On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks
On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks
复制标题
软件定义网络中用于入侵检测的所选机器学习算法的效率
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. Amanowicz
中科院分区：
文献类型：
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作者：
Damian Jankowski;M. Amanowicz
我们提出了使用软件定义网络（SDN）技术和机器学习算法来监控和检测SDN数据平面中的恶意活动的概念。网络流量的统计和特征由SDN技术的原生机制生成。为了进行测试和验证这一概念，有必要获得一组网络工作负载测试数据。我们提出了虚拟环境，使生成的SDN网络流量。本文研究了所选机器学习方法的效率：自组织映射和学习矢量量化及其增强版本。并与其他基于SDN的入侵检测系统进行了比较。
We propose a concept of using Software Defined Network (SDN) technology and machine learning algorithms for monitoring and detection of malicious activities in the SDN data plane. The statistics and features of network traffic are generated by the native mechanisms of SDN technology. In order to conduct tests and a verification of the concept, it was necessary to obtain a set of network workload test data. We present virtual environment which enables generation of the SDN network traffic. The article examines the efficiency of selected machine learning methods: Self Organizing Maps and Learning Vector Quantization and their enhanced versions. The results are compared with other SDN-based IDS.