Phishing Website Detection Using Ensemble Technique
||<--International Journal of Computer Trends and Technology (IJCTT)-->||
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Priyanka Sharma, Rajni Ranjan Singh Makwana|
|DOI : 10.14445/22312803/IJCTT-V69I3P106|
How to Cite?
Priyanka Sharma, Rajni Ranjan Singh Makwana, "Phishing Website Detection Using Ensemble Technique," International Journal of Computer Trends and Technology, vol. 69, no. 3, pp. 26-29, 2021. Crossref, 10.14445/22312803/IJCTT-V69I3P106
In the age of the internet, there is an enormous number of online transactions performed every day; therefore security and privacy of online transactions and banking websites is a challenging task. Website phishing attacks are carried out by presenting a fake website as a genuine one in order to gain confidential information and using that for some non-genuine activities. In this work, the Bagging technique is used with neural network and LMT classifiers as base classifiers in ensemble to classify a set of URLs and to determine the URLs as phishing or legitimate so that a user can be secured from phishing attacks. In this work, we have obtained an accuracy of 90%.
 N. Lord, What is a Phishing Attack? Defining and Identifying Different Types of Phishing Attacks., https://digitalguardian.com/blog/what-phishing-attack-defining-andidentifying-different-types-phishing-attacks, (2018).
 D. R. Patil and J. Patil, J., Survey on malicious web pages detection techniques, International Journal of u-and e-Service, Science and Technology, 8(5)(2015) 195–206.
 W. Hadi, F. Aburrub, and S, Alhawari, A new fast associative classification algorithm for detecting phishing websites, Applied Soft Computing 48(2016) 729-734.
 Arun Kulkarni1, Leonard L. Brown, III2, Phishing Websites Detection using Machine Learning, (IJACSA) International Journal of Advanced Computer Science and Applications,10(7)(2019).
 UCI Machine Learning Repository: Website Phishing Data Set (Online) https://archive.ics.uci.edu/ml/datasets/Website+Phishing.
 R. P. Lippman, An introduction to computing with neural nets. IEEE ASSP Magazine,3(4)(1987) 4-22.
 S. L. Gallant, Neural network learning and expert systems, The MIT Press, Cambridge, MA, (1993).
 Niels Landwehr, Mark Hall, and Eibe Frank., Logistic model trees,(2003).
 Landwehr, N.; Hall, M.; Frank, E., Logistic Model Trees. Machine Learning., (2005).
 Xianwei Gao, Chun Shan, Changzhen Hu, Zequn Niu, Zhen Liu "An Adaptive Ensemble Machine Learning Model for Intrusion Detection." IEEE,7(2019).
 A.D. Kulkarni, Generating classification rules from training samples, International Journal of Advanced Computer Science Applications, 9(6),1-6.
 Surendiran,R., and Alagarsamy,K., 2013. "Privacy Conserved Access Control Enforcement in MCC Network with Multilayer Encryption". SSRG International Journal of Engineering Trends and Technology (IJCTT), 4(5), pp.2217-2224.
Phishing detection, Ensemble classifiers, Classification techniques, Internet security, Machine Learning.