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