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Title	The Graph Neural Network With Wasserstein Generative Adversarial Network For Botnet Detection In Smart City Iot
ID_Doc	55625
Authors	Thota M.K.; Prathibhavani P.M.; Venugopal K.R.
Year	2024
Published	2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
DOI	http://dx.doi.org/10.1109/ICCCNT61001.2024.10724763
Abstract	One of the main risks to the security and stability of Internet of Things (IoT) networks in smart cities is botnet assaults. The identification of complex and emerging botnet attacks has not been entirely contained by traditional methods of botnet detection. In this study, we present a new hybrid approach, called GNN-WGAN, to efficiently detect bots in IoT-based smart city networks by integrating Graph Neural Network and Wasserstein Generative Adversarial Network. The suggested method, which ultimately aims to improve the precision and robustness against botnet detection in dynamic IoT networks, effectively uses WGANs to generate synthetic botnet traffic patterns to add more training data and GNNs to capture dependencies between and interactions across network topology. The experimental results demonstrate the effectiveness of the GNN-WGAN technique in accurately recognizing botnet activities, which enhances the security and resilience of IoT networks in smart cities. © 2024 IEEE.
Author Keywords	Cyber Attacks; Graph Neural Networks; Internet of Things; Smart Cities; Wasserstein Generative Adversarial Networks

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