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Title	An Agile Solution For Enhancing Cybersecurity Attack Detection Using Deep Learning Privacy-Preservation In Iot-Smart City
ID_Doc	7444
Authors	Jaganraja V.; Srinivasan R.
Year	2025
Published	Wireless Networks, 31, 3
DOI	http://dx.doi.org/10.1007/s11276-024-03876-1
Abstract	The rapid evolution of cyber threats in IoT-based smart cities necessitates advanced detection methods beyond traditional security measures. Existing models lack adaptability to emerging attacks and often neglect user privacy. This research addresses the gap by developing a robust deep learning framework that ensures both security and privacy. This work proposes an adaptable approach that uses deep learning techniques while also respecting user privacy in order to improve the detection of cyberattacks. The solution employs a novel strategy of training using several distributions, making the model more tolerant to shifting attack. Deep attention networks can efficiently capture complex patterns in Internet of Things (IoT) traffic data. The Whale Optimization Algorithm is used to increase convergence and performance by optimizing the proposed model. Because of the deployment of privacy-preserving technology, users may be confident that their IoT data is secure. The Proposed Deep Attention Network (DAN) has been shown in experimental assessments to outperform state-of-the-art solutions for attack detection accuracy while keeping private data private. This research contributes to the improvement of cybersecurity in IoT-based smart cities, resulting in more secure and reliable urban environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Author Keywords	Agile solution; Attack detection; Cybersecurity; Deep learning; IoT-based smart city

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