To address the limitations of existing unified IDS solutions in complex IoT ecosystems.

Main Methods:

  • Dataset collection and simulation of normal and abnormal traffic in Contiki-NG OS for RPL-IIoT.
  • Federated transfer learning (FTL) for training edge IDSs using local and globally shared parameters.
  • Customized IDS model development through partial retraining and leveraging shared server knowledge.

Main Results:

  • The FT-CID model successfully detects RPL intrusions in heterogeneous IoT networks.
  • The proposed model achieved an accuracy of 85.52% in detecting intrusions.
  • Implicit utilization of local and global parameters from diverse IoTs enhanced overall RPL security.

Conclusions:

  • The FT-CID model offers a customized and effective solution for RPL intrusion detection in heterogeneous IoT.
  • Federated transfer learning is a viable approach for building adaptive and secure IoT IDS.
  • The FT-CID model significantly improves RPL security by addressing the challenge of novel intrusion detection.