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Research Article
Detecting of E-Banking Phishing website -using machine learning approach
Prof Durga wanjari1
Nikahat Salam qureshi2
Divya mahesh Bansod3
Nikita kuldip Sawankar4
Bhavana yashvant Wagmare5
Sujata yashwant Ghodeswer6
123456Smt.Radhika tai pandav college of engineering, nagpur/RTMNU, India.
Published Online: May-June 2022
Pages: 259-264
Cite this article
No DOIReferences
1.Aburrous M.., Hossain M., Dahal K.P. and Thabtah F. (2020) Experimental Case Studies for Investigating E- Banking Phishing Techniques and Attack Strategies. Journal of Cognitive Computation, Springer Verlag, 2 (3): 242-253.
2.Aravindhan, Dr.R.Shanmugalakshmi, Certain Investigation on Web Application Security: Phishing Detection and Phishing Target
Discovery, January 2019.
3.Abdelhamid N., Thabtah F., Ayesh A. (2019) Phishing detection based associative classification data mining. Expert systems with
Applications Journal. 41 (2019) 5948-5959.
4.R. B. Basnet, A. H. Sung, "Mining web to detect phishing URLs", Proceedings of the International Conference on Machine Learning and Applications, vol. 1, pp. 568-573, Dec 2018.
5.Naghmeh Moradpoor, Employing Machine Learning Techniques for Detection and Classification of Phishing Emails, July 2017.
6.X. Zhang, Y. Zeng, X. Jīn, Z. Yan, and G. Geng, “Boosting the Phishing Detection Performance by
Semantic Analysis,” 2017.
7.Jain, Ankit Kumar, and B. B. Gupta. "Comparative analysis of features-based machine learning
approaches for phishing detection." Computing for Sustainable Global Development (INDIA Com),
2016 3rd International Conference on. IEEE, 2016, pp. 2125-2130.
8.Mohammad R., Thabtah F., McCluskey L., (2014B) Intelligent Rule based Phishing Websites Classification. Journal of Information Security(2), 1-17. ISSN 17518709. IET.
9.L. A. T. Nguyen, B. L. To, H. K. Nguyen, and M. H. Nguyen, “A novel approach for phishing detection using URL-based heuristic,” 2014
Int. Conf. Computer. Manag. Telecommand. ComManTel 2014, pp. 298–303, 2014.
10.Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I. (2009) The WEKA Data Mining Software: An Update; SIGKDD
Explorations, Volume 11, Issue 1.
2.Aravindhan, Dr.R.Shanmugalakshmi, Certain Investigation on Web Application Security: Phishing Detection and Phishing Target
Discovery, January 2019.
3.Abdelhamid N., Thabtah F., Ayesh A. (2019) Phishing detection based associative classification data mining. Expert systems with
Applications Journal. 41 (2019) 5948-5959.
4.R. B. Basnet, A. H. Sung, "Mining web to detect phishing URLs", Proceedings of the International Conference on Machine Learning and Applications, vol. 1, pp. 568-573, Dec 2018.
5.Naghmeh Moradpoor, Employing Machine Learning Techniques for Detection and Classification of Phishing Emails, July 2017.
6.X. Zhang, Y. Zeng, X. Jīn, Z. Yan, and G. Geng, “Boosting the Phishing Detection Performance by
Semantic Analysis,” 2017.
7.Jain, Ankit Kumar, and B. B. Gupta. "Comparative analysis of features-based machine learning
approaches for phishing detection." Computing for Sustainable Global Development (INDIA Com),
2016 3rd International Conference on. IEEE, 2016, pp. 2125-2130.
8.Mohammad R., Thabtah F., McCluskey L., (2014B) Intelligent Rule based Phishing Websites Classification. Journal of Information Security(2), 1-17. ISSN 17518709. IET.
9.L. A. T. Nguyen, B. L. To, H. K. Nguyen, and M. H. Nguyen, “A novel approach for phishing detection using URL-based heuristic,” 2014
Int. Conf. Computer. Manag. Telecommand. ComManTel 2014, pp. 298–303, 2014.
10.Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I. (2009) The WEKA Data Mining Software: An Update; SIGKDD
Explorations, Volume 11, Issue 1.
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