Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.
References
[1]
Aleroud, A. and Zhou, L. (2017) Phishing Environments, Techniques, and Countermeasures: A Survey. Computers and Security, 68, 160-196.
https://doi.org/10.1016/j.cose.2017.04.006
[2]
Mouton, F., Leenen, L. and Venter, H.S. (2016) Social Engineering Attack Examples, Templates and Scenarios. Computers and Security, 59, 186-209.
https://doi.org/10.1016/j.cose.2016.03.004
[3]
Goel, D. and Jain, A.K. (2017) Mobile Phishing Attacks and Defence Mechanisms: State of Art and Open Research Challenges. Computers and Security, 73, 519-544.
[4]
Moghimi, M. and Varjani, A.Y. (2016) New Rule-Based Phishing Detection Method. Expert Systems with Applications, 53, 231-242.
https://doi.org/10.1016/j.eswa.2016.01.028
[5]
Barraclough, P., Hossain, M., Tahir, M., Sexton, G. and Aslam, N. (2013) Intelligent Phishing Detection and Protection Scheme for Online Transactions. Expert Systems with Applications, 40, 4697-4706. https://doi.org/10.1016/j.eswa.2013.02.009
[6]
Fernandes, D.A.B., Freire, M.M., Paulo, A., Fazendeiro, A. and Inácio, R. (2017) Applications of Artificial Immune Systems to Computer Security: A Survey. Journal of Information Security and Applications, 35, 138-159.
https://doi.org/10.1016/j.jisa.2017.06.007
[7]
Abutair, H.I. and Belghith, A. (2017) Using Case-Based Reasoning for Phishing Detection. Procedia Computer Science, 109, 281-288.
https://doi.org/10.1016/j.procs.2017.05.352
[8]
Hadi, W., Aburub, F. and Alhawari, S. (2016) A New Fast Associative Classification Algorithm for Detecting Phishing Websites. Applied Soft Computing, 48, 729-734.
https://doi.org/10.1016/j.asoc.2016.08.005
[9]
Abdelhamid, N., Ayesh, A. and Thabtah, F. (2014) Phishing Detection Based Associative Classification Data Mining. Expert Systems with Applications, 41, 5948-5959.
[10]
Lakshmi, S. and Vijaya, M.S. (2012) Efficient Prediction of Phishing Websites Using Supervised Learning Algorithms. Procedia Engineering, 30, 798-805.
https://doi.org/10.1016/j.proeng.2012.01.930
[11]
Al-Diabat, M. (2016) Detection and Prediction of Phishing Websites Using Classification Mining Techniques. International Journal of Computer Applications, 147, 5-12.
[12]
Almomani, A., Wan, T.C., Altaher, A., Manasrah, A., Almomani, E., Anbar, M. and Ra-Madass, S. (2012) Evolving Fuzzy Neural Network for Phishing Emails Detection. Journal of Computer Science, 7, 1099-1107.
[13]
Smadi, S., Aslam, N. and Zhang, L. (2018) Detection of Online Phishing Email Using Dynamic Evolving Neural Network Based on Reinforcement Learning. Decision Support Systems, 107, 88-102. https://doi.org/10.1016/j.dss.2018.01.001
[14]
Basnet, R.B., Sung, A.H. and Liu, Q. (2012) Feature Selection for Improved Phishing Detection. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, Berlin, Heidelber, 252-261.
https://doi.org/10.1007/978-3-642-31087-4_27
[15]
Mohammad, R.M., Thabtah, F. and McCluskey, L. (2014) Predicting Phishing Websites Based on Self-Structuring Neural Network. Neural Computing and Applications, 25, 443-458. https://doi.org/10.1007/s00521-013-1490-z
[16]
Haykin, S. (2001) Redes Neurais—Princípios e Práticas. 2nd Edition, Bookman, Porto Alegre.
[17]
Bigus, J.P. (1996) Data Mining with Neural Network: Solving Business Problems from Applications Development to Decision Support. McGraw-Hill, New York.
[18]
Silva, I.N., Spatti, D.H. and Flauzino, R.A. (2010) Redes Neurais Artificiais para Engenharia e Ciências Aplicadas. Artliber, SP.
[19]
Khonji, M., Iraqi, Y. and Jones, A. (2013) Phishing Detection: A Literature Survey. IEEE Communications Surveys & Tutorials, 15, 2091-2121.
[20]
Weider, D.Y., Nargundkar, S. and Tiruthani, N. (2008) A Phishing Vulnerability Analysis of Web Based Systems. IEEE Symposium on Computers and Communications, Marrakech, 6-9 July 2008, 326-331.
[21]
Whittaker, C., Ryner, B. and Nazif, M. (2010) Large-Scale Automatic Classification of Phishing in Pages. Proceedings of the Network and Distributed System Security Symposium, San Diego, CA, 28 February-3 March 2010, 1-14.
[22]
Stringhini, G., Kruegel, C. and Vigna, G. (2010) Detecting Spammers on Social Networks. Proceedings of the 26th Annual Computer Security Applications Conference, Austin, TX, 6-10 December 2010, 1-9.
https://doi.org/10.1145/1920261.1920263
[23]
Basnet, R., Mukkamala, S. and Sung, A.H. (2008) Detection of Phishing Attacks: A Machine Learning Approach. In: Prasad, B., Eds., Soft Computing Applications in Industry, Springer, Berlin, Heidelberg, 373-383.
https://doi.org/10.1007/978-3-540-77465-5_19
[24]
Bergholz, A., De Beer, J., Glahn, S., Moens, M.F., PaaB, G. and Strobel, S. (2010) New Filtering Approaches for Phishing Email. Journal of Computer Security, 18, 7-35. https://doi.org/10.3233/JCS-2010-0371
[25]
Lalitha, M.P. and Udutha, S. (2013) New Filtering Approaches for Phishing Email. International Journal of Computer Trends and Technology (IJCTT), 4, 1733-1736.
[26]
Simoes, M.G. and Shaw, I.S. (2007) Controle e Modelagem fuzzy. FAPESP, São Paulo.
[27]
Han, J., Kamber, M. and Pei, J. (2011) Data Mining: Concepts and Techniques. 3rd Edition, Morgan Kaufmann, Waltham, MA.
[28]
Tkác, M. and Verner, R. (2016) Artificial Neural Networks in Business: Two Decades of Research. Applied Soft Computing, 38, 788-804.
https://doi.org/10.1016/j.asoc.2015.09.040
[29]
Mitchell, T.M. (1997) Machine Learning. McGraw-Hill, New York.
[30]
Mohammad, R., McCluskey, T.L. and Thabtah, F.A. (2014) Intelligent Rule Based Phishing Websites Classification. IET Information Security, 8, 153-160.
[31]
Rao, R.S. and Ali, S.T. (2015) PhishShield: A Desktop Application to Detect Phishing Webpages through Heuristic Approach. Procedia Computer Science, 54, 147-156. https://doi.org/10.1016/j.procs.2015.06.017
[32]
Montazer, G.A. and ArabYarmohammadi, S. (2015) Detection of Phishing Attacks in Iranian e-Banking Using a Fuzzy-Rough Hybrid System. Applied Soft Computing, 35, 482-492. https://doi.org/10.1016/j.asoc.2015.05.059