Multilayer Perceptron For Classification Of Website Phishing

Maheep Singh*, Roshni Tayal**
*-** Graduate Engineer, Department of Computer Science and Engineering, Central University, Bilaspur, Chhattisgarh, India.
Periodicity:March - May'2018
DOI : https://doi.org/10.26634/jit.7.2.14649

Abstract

Today websites are used for various purposes. There is a crime named website phishing which comes under Cybercrime. A website phishing tries to steal your account password or other private information by misleading you into believing that you're on a legitimate website. Several conventional techniques for detecting phishing websites have been suggested to cope with this problem. One could even land on a phishing site by mistyping a URL. In this study, a Multilayer Perceptron Learning approach is used after applying 10-fold cross validation as a preprocessing for website phishing classification which gives almost 100% accuracy. The experimental results show that the performance of the multilayer perceptron learning classifiers improved the results up to a greater extent.

Keywords

Website Phishing, cybercrime, Multilayer Perceptron Learning.

How to Cite this Article?

Singh,M., and Tayal,R. (2018). Multilayer Perceptron for Classification of Website Phishing. i-manager’s Journal on Information Technology, 7(2), 30-36. https://doi.org/10.26634/jit.7.2.14649

References

[1]. Aburrous, M., Hossain, M. A., Thabatah, F., & Dahal, K. (2008). Intelligent phishing website detection system using fuzzy techniques. In Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. rd 3 International Conference on (pp. 1-6). IEEE.
[2]. Aburrous, M., Hossain, M. A., Dahal, K., & Thabtah, F. (2010). Predicting phishing websites using classification mining techniques with experimental case studies. In Information Technology: New Generations (ITNG), 2010 Seventh International Conference on (pp. 176-181). IEEE.
[3]. Afroz, S., & Greenstadt, R. (2011). Phishzoo: Detecting phishing websites by looking at them. In Semantic Computing (ICSC), 2011 Fifth IEEE International Conference on (pp. 368-375). IEEE.
[4]. Ali, W. (2017). Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection. International Journal of Advanced Computer Science and Applications, 8(9), 72-78.
[5]. Basnet, R., Mukkamala, S., & Sung, A. H. (2008). Detection of phishing attacks: A machine learning approach. In Soft Computing Applications in Industry (pp. 373-383). Springer, Berlin, Heidelberg.
[6]. Blum, A., Wardman, B., Solorio, T., & Warner, G. (2010). Lexical feature based phishing URL detection using online rd learning. In Proceedings of the 3 ACM Workshop on Artificial Intelligence and Security (pp. 54-60). ACM.
[7]. Dedakia, M., & Mistry, K. (2015). Phishing detection using content based associative classification data mining. Journal of Engineering Computers & Applied Sciences (JECAS), 4(7), 209-214.
[8]. Fatt, J. C. S., & Chiew, K. L. (2014). Phishdentity: Leverage Website Favicon to Offset Polymorphic Phishing Website. In Availability, Reliability and Security (ARES), 2014 Ninth International Conference on (pp. 114-119). IEEE.
[9]. James, J., Sandhya, L., & Thomas, C. (2013). Detection of phishing URLs using machine learning techniques. In Control Communication and Computing (ICCC), 2013 International Conference on (pp. 304-309). IEEE.
[10]. Jo, I., Jung, E., & Yeom, H. Y. (2010). You're not who you claim to be: Website identity check for phishing th detection. In 2010 Proceedings of 19 International Conference on Computer Communications and Networks.
[11]. Kadam, A. S., & Pawar, S. S. (2013). Comparison of association rule mining with pruning and adaptive technique for classification of phishing dataset. Third International Conference on Computational Intelligence and Information Technology (CIIT 2013) 2013 (CP646), 61-67.
[12]. Kim, D., Achan, C., Baek, J., & Fisher, P. S. (2013). Implementation of framework to identify potential phishing websites. In Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on (pp. 268- 268). IEEE.
[13]. Layton, R., Brown, S., & Watters, P. (2009). Using differencing to increase distinctiveness for phishing website clustering. In Ubiquitous, Autonomic and Trusted Computing, 2009. UIC-ATC'09. Symposia and Workshops on (pp. 488-492). IEEE.
[14]. Naresh, U., VidyaSagar, U., & Reddy, C. V. M. (2013). Intelligent phishing website detection and prevention system by using link guard algorithm. Proc. IOSR, 14(3), 28- 36.
[15]. Nguyen, L. A. T., To, B. L., Nguyen, H. K., & Nguyen, M. H. (2013). Detecting phishing websites: A heuristic URLbased approach. In Advanced Technologies for Communications (ATC), 2013 International Conference on (pp. 597-602). IEEE.
[16]. Panchal, G., Ganatra, A., Kosta, Y. P., & Panchal, D. (2011). Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering, 3(2), 332- 337.
[17]. Ramanathan, V., & Wechsler, H. (2012). Phishing Website detection using latent Dirichlet allocation and AdaBoost. In Intelligence and Security Informatics (ISI), 2012 IEEE International Conference on (pp. 102-107). IEEE.
[18]. Shahriar, H., & Zulkernine, M. (2011). Information source-based classification of automatic phishing website detectors. In Applications and the Internet th (SAINT), 2011 IEEE/IPSJ 11 International Symposium on (pp. 190-195). IEEE.
[19]. Singh, P., Jain, N., & Maini, A. (2015). Investigating the effect of feature selection and dimensionality reduction on phishing website classification problem. In Next Generation Computing Technologies (NGCT), 2015 st 1 International Conference on (pp. 388-393). IEEE.
[20]. Sonawane, J. S., & Patil, D. R. (2014). Prediction of heart disease using multilayer perceptron neural network. In Information Communication and Embedded Systems (ICICES), 2014 International Conference on (pp. 1-6). IEEE.
[21]. Tan, C. L., & Chiew, K. L. (2014). Phishing website detection using URL-assisted brand name weighting s y s t em. I n I n t e l l i g e n t S i g n a l Pro c e s s i n g a n d Communication Systems (ISPACS), 2014 International Symposium on (pp. 54-59). IEEE.
[22]. Zhuang, W., Jiang, Q., & Xiong, T. (2012). An intelligent anti-phishing strategy model for phishing website detection. In Distributed Computing Systems n d Workshops (ICDCSW), 2012 32 International Conference on (pp. 51-56). IEEE.
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