Soft computing based imputation and hybrid data and text mining: The case of predicting the severity of phishing alerts |
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| Authors: | Kancherla Jonah Nishanth Vadlamani Ravi Narravula Ankaiah Indranil Bose |
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| Affiliation: | 1. Institute for Development and Research in Banking Technology (IDRBT), Castle Hills Road #1, Masab Tank, Hyderabad-500 057, AP, India;2. Indian Institute of Management Calcutta, Diamond Harbour Road Joka, Kolkata 700 104, West Bengal, India;1. Institute of Chemistry, Far East Branch of RAS, 159, Prosp. 100-letiya Vladivostoka, Vladivostok 690022, Russia;2. I.Ya. Postovsky Institute of Organic Synthesis, Ural Branch of RAS, 20, S. Kovalevskoy street, Yekaterinburg 620990, Russia;2. DIMA, University of Genova, Italy;1. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;2. Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA;3. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA;4. School of Energy and Traffic Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;1. Crustal Geophysics and Geochemistry Science Center, United States Geological Survey, Denver Federal Center, Box 25046, MS 964, Lakewood, Colorado 80225, United States;2. Center for Computational and Mathematical Biology, University of Colorado, Campus Box 170, PO Box 173364, Denver, CO 80217-3364, United States;1. University of Petra, MIS Department, Jordan;2. The World Islamic Science & Education University, MIS Department, Jordan |
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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. |
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