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Title	Malicious Attack Detection In Iot By Generative Adversarial Networks
ID_Doc	36207
Authors	Bethu S.
Year	2025
Published	SN Computer Science, 6, 4
DOI	http://dx.doi.org/10.1007/s42979-025-03874-w
Abstract	The rapid expansion of the Internet of Things (IoT) has led to its widespread adoption across various domains, including smart cities, industry, and agriculture. IoT systems consist of billions of interconnected devices capable of sending and receiving data over the Internet, making them vulnerable to cyberattacks that compromise their security. To address this challenge, we propose a Malicious Attack Detector (MAD) designed to safeguard IoT systems against such threats. The MAD system leverages a GAN-based model to effectively learn from poisoned datasets and detect malicious activities. In this work, we evaluate the accuracy of the discriminator within the GAN framework across multiple training epochs, achieving a notable accuracy of 95.4%, as demonstrated in the results section. Furthermore, we discuss the latest risks associated with IoT systems and present potential solutions to mitigate malicious activities. This study underscores the effectiveness of the proposed model in enhancing the security of IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Author Keywords	Deep Learning; GAN; IoT; Malicious Attack Detector (MAD)

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