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Comparative study of selected data mining algorithms used for intrusion detection

Authors: AJAYI Adebowale , Amarachi Anyaehie

Publication Type: Journal article

Journal: International Journal Of Soft Computing And Engineering (ijsce) 3 (3), 237-241

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Abstract

In the relatively new field of data mining and intrusion detection a lot of techniques have been proposed by various research groups. Researchers continue to find ways of optimizing and enhancing the efficiency of data mining techniques for intrusion attack classification. This paper evaluates the performance of well known classification algorithms for attack classification. The focus is on five of the most popular data mining algorithms that have been applied to intrusion detection research; Decision trees, Naïve bayes, Artificial neural network, K-nearest neighbor algorithm and Support vector machines. We discuss their advantages and disadvantages and finally we induce the NSL-KDD dataset with the respective algorithms to see how they perform.

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AJAYI,A. Amarachi,A. . (2013). Comparative study of selected data mining algorithms used for intrusion detection, 3 (), 237-237.

AJAYI,A. Amarachi,A. . "Comparative study of selected data mining algorithms used for intrusion detection" 3, no (), (2013): 237-237.

AJAYI,A. and Amarachi,A. and . (2013). Comparative study of selected data mining algorithms used for intrusion detection, 3 (), pp237-237.

AJAYIA, AmarachiA, . Comparative study of selected data mining algorithms used for intrusion detection. 2013, 3 ():237-237.

AJAYI,Adebowale , and Amarachi,Anyaehie . "Comparative study of selected data mining algorithms used for intrusion detection", 3 . (2013) : 237-237.

A.Adebowale & A.Anyaehie , "Comparative study of selected data mining algorithms used for intrusion detection" vol.3, no., pp. 237-237, 2013.

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