ACE Journal of Computer Science and Engineering
ISSN: OnlineISSN: Print 2520-3746
Abstract
Phishing assaults cost internet users billions of dollars every year and are a constantly growing hazard in the cyberspace. It is illegal to gather sensitive information from consumers through a number of social engineering techniques. Email, instant messaging, pop‐up messages, web pages and other forms of communication can all be used to identify phishing tactics. This study offers a model that can determine whether a URL link is genuine or fraudulent. The data set used for the classification was sourced from the University of New Brunswick dataset bank, which has a collection of benign, spam, phishing, malware and defacement URLs, as well as from an open source service called "Phish Tank," which contains phishing URLs in multiple formats such as CSV, JSON, etc. Phishing URLs are identified using a combination of deep neural network methods and more than six machine learning models. The goal of this study is to create a web application software that can identify phishing URLs from a database of more than 5,000 URLs that have been randomly selected, divided into 80,000 training samples and 20,000 testing samples and then divided again into equal portions of phishing and legitimate URLs. To distinguish between legal and phishing URLs, the URL dataset is trained and tested using feature selections like address bar‐based features, domain‐based features, HTML and JavaScript‐based features. Finally, the study provided a model for classifying URLs as phishing or legitimate. This would be extremely useful in assisting individuals and businesses in identifying phishing attacks by authenticating any link provided to them to prove its validity.
How to cite this article:
Prasanth Baskaran, B.J. Ronald Isaac, R. Manju and G. Chandru. Phishing Websites Spotting with Help of Using Machine Learning Tools.
DOI: https://doi.org/10.36478/10.59218/makacejcse.2023.1.11
URL: https://www.makhillpublications.co/view-article/2520-3746/10.59218/makacejcse.2023.1.11