Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

March Edition 2023
International Journal of Applied Information Systems solicits high quality original research papers for the March 2023 Edition of the journal. The last date of research paper submission is February 15, 2023.

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
Series: Volume 12 Number 23
Authors:Aftab Ahmad Malik, Muhammad Bilal Butt, Rabia Aslam Khan
10.5120/ijais2019451817
Download full text

  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

  1. N. F. Haq, “Application of Machine LearningApproaches in Intrusion Detection System?: A Survey,” vol. 4, no. 3, pp. 9–18, 2015.
  2. S. Juma, Z. Muda, M. A. Mohamed, and W. Yassin, “MACHINE LEARNING TECHNIQUES FOR INTRUSION DETECTION SYSTEM?: A REVIEW,” vol. 72, no. 3, 2015.
  3. A. A. Shah, “Analysis of Machine Learning Techniques for Intrusion Detection System?: A Analysis of Machine Learning Techniques for Intrusion Detection System?: A Review,” no. June, 2015.
  4. M. Zamani, “Machine Learning Techniques for Intrusion Detection,” no. December 2013, 2014.
  5. B. Zhang, “Network Intrusion Detection Method Based on PCA and Bayes Algorithm,” vol. 2018, 2018.
  6. R. R. Chaudhari and S. P. Patil, “A Study on Data Mining & Machine Learning for Intrusion Detection System,” vol. 6, no. 2, pp. 114–118, 2017.
  7. U. R. Salunkhe, “Security Enrichment in Intrusion Detection System Using Classifier Ensemble,” vol. 2017, 2017.
  8. M. Alkasassbeh and M. Almseidin, “Machine Learning Methods for Network Intrusion Detection,” no. October, 2018.
  9. Shaoqiang Wang, DongSheng Xu , ShiLiang Yan “Analysis and application of Wireshark in TCP/IP protocol teaching”, Publisher: IEEE
  10. Mark Hall, Eibe Frank etal (2009), “WEKA data Mining Software”, ACM SIGKDD Explorations, Volume 11 Issue 1, ACM New York, NY, USA doi>10.1145/1656274.1656278

Keywords

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