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Title	Q-Learning And Deep Q Networks For Securing Iot Networks, Challenges, And Solution
ID_Doc	43797
Authors	Farooq M.; Khan R.A.; Zahoor S.Z.
Year	2024
Published	Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies
DOI	http://dx.doi.org/10.1201/9781003500865-9
Abstract	The Internet of Things (IoT) is a rapidly growing network of interconnected devices that enable various applications in diverse domains, such as smart homes, smart cities, and industrial automation. However, the increased connectivity and data exchange among IoT devices also raise security concerns, as these devices are vulnerable to various types of attacks, including malware, data breaches, and unauthorized access. Therefore, ensuring the security of IoT networks is crucial to protect the privacy, integrity, and availability of IoT data and services. By leveraging the power of machine learning and artificial intelligence, the proposed approach which includes Q-learning and Deep Q networks offers a proactive and adaptive solution to the security challenges in IoT networks. It enables the network to learn from past experiences, improve its decision-making capabilities over time, and respond effectively to emerging security threats. This combination of reinforcement learning and deep neural networks presents a novel and promising avenue for enhancing the security of IoT networks. © 2025 selection and editorial matter, Inam Ullah Khan, Salma El Hajjami, Mariya Ouaissa, Salwa Belaqziz and Tarandeep Kaur Bhatia. All rights reserved.
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