A Supervised Classification Approach for Detecting Packets Originated in a HTTP-based Botnet

Q4 Mathematics CLEI Electronic Journal Pub Date : 2013-12-01 DOI:10.19153/CLEIEJ.16.3.2
Felix Brezo, José Gaviria de la Puerta, Xabier Ugarte-Pedrero, I. Santos, P. G. Bringas
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引用次数: 8

Abstract

The possibilities that the management of a vast amount of computers and/or networks oer is attracting an increasing number of malware writers. In this document, the authors propose a methodology thought to detect malicious botnet trac, based on the analysis of the packets that ow within the network. This objective is achieved by means of the extraction of the static characteristics of packets, which are lately analysed using supervised machine learning techniques focused on trac labelling so as to proactively face the huge volume of information nowadays lters work with.
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基于http的僵尸网络中数据包检测的监督分类方法
管理大量计算机和/或网络的可能性正在吸引越来越多的恶意软件编写者。在本文档中,作者提出了一种基于对网络中传输的数据包的分析来检测恶意僵尸网络跟踪的方法。这一目标是通过提取数据包的静态特征来实现的,这些特征最近使用集中在跟踪标签上的监督机器学习技术进行分析,以便主动面对如今信件处理的大量信息。
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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来源期刊
CLEI Electronic Journal
CLEI Electronic Journal Computer Science-Computer Science (miscellaneous)
CiteScore
0.70
自引率
0.00%
发文量
18
审稿时长
40 weeks
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