异常网络入侵检测系统的深度强化学习方法

Ying-Feng Hsu, Morito Matsuoka
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引用次数: 25

摘要

网络入侵检测系统(NIDS)对于组织确保其通信和信息的安全性至关重要。本文提出了一种基于深度强化学习(DRL)的异常网络入侵检测系统。我们的系统具有自我更新的能力,以反映新型的网络流量行为。本研究包括三个主要贡献。首先,为了展示我们方法的整体适用性,我们通过两个著名的NIDS基准数据集:NSL-KDD和UNSW-NB15以及真实的校园网日志来演示我们的工作。其次,为了证明我们方法的可行性,我们将我们的方法与其他三种经典机器学习方法和两个相关的已发表结果进行了比较。第三,我们的模型能够实时处理百万规模的网络流量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Deep Reinforcement Learning Approach for Anomaly Network Intrusion Detection System
Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In this paper, we propose a deep reinforcement learning-based (DRL) for anomaly network intrusion detection system. Our system has the ability of self-updating to reflect new types of network traffic behavior. This study includes three major contributions. First, to show the overall applicability of our approach, we demonstrate our work through two well-known NIDS benchmark datasets: NSL-KDD and UNSW-NB15 and a real campus network log. Second, to show the feasibility of our approach, we compared our method with three other classic machine learning methods and two related published results. Third, our model is capable of processing a million scale of network traffic on a real-time basis.
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