Home / Current Issue / Paper 1707096
SafeNet Shield: Finding illegal websites using RNN-GRU and inappropriate messages using Logistic Regression, Decision Tree & Random Forest
Subject area: Science,Engineering and Technology · Area of research: Computer Science
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
References
[1] Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
[2] Lizhen Tang and Qusay h. Mahmoud, “A Deep Learning-Based Framework for Phishing Website Detection”, IEEE Access, 2022.
[3] Teoh Hwai Teng, Kasturi Dewi Varathan,“Cyberbullying Detection in Social Networks: A Comparison Between Machine Learning and Transfer Learning Approaches”, IEEE Access, 2023.
[4] Hadiya E M, “Cyber Bullying Detection in Twitter using Machine Learning Algorithm”, 2022.
[5] Mohammed Hazim Alkawaz, Stephanie Joanne Steven, Asif Iqbal Hajamydeen, Rusvaizila Ramli, “A Comprehensive Survey on Identification and Analysis of Phishing Websites Based on Machine Learning Methods”,2021.
[6] Abdul Basit, Maham Zafar, Abdul Rehman Javed, Zunera Jalil,“A Novel Ensemble Machine Learning Method to Detect Phishing Attacks”,2020.
How to cite this paper
@article{1707096,
author = {Tarani S, Sadiya Kaunain, Alice Patricia Innes, Shawn Thomas},
title = {SafeNet Shield: Finding illegal websites using RNN-GRU and inappropriate messages using Logistic Regression, Decision Tree & Random Forest},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {8},
pages = {335-339},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707096.pdf},
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},
month = {February},
}