Extraction of Features and Classification on Phishing Websites using Web Mining Techniques

  Nandhini.S,  Dr.V.Vasanthi

Website Phishing is serious web security problem that involves mirroring genuine websites to deceive online users in order to steal their sensitive information. Phishing can be seen as a typical classification problem in data mining where the classifier is constructed from large number of website’s features. There are high demands on identifying the best set of features that when mined the predictive accuracy of the classifiers is enhanced. This research work investigates features selection aiming to determine the effective set of features in terms of classification performance. We compare features selection and classification methods in order to determine the least set of features of phishing detection using data mining. Experimental tests on large number of features data set have been done using Information Gain and Correlation Features set methods. Further, five data mining algorithms Naïve Bayes, KNN, Random Forest, SVM and j48 have been used to classify the web phishing data set, analyse the results and identify the efficient technique to classify the web page phishing data set.

Keywords- Website Phishing, Classification, Feature Selection, Web Security, Web Mining

Unique Identification Number - IJEDR1704198
Page Number(s) - 1215-1225
Pubished in - Volume 5 | Issue 4 | December 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Nandhini.S,  Dr.V.Vasanthi,   "Extraction of Features and Classification on Phishing Websites using Web Mining Techniques", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 4, pp.1215-1225, December 2017, Available at :http://www.ijedr.org/papers/IJEDR1704198.pdf