A syllabus on data mining and machine learning with applications to cybersecurity

A. Epishkina, S. Zapechnikov
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引用次数: 6

Abstract

Big data analytics are very fruitful for solving problems in cybersecurity. We have analyzed modern trends in intelligent security systems research and practice and worked out a syllabus for a new university course in the area of data mining and machine learning with applications to cybersecurity. The course is for undergraduate and graduate students studying the cybersecurity. The main objective of the course is to provide students with fundamental concepts in data mining (in particular, mining frequent patterns, associations and correlations, classification, cluster analysis, outlier detection), machine learning (including neural networks, support vector machines etc.) and related issues, e.g. the basics of multidimensional statistics. Contrary to the traditional data mining and machine learning courses we illustrate course topics by cases from the area of cybersecurity including botnet detection, intrusion detection, deep packet inspection, fraud monitoring, malware detection, phishing detection, active authentication. We note that our course has great potential for development.
关于数据挖掘和机器学习在网络安全中的应用的教学大纲
大数据分析在解决网络安全问题方面非常有成效。我们分析了智能安全系统研究和实践的现代趋势,并为数据挖掘和机器学习在网络安全领域的应用制定了一个新的大学课程大纲。本课程面向网络安全专业的本科生和研究生。本课程的主要目的是为学生提供数据挖掘的基本概念(特别是挖掘频繁模式,关联和相关性,分类,聚类分析,离群值检测),机器学习(包括神经网络,支持向量机等)和相关问题,例如多维统计的基础知识。与传统的数据挖掘和机器学习课程相反,我们通过网络安全领域的案例来说明课程主题,包括僵尸网络检测,入侵检测,深度数据包检测,欺诈监控,恶意软件检测,网络钓鱼检测,主动身份验证。我们注意到,我们的课程有很大的发展潜力。
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