[1] Y. Zhang, S. Egelman, L. Cranor, and J. Hong, “Phinding phish: Evaluating anti-phishing tools,” 2006.
[2] M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabtah, “Intelligent phishing detection system for e-banking using fuzzy data mining,” Expert systems with applications, vol. 37, pp. 7913-7921, 2010.
[3] M. D. I. A. Ajlouni, W. E. Hadi, and J. Alwedyan, “Detecting Phishing Websites Using Associative Classification,” European Journal of Business and Management, vol. 5, pp. 36-40, 2013.
[4] L. F. Cranor, S. Egelman, J. I. Hong, and Y. Zhang, “Phinding Phish: An Evaluation of Anti-Phishing Toolbars,” in NDSS, 2007.
[5] M. Sirajuddin, “Data Mining Approach for Deceptive Phishing Detection System,” ijsret, vol. 2, pp. 337-334, 2013.
[6] E. Medvet, E. Kirda, and C. Kruegel, “Visual-similarity-based phishing detection,” in Proceedings of the 4th international conference on Security and privacy in communication netowrks, p. 22, 2008.
[7] W. Zhang, H. Lu, B. Xu, and H. Yang, “Web phishing detection based on page spatial layout similarity,” Informatica, vol. 37, pp. 231-244, 2013.
[8] L. Wenyin, G. Huang, L. Xiaoyue, Z. Min, and X. Deng, “Detection of phishing webpages based on visual similarity,” in Special interest tracks and posters of the 14th international conference on world wide web, pp. 1060-1061, 2005.
[9] S. T. Kumar, V. Kumar, and A. Kumar, “Detection and Prevention of Phishing Attacks Using Linkguard Algorithm,” 2008.
[10] J. S. White, J. N. Matthews, and J. L. Stacy, “A method for the automated detection phishing websites through both site characteristics and image analysis,” in SPIE Defense, Security and Sensing, pp. 84080B-84080B-11, 2012.
[11] A. P. Rosiello, E. Kirda, C. Kruegel, and F. Ferrandi, “A layout-similarity-based approach for detecting phishing pages,” in Security and Privacy in Communications Networks and the Workshops, 2007. Secure Comm 2007. Third International Conference on, pp. 454-463, 2007.
[12] N. R. T. Guhan, “Analyzing and Detecting Phishing Webpages with Visual Similarity Assessment Based on Earth Movers Distance with Linear Programming Model,” International Journal of Advanced Engineering Technology, vol. III, pp. 327-330, 2012.
[13] P. Barraclough, M. Hossain, M. Tahir, G. Sexton, and N. Aslam, “Intelligent phishing detection and protection scheme for online transactions,” Expert systems with applications, vol. 40, pp. 4697-4706, 2013.
[14] A. Demaris and S. H. Selman, “Logistic regression,” in Converting Data into Evidence, ed: Springer, pp.115-136, 2013.
[15] S. Garera, N. Provos, M. Chew, and A. D. Rubin, “A framework for detection and measurement of phishing attacks,” in Proceedings of the 2007 ACM workshop on Recurring malcode, pp. 1-8, 2007.
[16] P. Sengar and V. Kumar, “Client-side defense against phishing with pagesafe,” International Journal of Computer Applications, vol. 4, pp. 6-10, 2010.
[17] SS. Abu-Nimeh, D. Nappa, X. Wang, and S. Nair, “A comparison of machine learning techniques for phishing detection,” in Proceedings of the anti-phishing working groups 2nd annual ecrime researchers summit, pp. 60-69, 2007.
[18] J. M. De-Sa, “Pattern recognition: concepts, methods, and applications,” Springer, 2001.
[19] H. M. Deylami and Y. P. Singh, “Cybercrime detection techniques based on support vector machines,” Artificial Intelligence Research, vol. 2, 2013.
[20] L. Breiman, “Random forests,” Machine learning, vol. 45, pp. 5-32, 2001.
[21] D. M. L. V. Radha Damodaram, “Experimental Study on Meta Heuristic Optimization Algorithms for Fake Website Detection,” International Association of Scientific Innovation and Research (IASIR), vol. 2, pp. 43-53, 2012.
[22] M. Radha Damodaram and M. Valarmathi, “Phishing Website Detection and Optimization Using Particle Swarm Optimization Technique,” International Journal of Computer Science and Security (IJCSS), vol. 5, p. 477, 2011.
[23] M. R. Damodaram and M. Valarmathi, “Bacterial Foraging Optimization for Fake Website Detection,” International Journal of Computer Science & Applications (TIJCSA), vol. 1, 2013.
[24] M. H. Mozaffari, H. Abdy, and S. H. Zahiri, “Application of inclined planes system optimization on data clustering,” in Pattern Recognition and Image Analysis (PRIA), 2013 First Iranian Conference on, pp. 1-3, 2013.
[25] M. Aburrous, M. Hossain, K. Dahal, and F. Thabtah, “Associative classification techniques for predicting e-banking phishing websites,” in Multimedia Computing and Information Technology (MCIT), 2010 International Conference on, pp. 9-12, 2010.
[26] M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabatah, “Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining,” in Cyber Worlds, 2009. CW'09. International Conference on, pp. 265-272, 2009.
[27] P. Barraclough, M. Hossain, M. Tahir, G. Sexton, and N. Aslam, “Intelligent phishing detection and protection scheme for online transactions,” Expert Systems with Applications, 2013.
[28] S. H. Zahiri and S. A. Seyedin, “Intelligent Particle Swarm Classifiers,” Iranian journal of electrical and computer engineering, vol. 4, p. 63, 2015.
[29] M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabtah, “Experimental case studies for investigating e-banking phishing techniques and attack strategies,” Cognitive Computation, vol. 2, pp. 242-253, 2010.
[30] A. Y. Fu, L. Wenyin, and X. Deng, “Detecting phishing web pages with visual similarity assessment based on earth mover's distance (EMD),” Dependable and Secure Computing, IEEE Transactions on, vol. 3, pp. 301-311, 2006.
[31] R. Mohammad, T. McCluskey, and F. A. Thabtah, “Intelligent Rule based Phishing Websites Classification,” IET Information Security, 2013.
)
