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Intrusion Detection System Using Deep Learning for Software Defined Networks (SDN)

机译：使用深度学习的软件定义网络入侵检测系统（SDN）

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摘要

The new emerging area Software Defined Networking (SDN) is one of the propitious solutions to build and run the network dynamically. The distinctive architecture of Software Defined Network introduces many security challenges which demand better security mechanisms. Emerging of different and unknown types of attacks and to identify these attacks types using the anomaly detection method is a big challenging task in SDN. Intrusion Detection System (IDS) performs network traffic scanning to construct intelligence detection of promising network attacks. Many researchers proposed Machine Learning based IDS to detect the intrusion and now they are moving towards Deep Learning to achieves better accuracy. Deep Learning has a capability to provide a significant level of conceptual information through progressively consolidating basic features layer by layer into complex features. In this paper, we proposed an anomaly-based network intrusion detection system using the deep learning approach for SDN to detect different and unknown types of attacks.
机译：新出现的新兴领域软件定义网络（SDN）是动态构建和运行网络的有利解决方案之一。软件定义网络的独特体系结构带来了许多安全挑战，需要更好的安全机制。出现各种未知类型的攻击并使用异常检测方法识别这些攻击类型是SDN中一项艰巨的任务。入侵检测系统（IDS）执行网络流量扫描，以构建对有前途的网络攻击的智能检测。许多研究人员提出了基于机器学习的IDS来检测入侵，现在他们正朝着深度学习迈进，以实现更高的准确性。深度学习具有通过逐步将基本功能逐步整合为复杂功能来提供重要级别的概念信息的能力。在本文中，我们提出了一种基于深度学习的基于异常的网络入侵检测系统，用于SDN来检测不同类型和未知类型的攻击。

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