文章基本信息

标题：An Efficient Machine Learning Approach for Identification of Operating System Processes
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作者：Amit Kumar ; Shishir Kumar
期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2014
卷号：8
期号：6
页码：209-228
DOI：10.14257/ijseia.2014.8.6.17
出版社：SERSC
摘要：For providing security to computer systems various approaches like firewalls, anti-virus tool, network security tools, malware removal tools, monitoring tools and many more are being used in present scenario. Computer security tools available in present era need regular updating and monitoring. If any computer users do not regularly update the security tools, then the system may be infected by any virus or any other attack. In this paper a learning system is proposed to identify the operating system process as self and non-self using the concepts of Machine Learning. Three concepts of machine learning have been used to provide the efficient learning system. As a first concept the approach of Concept Learning and the general-to-specific ordering of hypotheses has been used in which Version Space has been generated using the Candidate-Elimination algorithm to provide the learning. As second concept Decision Tree Learning has been used in which ID3 algorithm has been used to construct a decision tree. As a third concept an Artificial Neural Network (ANN) has been used and this concept uses the Gradient Descent Algorithm. Finally, it has been observed that the Decision Tree and Artificial Neural Network learning are the best suited learning approach for identifying self and non-self process.
关键词：Self and Non Self Process; Machine Learning; Decision Tree; Hypothesis; ; Version Space; Artificial Neural Network