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Performance Analysis of Machine Learning Techniques for Intrusion Detection

Aftab Ahmad Malik, Muhammad Bilal Butt, Rabia Aslam Khan in Security

International Journal of Applied Information Systems
Year of Publication: 2019
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Aftab Ahmad Malik, Muhammad Bilal Butt, Rabia Aslam Khan
10.5120/ijais2019451817
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  1. Aftab Ahmad Malik, Muhammad Bilal Butt and Rabia Aslam Khan. Performance Analysis of Machine Learning Techniques for Intrusion Detection. International Journal of Applied Information Systems 12(23):12-19, August 2019. URL, DOI BibTeX

    @article{10.5120/ijais2019451817,
    	author = "Aftab Ahmad Malik and Muhammad Bilal Butt and Rabia Aslam Khan",
    	title = "Performance Analysis of Machine Learning Techniques for Intrusion Detection",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August, 2019",
    	volume = 12,
    	number = 23,
    	month = "August",
    	year = 2019,
    	issn = "2249-0868",
    	pages = "12-19",
    	url = "http://www.ijais.org/archives/volume12/number23/1062-2019451817",
    	doi = "10.5120/ijais2019451817",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

During the recent years, there has been tremendous development in the area of Computer Networks. This paper deals with the important area that is performance analysis of techniques used in machine learning. One of the major problems in Network Security is “intrusion detection system”, which is software, remains active during processing. The intrusion detection system helps in monitoring computers and computer networks, vulnerabilities or malicious activities. The attacks or malicious activities censor information and then corrupt the system networking protocols. In this paper, different machine learning techniques and their performance are compared and discussed. How machine learning techniques can ideally help in developing efficient “Intrusion detection system”.

Reference

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Keywords

Machine Learning Algorithm, Security, weka, Classification, Intrusion Detection, Decision tree