• Rayees Ahmad Sheikh
• Aabid Ud Din Wani
• Abhishek Bhardwaj

There is a fast growth of increasing online systems with these more susceptible chances of intrusions in the systems or the networks can occur. Intrusions are simply intruder gains, or they always in process to gain and broke the systems very well to steal very important and sensitive information. Likewise, replicating databases and running on the pirated software. Their needs to high security models which can achieve maximum accuracy as compared to the existing classifiers. In present and future networks our day by day requirements are basically dependent on the Intrusion detection systems. Many techniques have been traditionally used in Intrusion detection but they are not so providing so much greater accuracy. In recently lot of machine learning algorithms have been used in Intrusion detection. In this paper focus will be on Extreme learning machine it will overcome the issues for large amount of data and large datasets. To study and analyze the performance of existing Intrusion detection techniques with some feature selection techniques and also implement Feature selection with Mutual Information technique and then classify selected features with ELM machine learning technique. Lastly, analyze the performance of proposed MI_ELM technique with the existing Voting technique with respect to accuracy, precision, recall, f-measure and FP rate.

Intrusion detection, Extreme Learning Machine, accuracy, Mutual information.

"An Intrusion Detection Model using Extreme Learning Machine and Group of classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.251-260, June 2019, Available :http://www.jetir.org/papers/JETIR1907F34.pdf

"An Intrusion Detection Model using Extreme Learning Machine and Group of classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp251-260, June 2019, Available at : http://www.jetir.org/papers/JETIR1907F34.pdf