Detection and Classification of DDos Attack on Software Defined Network

Irma Anggraeni, Dinar Munggaran Akhmad
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Abstract

Software-Defined Networking (SDN) is a new network paradigm that changes network architecture. However, it turns out that the SDN network also has several issues, one of which is security. The higher the traffic on the network, the higher the possibility of security threats that will occur. Therefore, it is necessary to detect attacks that might occur on this SDN network. This study will detect attacks on the SDN network with the stages carried out, namely the process of building an SDN architecture using a mininet emulator, then network simulation according to the topology, retrieval of traffic data using wireshark and performing data analysis using the Weka application on the NSL KDD dataset. The results of this research found that a DDos attack a ping of death is an attack that sends messages continuously to the recipient, causing the computer to crash, then analysis of attack classification data is carried out using a dataset to compare machine learning algorithms that have high accuracy was Random Forests. The targeted output in this research is published in the Journal of Computing.
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基于软件定义网络的DDos攻击检测与分类
软件定义网络(SDN)是一种改变网络体系结构的新型网络范式。然而,事实证明,SDN网络也存在几个问题,其中之一就是安全性。网络上的流量越大,出现安全威胁的可能性就越高。因此,有必要对该SDN网络中可能发生的攻击进行检测。本研究将检测针对SDN网络的攻击,分阶段进行,即使用mininet模拟器构建SDN架构,然后根据拓扑进行网络仿真,使用wireshark检索流量数据,并使用Weka应用程序对NSL KDD数据集进行数据分析。本研究结果发现,DDos攻击a ping of death是一种连续向接收者发送消息的攻击,导致计算机崩溃,然后使用数据集对攻击分类数据进行分析,将具有较高准确率的机器学习算法与Random Forests进行比较。这项研究的目标产出发表在《计算机杂志》上。
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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