Abstract

Data mining and machine learning is one of the most essential tools in new generation technology. That is used in a number of applications i.e. security, banking and decision making. In this paper, data mining application of web data security is described in details. In this context the domain of phishing URL detection and classification is key aim of the proposed work. This paper includes the different aspects of phishing and recently made contributions for accurately classification of phishing URLs. In addition of that a data mining based model is also proposed that is help to classify the phishing URLs more accurately. Finally the paper provides the future extension of the work.

References

• B. B. Gupta, Nalin A.G. Arachchilage, Konstantinos E. Psannis, “Defending against Phishing Attacks: Taxonomy of Methods, Current Issues and Future Directions”, https://arxiv.org/ftp/arxiv/papers/1705/1705.09819.pdf
• G.Parthasarathy, D.C.Tomar, K. Christina Praisy, “AN ENHANCEMENT OF ASSOCIATION CLASSIFICATION ALGORITHM FOR IDENTIFYING PHISHING WEBSITES”, Indian Journal of Computer Science and Engineering (IJCSE) Vol. 7 No. 4 Aug-Sep 2016
• R. Sathya, Annamma Abraham, “Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification”, International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 2, 2013
• Patrick Dave P. Woogue, Gabriel Andrew A. Pineda, and Christian V. Maderazo, “Automatic Web Page Categorization Using Machine Learning and Educational-Based Corpus”, International Journal of Computer Theory and Engineering, Vol. 9, No. 6, December 2017
• Zheng Dong, Apu Kapadia, Jim Blythe and L. Jean Camp, “Beyond the Lock Icon: Real-time Detection of Phishing Websites Using Public Key Certificates”, 978-1-4799-8909-6/15/$31.00 c 2015 IEEE
• Peter F. Likarish, “Early detection of malicious web content with applied machine learning”, PhD (Doctor of Philosophy) thesis, University of Iowa, 2011.
• https://www.phishtank.com/
• Parth Parekh, Kajal Parmar, Pournima Awate, “Spam URL Detection and Image Spam Filtering using Machine Learning”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 05 Issue: 07 | July 2018
• Neda Abdelhamid, Fadi Thabtah, Hussein Abdel-jaber, “Phishing Detection: A Recent Intelligent Machine Learning Comparison based on Models Content and Features”, 978-1-5090-6727-5/17/$31.00 ©2017 IEEE
• Hemali Sampat, Manisha Saharkar, Ajay Pandey, Hezal Lopes, “Detection of Phishing Website Using Machine Learning”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 05 Issue: 03 | Mar-2018
• Anna L. Buczak, and Erhan Guven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection”, IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 18, NO. 2, SECOND QUARTER 2016
• Mahmood Moghimi, Ali Yazdian Varjani, “New rule-based phishing detection method”, Expert Systems With Applications 53 (2016) 231–242