Affiliation:
1. Vellore Institute of Technology, Chennai, Tamil Nadu, India
Abstract
Phishing attacks are a common way for hackers to obtain sensitive and valuable information from unsuspecting users. These attacks often target critical data such as passwords and financial details. To combat this threat, cybersecurity professionals are constantly searching for reliable and effective techniques for detecting phishing websites. This project investigates the use of machine learning algorithms to identify phishing URLs by extracting and analyzing various features of both legitimate and phishing URLs. The goal is to create a blacklist of known phishing websites that can alert individuals when they browse or access a potentially dangerous site. The project will compare the performance of four machine learning algorithms such as Ensemble Adaboost Classifier, Multi-Layer Perceptron Classifier, Stochastic Gradient Descent classifier, and XGBoost - based on their accuracy, speed, and other factors.
Reference12 articles.
1. Alyssa Anne Ubing , Syukrina Kamilia Binti Jasmi , Azween Abdullah, NZ Jhanjhi, Mahadevan Supramaniam, “Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning”, International Journal of Advanced Computer Science and Applications, Vol. 10, No. 1, 2019
2. Choon Lin Tan, Kng, Leng Chiew, Nah Sze, “Phishing website detection using URL-assisted brand name weighting system”, International Symposium on Intelligent signal processing and communication systems,14885974, Dec 2014
3. Muhammad Rayhan Natadimadja, Maman Abdurohman, Hilal Hudan Nuha ,“A Survey on Phishing Website Detection Using Hadoop”,Jurnal Informatika Universitas Pamulang,Vol. 5, No. 3, September 2020
4. Maher Aburrous, Adel Khelifi,” Phishing Detection Plug-In Toolbar Using Intelligent Fuzzy-Classification Mining Techniques,” The International Journal of Soft Computing and Software Engineering, San Francisco State University, CA, U.S.A., March 2013
5. Mustafa Al-Fayoumi, Jaber Al Widyan, and Mohammad Abusaif,” Intelligent Association Classification Technique for Phishing Website Detection”, The International Arab Journal of Information Technology, Vol. 17, No. 4, July 2020.