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Deep Reinforcement Learning for Cybersecurity Applications

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.10, No. 12)

Publication Date:

Authors : ;

Page : 32-38

Keywords : Cybersecurity; Reinforcement Algorithms; Machine Learning; Cyber-threats; Supervised learning; unsupervised learning;

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There has been a rapid growth of the devices connected to the internet in the last decade for the various internet (IoT) of things applications. The increase of these smart devices has posed a great security concern in the internet of things ecosystem. The internet of things ecosystem must be protected from these threats. Reinforcement learning has been proposed by the cybersecurity professionals to provide the needed security tools for securing the IoT system since it is able to interact with the environment and learn how to detect the threats. This paper presents a comprehensive research on cybersecurity threats to the IoT system applications. The RL algorithms are also presented to understand the attacks on the IoT. Reinforcement learning is widely employed in cybersecurity because it can learn on its own experience by investigating and capitalizing on the unknown ecosystem, this enables it solve many complex problems. The RL capabilities on dealing with cybercrime challenges are also exploited in this paper.

Last modified: 2021-12-30 17:24:22