使用机器学习方法的僵尸网络检测特征表示

P. C. Tikekar, S. Sherekar, V. Thakre
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引用次数: 0

摘要

在过去的十年中,僵尸网络已经成为一种日益增长的新兴威胁,并受到研究人员的欢迎。僵尸网络检测是一项非常具有挑战性的任务,因此人们在开发有效和高效的僵尸网络检测技术方面进行了大量的科学研究。为了开发僵尸网络检测技术,大多数研究人员使用机器学习技术。有时由于僵尸网络的C&C性质和不同类型机器人的各种特征,识别僵尸网络变得具有挑战性。本文研究和分析了僵尸网络在机器学习技术中负责检测的多个特征。本文讨论了僵尸网络的各种特征,包括它们的类型、流量参数、数据库以及测试结果所必需的僵尸网络检测方法参数。研究人员需要分析现有的僵尸网络检测技术及其数据库和参数,以开发更好的检测技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Features Representation of Botnet Detection Using Machine Learning Approaches
Over the past ten years, Botnet has been an emerging threat that is increasing day by day & has gained popularity amongst researchers. Botnet detection is a very challenging task, so great Scientific research efforts have been made to develop effective & efficient techniques to detect the presence of Botnet. For developing the Botnet detection technique, most of the researchers use machine learning. Sometimes due to the C&C nature of Botnet & various characteristics of different types of bots, it becomes challenging to identify the Botnet. This paper studies & analyze multiple features of Botnet in machine learning techniques responsible for the detection. The paper discusses various Botnet features with their type, traffic parameters, databases, and the Botnet Detection method's parameters essential to test the results. The researcher needs to analyze the existing Botnet detection technique with its databases & parameters to develop a better detection technique.
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