Phishing Website Classification using Least Square Twin Support Vector Machine
Mayank Arya Chandra1, S S Bedi2, Shashank Chandra3, Suhail Javed Quraishi4
1Mayank Arya Chandra, Mahatma Jyotiba Phule Rohilkhand University, Bareilly (U.P), India.
2S S Bedi, Mahatma Jyotiba Phule Rohilkhand University, Bareilly (U.P), India.
3Shashank Chandra, Research Fellow, Recruitment & Assessment Centre, DRDO Delhi, India.
4Suhail Javed Quraishi, Invertis University, Bareilly (U.P), India.
Manuscript received on October 17, 2019. | Revised Manuscript received on 26 October, 2019. | Manuscript published on November 10, 2019. | PP: 2063-2068 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3905119119/2019©BEIESP | DOI: 10.35940/ijitee.A3905.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Phishing is one among the luring procedures used by phishing attackers in the means to abuse the personal details of clients. Phishing is earnest cyber security issue that includes facsimileing legitimate website to apostatize online users so as to purloin their personal information. Phishing can be viewed as special type of classification problem where the classifier is built from substantial number of website’s features. It is required to identify the best features for improving classifiers accuracy. This study, highlights on the important features of websites that are used to classify the phishing website and form the legitimate ones by presenting a scheme Decision Tree Least Square Twin Support Vector Machine (DT-LST-SVM) for the classification of phishing website. UCI public domain benchmark website phishing dataset was used to conduct the experiment on the proposed classifier with different kernel function and calculate the classification accuracy of the classifiers. Computational results show that DT-LST-SVM scheme yield the better classification accuracy with phishing websites classification dataset.
Keywords: Least Square, SVM, Twin SVM, Phishing, kernel, Classification, Machine Learning.
Scope of the Article: Classification