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Title	A Novel Explainable Artificial Intelligence-Based Deep Reinforcement Learning For Secured Smart City Applications
ID_Doc	3350
Authors	Sharma V.; Seetharaman T.; Mohammed Essam K.; Alkhayyat A.
Year	2023
Published	Lecture Notes in Networks and Systems, 787 LNNS
DOI	http://dx.doi.org/10.1007/978-981-99-6550-2_9
Abstract	In recent times, smart cities have acquired tremendous transition toward sustainable development. However, with growing advancements, there comes numerous security challenges. With the rapid technological advancements, there comes greater connectivity between devices giving rise to a plethora of data and security constraints. Since data is continuously generated and transmitted across the smart city applications, preserving security measures has become a vital factor. Conventionally, intrusion detection systems are widely used to monitor and preserve the security parameters across smart cities. One major challenge with the traditional approaches is that most of the security methods described are supervised classifier and they require high-quality labels. But, however, real-time applications mainly deals with unlabeled data. Further, it lacks in explainability of the predictive model. In order to overcome these constraints, in this paper, we define an novel explainable deep reinforcement learning techniques for securing smart city applications. The proposed approach actively prevents various security threats across smart cities and the experimental results provide improved explainability, stability, security, and efficiency measures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Deep reinforcement learning; Explainable artificial intelligence; Security; Smart city

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