A novel Machine Learning-based Network Intrusion Detection System for Software-Defined Network
A novel Machine Learning-based Network Intrusion Detection System for Software-Defined Network
复制标题
一种新颖的基于机器学习的软件定义网络网络入侵检测系统
DOI：
10.1109/nics51282.2020.9335863
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Hai
中科院分区：
文献类型：
--
作者：
Duc;Hai
网络入侵检测系统（NIDS）是许多网络系统的重要组成部分。Internet的快速发展要求NIDS在准确性和效率方面都有很大的提高。在本文中，我们提出了一个基于流的异常检测系统中应用机器学习方法在SDN网络。本文实现了一个测试平台，以实现八个特征数据集作为训练六个机器学习模型的输入。实验结果表明，该网络入侵检测系统是一种有效的SDN网络安全解决方案。
Network Intrusion Detection System (NIDS) is an important component in many network systems. The rapid development of the Internet requires NIDS to improve performance in terms of both accuracy and efficiency. In this paper, we propose a flow-based anomaly detection system in applying Machine Learning approach in a SDN network. The paper implements a testbed to achieve an eight-feature dataset as the input for training six Machine Learning models. The obtained experimental results showed that the proposed NIDS is potentially a good security solution for a SDN network.