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Machine learning based intrusion detection framework for detecting security attacks in internet of things.

V Kantharaju1, H Suresh2, M Niranjanamurthy1

  • 1Deparment of AI&ML, BMS Institute of Technology and Management (Affiliated to Visvesvaraya Technological University, Belagavi), Bengaluru, India.

Scientific Reports
|December 5, 2024
PubMed
Summary

This study introduces a Self-Attention Progressive Generative Adversarial Network (SAPGAN) for advanced Internet of Things (IoT) security. SAPGAN enhances intrusion detection accuracy and reduces computational time for identifying threats in IoT networks.

Keywords:
Data acquisitionInternet of thingsIntrusion detectionSecurityWSOA

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) involves interconnected devices exchanging data, necessitating robust security measures.
  • Traditional deep learning-based Intrusion Detection Systems (IDS) for IoT face challenges with accurate attack classification and high computational demands.
  • Existing IDS methods struggle to efficiently and accurately detect diverse threats in complex IoT environments.

Purpose of the Study:

  • To propose an advanced Intrusion Detection System (IDS) framework, the Self-Attention Progressive Generative Adversarial Network (SAPGAN), for enhanced security in IoT networks.
  • To address the limitations of traditional deep learning IDS in terms of accuracy and computational efficiency for IoT threat detection.
  • To develop a novel framework capable of accurately classifying intrusions and optimizing feature selection for improved IoT network security.

Main Methods:

  • Data gathering and pre-processing using Local Least Squares to handle missing values.
  • Feature selection employing a modified War Strategy Optimization Algorithm (WSOA) to identify optimal features.
  • Implementation of the SAPGAN framework for classifying network traffic into 'Anomaly' and 'Normal' categories, including attacks like camera-based flood and DDoS.

Main Results:

  • The proposed SAPGAN framework achieved higher accuracy compared to state-of-the-art models, with improvements of 23.19%, 27.55%, and 18.35%.
  • SAPGAN demonstrated significantly lower computational time, outperforming traditional models by 14.46%, 26.76%, and 13.65%.
  • The framework effectively categorized intruders into 'Anomaly' and 'Normal' based on optimized features derived from WSOA.

Conclusions:

  • The SAPGAN framework offers a superior solution for IoT intrusion detection, surpassing traditional methods in both accuracy and efficiency.
  • The integration of Self-Attention mechanisms and Generative Adversarial Networks provides a powerful approach for identifying sophisticated cyber threats in IoT.
  • This research contributes a computationally efficient and highly accurate IDS, crucial for securing the rapidly expanding landscape of the Internet of Things.