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A Comparative Study of Clustering Algorithms for Detect SQL Injection Attack

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dc.contributor.author Salma Babker Mohammed
dc.contributor.author Mohamed Ahmed Elmobark
dc.contributor.author Mohammed kabashi Abd Elrhman
dc.date.accessioned 2017-07-05T07:18:07Z
dc.date.available 2017-07-05T07:18:07Z
dc.date.issued 2017-07-05
dc.identifier.uri http://repository.rsu.edu.sd/
dc.description.abstract data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Waikato environment for knowledge analysis (WEKA) is a data mining tool. It contains many machines leaning algorithms. It provides the facility to classify our data through various algorithms. In this paper we studied the various clustering algorithms to detect SQL injection attack. Our main aim is to show the comparison between the different clustering algorithms of WEKA and to find out which algorithm will be most suitable to detect SQL injection attack. k-means and Make density algorisms most suitable to detect SQL injection attack according to our result. en_US
dc.language.iso en en_US
dc.subject الاوراق العلمية en_US
dc.subject Data mining algorithms en_US
dc.subject WEKA tools en_US
dc.subject Clustering methods en_US
dc.subject SQL injection attack en_US
dc.title A Comparative Study of Clustering Algorithms for Detect SQL Injection Attack en_US
dc.type Article en_US


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