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Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection.

Sunil Kaushik1, Akashdeep Bhardwaj2, Ahmad Almogren3

  • 1American Towers (ATC TIPL), Gurgaon, India.

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Summary
This summary is machine-generated.

This study introduces a lightweight intrusion detection system (IDS) and feature selection for Industry 4.0 IoT devices. The novel approach enhances security and reduces training time, achieving over 99.9% accuracy.

Keywords:
Feature selectionLightweight IDSStatistical techniques

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Area of Science:

  • Cybersecurity
  • Internet of Things (IoT)
  • Industrial Control Systems

Background:

  • Rapid expansion of IoT devices in Industry 4.0 presents significant security vulnerabilities.
  • Resource-constrained IoT devices in challenging environments are susceptible to cyberattacks.
  • Existing intrusion detection systems (IDS) face challenges in efficiency and effectiveness for IoT.

Purpose of the Study:

  • To develop a lightweight intrusion detection system (IDS) for resource-limited IoT devices.
  • To propose a novel feature selection algorithm to improve IDS performance and reduce computational overhead.
  • To enhance the security of Industry 4.0 environments against cyber threats.

Main Methods:

  • A unique feature selection algorithm utilizing basic statistical methods.
  • Development of a lightweight intrusion detection system (IDS).
  • Evaluation using IoTID20 and NSLKDD datasets with various classifiers.

Main Results:

  • Reduced training time by 27-63% for multiple classifiers.
  • Improved detection accuracy by selecting the most discriminative features.
  • Achieved over 99.9% accuracy, precision, recall, and F1-Score on IoTID20 dataset.

Conclusions:

  • The proposed lightweight IDS and feature selection effectively address security challenges in Industry 4.0 IoT.
  • The methodology offers a significant improvement in performance and efficiency for IoT security.
  • The system demonstrates robust and consistent performance across different datasets.