IJBTT

Performance Analysis of various classifiers using Benchmark Datasets in Weka tools

	International Journal of Engineering Trends and Technology (IJETT)
	© 2017 by IJETT Journal
	Volume-47 Number-5
	Year of Publication : 2017
	Authors : Nishi Rani, Ravindra Kr. Purwar
	DOI :  10.14445/22315381/IJETT-V47P247

Citation 

Nishi Rani, Ravindra Kr. Purwar "Performance Analysis of various classifiers using Benchmark Datasets in Weka tools", International Journal of Engineering Trends and Technology (IJETT), V47(5),290-294 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Intrusion occurs in the network due to redundant and irrelevant data that cause problem in network traffic classification. These kinds of data slow down the network and create difficulties in detecting cyber attacks. Intrusion detection system monitors the network for malicious activities. For network intrusion detection many data mining and machine learning techniques exist in literature but their efficiency has always remain a challenge. In this paper, various classification techniques of weka tool have been studied over a number of datasets like KDD cup 99 dataset, NSL KDD dataset and Kyoto 2006 dataset which can reflect current network stages. KDD cup 99 dataset contains 42 features and can classify intrusion in five classes, NSL KDD is the filtered version of KDD and able to classify intrusion in two classes and Kyoto 2006 data set contains labels as normal (no attack), attack (known attack) and unknown attack which can reflect current stages of the network. We have analyzed the performance of the classification techniques with respect to time and accuracy.

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Keywords
Intrusion detection, KDD cup 99 dataset, NSL KDD dataset, Kyoto 2006 dataset, weka.