Soft computing based imputation and hybrid data and text mining: The case of predicting the severity of phishing alerts

Authors:
Kancherla Jonah Nishanth;Vadlamani Ravi;Narravula Ankaiah;Indranil Bose
Affiliations:
Institute for Development and Research in Banking Technology (IDRBT), Castle Hills Road #1, Masab Tank, Hyderabad-500 057, AP, India;Institute for Development and Research in Banking Technology (IDRBT), Castle Hills Road #1, Masab Tank, Hyderabad-500 057, AP, India;Institute for Development and Research in Banking Technology (IDRBT), Castle Hills Road #1, Masab Tank, Hyderabad-500 057, AP, India;Indian Institute of Management Calcutta, Diamond Harbour Road Joka, Kolkata 700 104, West Bengal, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

In this paper, we employ a novel two-stage soft computing approach for data imputation to assess the severity of phishing attacks. The imputation method involves K-means algorithm and multilayer perceptron (MLP) working in tandem. The hybrid is applied to replace the missing values of financial data which is used for predicting the severity of phishing attacks in financial firms. After imputing the missing values, we mine the financial data related to the firms along with the structured form of the textual data using multilayer perceptron (MLP), probabilistic neural network (PNN) and decision trees (DT) separately. Of particular significance is the overall classification accuracy of 81.80%, 82.58%, and 82.19% obtained using MLP, PNN, and DT respectively. It is observed that the present results outperform those of prior research. The overall classification accuracies for the three risk levels of phishing attacks using the classifiers MLP, PNN, and DT are also superior.