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A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things

Kashi Sai Prasad1, P Udayakumar2, E Laxmi Lydia3

  • 1Department of CSE-AI&ML, MLR Institute of Technology, Hyderabad, India.

Scientific Reports
|January 17, 2025
PubMed
Summary

This study introduces a new model for robust adversarial attack mitigation in IoT network security. The TTOS-RAAM model effectively detects adversarial attacks with 99.91% accuracy, enhancing IoT data protection.

Keywords:
Adversarial attackAfrican vulture optimizationHybrid feature selectionIntrusion detection systemIoTTwo-tier optimization

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • Adversarial attacks pose a growing threat to network security, particularly in the context of interconnected IoT systems.
  • While Deep Learning (DL) models are used in Intrusion Detection Systems (IDS), their vulnerability to adversarial examples remains an open research area.
  • The increasing reliance on IoT, AI, and 5G in Industry 4.0 amplifies security concerns due to vast data processing.

Purpose of the Study:

  • To introduce a novel Two-Tier Optimization Strategy for Robust Adversarial Attack Mitigation (TTOS-RAAM) model for enhanced IoT network security.
  • To address the challenge of detecting adversarial attack behavior within IoT environments.
  • To evaluate the effectiveness of DL models against adversarial attacks in IoT networks.

Main Methods:

  • Data preprocessing using a min-max scaler for uniform input.
  • Optimal feature selection employing a hybrid of Coati-Grey Wolf Optimization (CGWO).
  • Adversarial attack detection using a Conditional Variational Autoencoder (CVAE), with parameter tuning via Improved Chaos African Vulture Optimization (ICAVO).

Main Results:

  • The TTOS-RAAM model demonstrated superior performance in detecting adversarial attacks.
  • Experimental analysis on the RT-IoT2022 dataset showed a high accuracy of 99.91%.
  • The proposed method significantly outperforms existing approaches in adversarial attack mitigation for IoT networks.

Conclusions:

  • The TTOS-RAAM model provides a robust solution for mitigating adversarial attacks in IoT network security.
  • The study highlights the potential of advanced optimization and DL techniques for securing IoT data.
  • The findings contribute to the ongoing research on defending intelligent systems against sophisticated cyber threats.