Deep Reinforcement Learning Approach for Cyberattack Detection

Imad Tareq, B. Elbagoury, S. El-Regaily, El-Sayed M. El-Horbaty
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Abstract

Recently, there has been a growing concern regarding the detrimental effects of cyberattacks on both infrastructure and users. Conventional safety measures, such as encryption, firewalls, and intrusion detection, are inadequate to safeguard cyber systems against emerging and evolving threats. To address this issue, researchers have turned to reinforcement learning (RL) as a potential solution for complex decision-making problems in cybersecurity. However, the application of RL faces various obstacles, including a lack of suitable training data, dynamic attack scenarios, and challenges in modeling real-world complexities. This paper suggests applying deep reinforcement learning (DRL), a deep framework, to simulate malicious cyberattacks and enhance cybersecurity. Our framework utilizes an agent-based model that is capable of continuous learning and adaptation within a dynamic network security environment. The agent determines the most optimal course of action based on the network’s state and the corresponding rewards received for its decisions. We present the outcomes of our experimentation with the application of DRL on a specific model, double deep Q-network (DDQN), utilizing policy gradient (PG) on three distinct datasets: NSL-KDD, CIC-IDS-2018, and AWID. Our research demonstrates that DRL can effectively improve cyberattack detection outcomes through our model and specific parameter adjustments.
网络攻击检测的深度强化学习方法
最近,人们越来越关注网络攻击对基础设施和用户造成的有害影响。传统的安全措施,如加密、防火墙和入侵检测,不足以保护网络系统免受新出现和不断演变的威胁。为解决这一问题,研究人员将强化学习(RL)作为网络安全复杂决策问题的潜在解决方案。然而,强化学习的应用面临着各种障碍,包括缺乏合适的训练数据、动态攻击场景以及对现实世界复杂性建模的挑战。本文建议应用深度强化学习(DRL)这一深度框架来模拟恶意网络攻击并增强网络安全。我们的框架采用基于代理的模型,该模型能够在动态网络安全环境中不断学习和适应。代理根据网络状态和其决策所获得的相应奖励确定最优行动方案。我们介绍了 DRL 在特定模型--双深度 Q 网络(DDQN)--上的应用实验结果,并在三个不同的数据集上利用了策略梯度(PG):NSL-KDD、CIC-IDS-2018 和 AWID。我们的研究表明,通过我们的模型和特定参数调整,DRL 可以有效改善网络攻击检测结果。
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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