PhishGuard - PHISHING WEBSITE DETECTION USING MACHINE LEARNING
Author(s):
Sahith Peela, K MohanPrakash, B Purna Sandeep, K RanjithKumar, V Suresh
Keywords:
PhishGuard, Cybersecurity, Phishing Detection, Machine Learning, Web Application, Python Flask, Dataset Analysis, Feature Extraction, Gradient Boosting Classifier, Online Safety, URL Analysis, User Empowerment, Digital Security
Abstract
In the digital age, the internet has become indispensable for daily life by supporting many activities such as work, shopping and socializing. However, this convenience comes with higher risks, especially from phishing attacks, where malicious organizations create fake websites to trick users into revealing personal and sensitive information. This serious threat requires effective solutions to prevent people from falling victim to such scams. PhishGuard has emerged as a new solution designed to solve this challenge by allowing users to distinguish between and identify legitimate websites and phishing websites. PhishGuard is a web-based application built using Python and Flask framework that integrates machine learning to identify and predict features of websites. The app uses Kaggle's comprehensive database of 30 features such as URL length, HTTPS availability, and domain name length to evaluate websites. By carefully learning machine learning models of this data, PhishGuard can evaluate the characteristics of the URL each user submits to determine its legitimacy or potential phishing threat. Overall, PhishGuard demonstrates the power of combining machine learning with web development to create creative, user-friendly solutions to pressing digital security problems. Its development not only demonstrates the potential of new technologies to protect people in the digital environment, but also demonstrates the importance of community and collaboration in phishing attacks.
Article Details
Unique Paper ID: 163919

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 445 - 450
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