Reinforcement Learning Applications in Cyber Security: A Review

Emine Cengi̇z, Murat Gök
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引用次数: 2

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.
强化学习在网络安全中的应用综述
在我们生活的现代时代,互联网已经成为我们日常生活中必不可少的一部分。我们的很大一部分个人数据存储在网上,组织在网上开展业务。此外,随着互联网的发展,我们家庭和工作场所中的许多设备,如自主系统、投资组合工具和娱乐工具,已经或正在变得智能化。与此同时,旨在破坏智能系统的网络攻击也日益增多。随着网络攻击手段变得越来越复杂,攻击者造成的损害也呈指数级增长。传统的计算机算法可能不足以抵御虚拟世界中的这些攻击。因此,需要基于人工智能的方法。强化学习(RL)是一种机器学习方法,被用于网络安全领域。虽然RL在网络安全方面是一个新的文献课题,但研究是为了预测、预防和阻止攻击。在本研究中;本文综述了网络安全领域中RL的渗透测试、入侵检测系统(IDS)和网络攻击的相关文献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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