Year: 2024
Type: article
Abstract: Phishing is a criminal scheme to steal the user’s personal data and other credential information. It is a fraud that acquires victim’s confidential information such as password, bank account detail, credit card number, financial username and password etc. and later it can be misuse by attacker. The use of machine learning algorithms in phishing detection has gained significant attention in recent years. This research paper aims to evaluate the effectiveness of various machine learning algorithms in detecting phishing URL’s/website. The algorithms tested in this study are Decision Tree, Random Forest, Multilayer Perceptron, XGBoost, Autoencoder Neural Network, and Support Vector Machines. A dataset of phishing URLs is used to train and test the algorithms, and their performance is evaluated based on metrics such as accuracy, precision, recall, and F1 Score. The paper takes in data of phished URL from Phishtank and legitimate URL from University of New Brunswick. The results of this study demonstrate that the Random Forest and XGBoost algorithms outperforms other algorithms in terms of accuracy and other performance metrics and the system has an overall accuracy of 98 %.
Authors: Vishesh Bharuka, Allan Almeida, Sharvari Patil
Institution: Dwarkadas J. Sanghvi College of Engineering
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4393375588