Generating Labeled Flow Data from MAWILab Traces for Network Intrusion Detection

Jinoh Kim, Caitlin Sim, Jinhwan Choi
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引用次数: 9

Abstract

A growing issue in the modern cyberspace world is the direct identification of malicious activity over network connections. The boom of the machine learning industry in the past few years has led to the increasing usage of machine learning technologies, which are especially prevalent in the network intrusion detection research community. When utilizing these fairly contemporary techniques, the community has realized that datasets are pivotal for identifying malicious packets and connections, particularly ones associated with information concerning labeling in order to construct learning models. However, there exists a shortage of publicly available, relevant datasets to researchers in the network intrusion detection community. Thus, in this paper, we introduce a method to construct labeled flow data by combining the packet meta-information with IDS logs to infer labels for intrusion detection research. Specifically, we designed a NetFlow-compatible format due to the capability of a a large body of network devices, such as routers and switches, to export NetFlow records from raw traffic. In doing so, the introduced method at hand would aid researchers to access relevant network flow datasets along with label information.
从MAWILab轨迹生成标记流数据用于网络入侵检测
在现代网络世界中,一个日益严重的问题是通过网络连接直接识别恶意活动。过去几年机器学习行业的蓬勃发展导致机器学习技术的使用越来越多,这在网络入侵检测研究界尤为普遍。当利用这些相当现代的技术时,社区已经意识到数据集对于识别恶意数据包和连接至关重要,特别是与标签信息相关的数据集,以便构建学习模型。然而,对于网络入侵检测领域的研究人员来说,缺乏公开可用的相关数据集。因此,本文提出了一种将数据包元信息与入侵检测日志相结合来构造标记流数据的方法,用于入侵检测研究。具体来说,我们设计了一种NetFlow兼容的格式,因为大量网络设备(如路由器和交换机)能够从原始流量中导出NetFlow记录。在这样做的过程中,所介绍的方法将帮助研究人员访问相关的网络流量数据集以及标签信息。
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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