Adaptive Multiobjective Evolutionary Generative Adversarial Network for Metaverse Network Intrusion Detection - Dikai Xu; Bin Cao (Research)
The convergence of the Metaverse and the Internet of Things (IoT) paves the way for extensive data interaction between connected devices and digital twins; however, this simultaneously introduces considerable cybersecurity threats, including data breaches, ransomware, and device tampering. Existing intrusion detection algorithms struggle to effectively defend against emerging cyberattacks in the rapidly evolving Metaverse environment. Designing effective neural networks for intrusion detection algorithms relies heavily on expert experience, making the manual process time-consuming and often yielding suboptimal results. This paper addresses a critical gap in cybersecurity for Metaverse devices, which are often overlooked in traditional detection methods, and proposes an adaptive multiobjective evolutionary generative adversarial network (AME-GAN) as a novel, scalable solution for optimizing network intrusion detection. An inversely proportional hybrid attention-based long short-term memory GAN is proposed, combining GANs to generate minority class samples and alleviate the imbalance problem in training datasets, which has long hindered accurate intrusion detection. Additionally, an adaptive evolutionary neural architecture search algorithm for the supernet of the GAN is designed to guide the mutation direction of the supernet, enhancing the training stability. This paper further introduces a double mutation multiobjective evolutionary neural architecture search algorithm, integrating both the multiobjective evolutionary algorithm and the neural architecture search to optimize accuracy, real-time performance, and model diversity—a crucial aspect for Metaverse devices with diverse hardware constraints. Experiments conducted on 3 well-known datasets—NSL-KDD, UNSW-NB15, and CIC-IDS2017—demonstrate that AME-GAN outperforms state-of-the-art approaches, with improvements of 0.32% in accuracy, 0.31% in F1 score, 0.47% in precision, and 0.37% in recall. This paper offers a promising, adaptive framework to enhance cybersecurity in the Metaverse, improving detection performance and real-time applicability, and contributing to the future of network intrusion detection in next-generation digital environments.