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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

The Effect of Feature Selection on Phish Website Detection

Author 1: Hiba Zuhair
Author 2: Ali Selmat
Author 3: Mazleena Salleh

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2015.061031

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 10, 2015.

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Abstract: Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.

Keywords: phish website; phishing detection; feature selection; classification model

Hiba Zuhair, Ali Selmat and Mazleena Salleh, “The Effect of Feature Selection on Phish Website Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 6(10), 2015. http://dx.doi.org/10.14569/IJACSA.2015.061031

@article{Zuhair2015,
title = {The Effect of Feature Selection on Phish Website Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.061031},
url = {http://dx.doi.org/10.14569/IJACSA.2015.061031},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {10},
author = {Hiba Zuhair and Ali Selmat and Mazleena Salleh}
}


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