Network Traffic Image Dataset Generation from PCAP files for Evaluating Performance of Machine Learning Models

S. Swathi, G. Lakshmeeswari
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引用次数: 1

Abstract

Detection of network attack traffic in network environments is majorly studied in the literature by applying various data mining and machine learning techniques. The existing studies which applied data and machine learning techniques consider network traffic instances in either pcap or csv representations. In the current contribution, the basic idea is to use network traffic images which are obtained from pcap representations. These generated network traffic images are then used to design and build efficient machine learning models. Another advantage of representing the network traffic in the form of images is that these images can be used to evaluate the computational performance of deep learning models also. Till date, the existing machine learning studies on cloud network traffic attack detection and network environments did not apply network traffic images to build machine learning models but only applied to build deep learning models which form the motivation for the present research. We propose to use the network traffic images for designing new machine learning models.
从PCAP文件生成用于评估机器学习模型性能的网络流量图像数据集
文献中主要通过应用各种数据挖掘和机器学习技术来研究网络环境中网络攻击流量的检测。应用数据和机器学习技术的现有研究考虑了pcap或csv表示的网络流量实例。在目前的贡献中,基本思想是使用从pcap表示中获得的网络流量图像。然后使用这些生成的网络流量图像来设计和构建高效的机器学习模型。以图像形式表示网络流量的另一个优点是,这些图像也可以用来评估深度学习模型的计算性能。到目前为止,现有的云网络流量攻击检测和网络环境的机器学习研究并没有应用网络流量图像来构建机器学习模型,而只是应用于构建深度学习模型,这也是本研究的动力所在。我们建议使用网络流量图像来设计新的机器学习模型。
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