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

@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},
  }