Abstract
In the modern age we live in, the internet has become an essential part of our daily life. A significant portion of our personal data is stored online and organizations run their business online. In addition, with the development of the internet, many devices such as autonomous systems, investment portfolio tools and entertainment tools in our homes and workplaces have become or are becoming intelligent. In parallel with this development, cyberattacks aimed at damaging smart systems are increasing day by day. As cyberattack methods become more sophisticated, the damage done by attackers is increasing exponentially. Traditional computer algorithms may be insufficient against these attacks in the virtual world. Therefore, artificial intelligence-based methods are needed. Reinforcement Learning (RL), a machine learning method, is used in the field of cyber security. Although RL for cyber security is a new topic in the literature, studies are carried out to predict, prevent and stop attacks. In this study; we reviewed the literature on RL's penetration testing, intrusion detection systems (IDS) and cyberattacks in cyber security.
Publisher
Sakarya University Journal of Science
Reference87 articles.
1. [1] B. von Solms, R. von Solms, “Cybersecurity and information security – what goes where?,” Information & Computer Security, vol. 26, no. 1, pp. 2–9, 2018.
2. [2] Z. Guan, J. Li, L. Wu, Y. Zhang, J. Wu, X. Du, “Achieving efficient and secure data acquisition for cloud-supported internet of things in smart grid,” IEEE Internet Things Journal, vol. 4, no. 6, pp. 1934–1944, 2017.
3. [3] J.-H. Li, “Cyber security meets artificial intelligence: a survey,” Frontiers of Information Technology & Electronic Engineering, vol. 19, no. 12, pp. 1462–1474, 2018.
4. [4] T. T. Nguyen, V. J. Reddi, “Deep reinforcement learning for cyber security,” arXiv [cs.CR], 2019.
5. [5] N. D. Nguyen, T. T. Nguyen, H. Nguyen, D. Creighton, S. Nahavandi, “Review, analysis and design of a comprehensive deep reinforcement learning framework,” arXiv [cs.LG], 2020.
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