Abstract
Upcoming technological innovations and findings are immense in the field of intelligent transport systems (ITS). Cyber-physical systems (CPSs) are complex systems that integrate communication, control, and computing technology. CPSs are widely used today in intelligent transportation. For every development of a new system, there is a parallel offender who initiates the attack to destroy the root of the system developed. The attack category is infinite in such developing technology. In this paper, we have focused on preserving the security of ITS from cyber-physical systems perceptive, and various vulnerabilities, attacks, and countermeasures against ITS. Reinforcement learning is the latest buzzword in which an agent can understand and explain the environment, perform an action, and learn through trial and error. The paper also explains how reinforcement learning helps ITS in terms of security.
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https://towardsdatascience.com/applying-of-reinforcement-learning-for-self-driving-cars-8fd87b255b81
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This article is part of the topical collection “Industrial IoT and Cyber-Physical Systems” guest edited by Arun K Somani, Seeram Ramakrishnan, Anil Chaudhary and Mehul Mahrishi.
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Pavithra, R., Kaliappan, V.k. & Rajendar, S. Security Algorithm for Intelligent Transport System in Cyber-Physical Systems Perceptive: Attacks, Vulnerabilities, and Countermeasures. SN COMPUT. SCI. 4, 544 (2023). https://doi.org/10.1007/s42979-023-01897-9
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DOI: https://doi.org/10.1007/s42979-023-01897-9