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Adaptive Multiobjective Evolutionary Generative Adversarial Network for Metaverse Network Intrusion Detection.

Dikai Xu1,2, Bin Cao1,2

  • 1State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China.

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The adaptive multiobjective evolutionary generative adversarial network (AME-GAN) enhances cybersecurity for Metaverse and Internet of Things (IoT) devices. This novel approach improves intrusion detection accuracy and real-time performance against evolving cyber threats.

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Intrusion Detection

Background:

  • The convergence of Metaverse and Internet of Things (IoT) creates significant cybersecurity vulnerabilities, including data breaches and device tampering.
  • Existing intrusion detection systems struggle to address emerging cyber threats in dynamic Metaverse environments.
  • Manual design of neural networks for intrusion detection is time-consuming and often results in suboptimal performance.

Purpose of the Study:

  • To propose a novel, scalable solution, the adaptive multiobjective evolutionary generative adversarial network (AME-GAN), for optimizing network intrusion detection in the Metaverse.
  • To address the critical gap in cybersecurity for Metaverse devices often overlooked by traditional methods.
  • To enhance the accuracy, real-time performance, and model diversity of intrusion detection systems for diverse hardware constraints.

Main Methods:

  • Development of an inversely proportional hybrid attention-based long short-term memory GAN to generate minority class samples and address data imbalance.
  • Implementation of an adaptive evolutionary neural architecture search algorithm to guide GAN supernet mutation and improve training stability.
  • Integration of a double mutation multiobjective evolutionary neural architecture search algorithm to optimize accuracy, real-time performance, and model diversity.

Main Results:

  • AME-GAN demonstrated superior performance on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets compared to state-of-the-art approaches.
  • Achieved improvements of 0.32% in accuracy, 0.31% in F1 score, 0.47% in precision, and 0.37% in recall.
  • The proposed framework offers enhanced detection performance and real-time applicability for Metaverse cybersecurity.

Conclusions:

  • AME-GAN provides a promising, adaptive framework for bolstering cybersecurity in the Metaverse.
  • The study contributes to advancing network intrusion detection for next-generation digital environments.
  • The developed approach effectively tackles data imbalance and optimizes diverse performance metrics crucial for Metaverse applications.