An Outlook on using Packet Sampling in Flow-based C2 TLS Malware Traffic Detection

Carlos Novo, J. M. Silva, Ricardo Morla
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引用次数: 1

Abstract

Packet sampling plays an important role in keeping storage and processing requirements at a manageable level in network management. However, because it reduces the amount of available information, it can also reduce the performance of some related tasks, such as detecting security events. In this context, this work explores how packet sampling impacts machine learning-based tasks, in particular, flow-based C2 TLS malware traffic detection using a deep neural network. Based on a proposed lightweight sampling scheme, the ongoing results show a small reduction in classification accuracy compared with analysing all the traffic, while reducing in 10 fold the number of packets processed.
基于流量的C2 TLS恶意流量检测中数据包采样技术的展望
在网络管理中,包采样在保证存储和处理需求处于可管理的水平上起着重要的作用。但是,由于它减少了可用信息的数量,因此也会降低一些相关任务的性能,例如检测安全事件。在此背景下,本工作探讨了数据包采样如何影响基于机器学习的任务,特别是使用深度神经网络进行基于流量的C2 TLS恶意软件流量检测。基于提出的轻量级采样方案,正在进行的结果表明,与分析所有流量相比,分类精度略有降低,而处理的数据包数量减少了10倍。
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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