SafeNet Shield: Finding illegal websites using RNN-GRU and inappropriate messages using Logistic Regression, Decision Tree & Random Forest

• Author(s): Tarani S ; Sadiya Kaunain ; Alice Patricia Innes ; Shawn Thomas
• Paper ID: 1707096
• Page: 335-339
• Published Date: 15-02-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 8 February-2025

Abstract

SafeNet Shield aims to enhance online safety by detecting phishing websites and cyberbullying messages, leveraging machine learning and deep learning techniques for accurate detection using RNN-GRU models and Random Forest, Decision Trees, and Logistic Regression. The system provides real-time detection and feedback through a user-friendly interface, addressing limitations of existing approaches, promoting a safer digital environment, mitigating online risks, and is scalable, efficient, and accessible. Built using HTML, CSS, Tailwind CSS, and Django, its objective is to reduce cyber threats, promote digital well-being, and contribute to secure online interactions and digital safety solutions. The project's scope includes developing a comprehensive system for online threat detection.

Keywords

Phishing, Cyberbullying, RNN-GRU Models, Random Forest, Decision Trees, Logistic Regression

Citations

IRE Journals:
Tarani S , Sadiya Kaunain , Alice Patricia Innes , Shawn Thomas "SafeNet Shield: Finding illegal websites using RNN-GRU and inappropriate messages using Logistic Regression, Decision Tree & Random Forest" Iconic Research And Engineering Journals Volume 8 Issue 8 2025 Page 335-339

IEEE:
Tarani S , Sadiya Kaunain , Alice Patricia Innes , Shawn Thomas "SafeNet Shield: Finding illegal websites using RNN-GRU and inappropriate messages using Logistic Regression, Decision Tree & Random Forest" Iconic Research And Engineering Journals, 8(8)