[1] R. Mohammad, F. Thabtah and L. McCluskey, “Predicting phishing websites based on self-structuring neural network”, Neural Computing and Applications, vol. 25(2), pp. 443-458, 2014.
[2] J. Maoa, J. Biana, W. Tiana, Sh. Zhua, T. Weic, A. Lid and Z. Liange, “Detecting Phishing Websites via Aggregation Analysis of PageLayouts”, In Proceedings of the International Conference on Identification, Information and Knowledge in the Internet of Things, China, 19-21 October, 2017.
[3] https://www.wombatsecurity.com/blog/the-latest-in-phishing-first-of-2019. (Accessed on: 2020)
[4] A. Jain and B. Gupta, “PHISH-SAFE: URL Features-Based Phishing Detection System Using Machine Learning”, Cyber Security, Advances in Intelligent Systems and Computing, vol. 729, pp. 467-474, 2018.
[5] N. Sanglerdsinlapachai and A. Rungsawang,“Using Domain Top-page Similarity Feature in Machine Learning-based Web Phishing Detection”, In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Thailand, 09-10 Jan, 2010.
[6] M. Adebowale, K. Lwin, E. Sánchez and M. Hossain, “Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text”, Expert Systems with Applications, vol. 15, pp. 300-313, 2019.
[7] I. Hamid, A. Rahmi and A. Jemal “Phishing e-mail feature selection approach 2011.” In Proceedings of the International Joint Conference of IEEE, Taiwan, 25-27 May, 2011.
[8] N. Shekokar, C. Shah, M. Mahajan, and S. Rachh, “An ideal approach for detection and prevention of phishing attacks”, Procedia Computer Science, vol. 49, pp. 82-91, 2015.
[9] Y. Zhang, I. Hong, and F. Cranor, “Cantina: a content-based approach to detecting phishing web sites”, In Proceedings of the 16thinternational conference on World Wide Web, ACM, Canada, 08-12 May, 2007.
[10] M. Aburrous, A. Hossain, K. Dahal, and F. Thabtah, “Intelligent phishing detection system for e-banking using fuzzy data mining”, Expert Systems with Applications, vol. 37(12), pp. 7913-7921, 2010.
[11] A. Barraclough, A. Hossain, A. Tahir, G. Sexton, and N. Aslam, “Intelligent phishing detection and protection scheme for online transactions. (Re- port)”, Expert Systems with Applications, vol. 40 (11), pp. 4697-4706, 2013.
[12] UCI Machine Learning Repository available at: https://archive.ics.uci.edu/ml/machine-learning-databases/00327/ Training%20Dataset.arff. (Accessed on: 2020)
[13] Phishing websites Database available at: http://eprints.hud.ac.uk/24330/9/Mohammad14JulyDS_1.arff. (Accessed on: 2020)
[14] A. Ahmed and N. Abdullah, “Real Time Detection of Phishing Websites”, In Proceedings of the IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference, Canada, 13-15 October, 2016.
[15] L. Cranor, S. Egelman, I. Hong, and Y. Zhang, “Phishing Phish: An Evaluation of Anti-Phishing Toolbars”, In Proceedings of the Network and Distributed System Security Symposium Conference, NDSS, USA, 28th February – 02nd March,2007.
[16] B. Osareh, "Intrusion Detection in Computer Networks based on Machine Learning Algorithms", International Journal of Computer Science and Network Security, vol. 8(11), pp. 15-23, 2008.
[17] H. Shahriar and M. Zulkernine, “Information Source-based Classification of Automatic Phishing Website Detectors”, IEEE/IPSJ International Symposium on Applications and the Internet, Munich, pp. 190-195, 2011.
[18] L. Wenyin1, G. Huang1, L. Xiaoyue, Z. Min, and X. Deng, “Detection of phishing webpages based on visual similarity”, In Proceedings of the 14th international conference on World Wide Web, Japan, 10-14 May, 2005.
[19] A. Fu, L. Wenyin, and X. Deng, “Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)”, IEEE Transactions on Dependable and Secure Computing, vol. 3(4), pp. 301 - 311, 2006.
[20] V. Kumar and R. Kumar, “Detection of a phishing attack using visual cryptography in ad-hoc network”, In Proceedings of the IEEE International Conference on Communications and Signal Processing (ICCSP), INDIA, 02-04 April, 2015.
[21] S. Fatt, K. Leng, and S. Nah, “Phishdentity: Leverage Website Favicon to Offset Polymorphic Phishing Website”, In Proceedings of the IEEE Ninth International Conference on Availability, Reliability and Security (ARES), Switzerland, 08-12 September, 2014.
[22] A. Barraclough, A. Hossain, A. Tahir, G. Sexton, and N. Aslam, “Intelligent phishing detection and protection scheme for online transactions”, Expert Systems with Applications, vol. 40(11), pp. 4697-4706, 2013.
[23] M. Jian, T. Wenqian, L. Pei, W. Tao and L. Zhenkai, “Phishing-Alarm: Robust and Efficient Phishing Detection via Page Component Similarity”, IEEE Access, vol. 5, pp. 17020-17030, 2017.
[24] V. Shreeram, M. Suban, P. Shanthi, and K. Manjula, “Anti-phishing detection of phishing attacks using genetic algorithm”, In Proceedings of the IEEE International Conference on Communication Control and Computing Technologies (ICCCCT), India, 7-9 October, 2010.
[25] A. Yasin and A. Abuhasan, “An intelligent model for phishing email detection”, International Journal of Network Security & Its Applications(IJNSA), vol. 8(4), pp. 55-72, 2016.
[26] A. Agarwal, M. Mittal, A. Pathak and L. Goyal, “Fake News Detection Using a Blend of Neural Networks: An Application of Deep Learning”, SN Computer Science, 1:134, pp. 1-9, 2020.
[27] C. Monica and N. Nagarathna, “Detection of Fake Tweets Using Sentiment Analysis”, SN Computer Science, 1:89, pp. 1-7, 2020.
[28] N. Abdelhamid, A. Ayesh, and F. Thabtah, “Phishing detection based Associative Classification data mining”, Expert Systems with Applications, vol. 41(13), pp. 5948-5959, 2014.
[29] Y. Ping, G. Yuxiang, Z. Futai, Y. Yao, W. Wei and Z. Ting, “Web Phishing Detection Using a Deep Learning Framework”, Wireless Communications and Mobile Computing, pp. 1-9, 2018.[30] Y. Peng, Z. Guangzhen and Z. Peng, “Phishing Website Detection based on Multidimensional Features driven by Deep Learning”, IEEE Access, vol. 7, pp. 15196-15209, 2019.
[31] Z. Erzhou, Ch. Yuyang, Y. Chengcheng, L. Xuejun and L. Feng, “OFS-NN: An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network”, IEEE Access, vol. 7, pp. 73271-73284, 2019.
[32] R. Mohammad, F. Thabtah and T. Mccluskey, “Predicting phishing websites based on self-structuring neural network”, Neural Computing and Applications, vol. 25(2), pp. 443-458, 2013.
[33] A. Tharwat, “Classification assessment methods”, Applied Computing and Informatics, pp. 1-13, 2018.