International Journal of Science, Engineering and TechnologyAuthors: Dhumal Pratiksha Shivnath, Thorat sakshi Sachin, Kale Omkar Santosh, Professor Bangar Abhishek Popat
Abstract: The rapid growth of cyber threats such as phishing websites, malicious scripts, fake academic journals, and deepfake videos has created serious challenges for online security and trust. Traditional protection systems based on static blacklists and signature detection are no longer sufficient to handle evolving and zero-day attacks. This paper proposes SafeURL AI Extension, an intelligent browser extension that integrates machine learning and real-time analysis to detect unsafe digital content and protect users during web browsing. The system incorporates three tightly integrated modules: (i) malicious URL detection using Logistic Regression and Random Forest classifiers trained on structural URL features and webpage behavioral signals; (ii) academic journal authenticity verification through DOI resolution, ISSN cross-checking, DOAJ indexing validation, and domain-blacklist matching; and (iii) deepfake video analysis using a CNN-based deep learning pipeline that processes frame-level facial artifacts to classify content as real or manipulated. Evaluation results demonstrate that the URL detection module achieves approximately 95% accuracy, while the deepfake module achieves 99.9% confidence on manipulated samples. The system provides risk scores, color-coded warnings, and actionable user alerts within 1–2 seconds of page load. By combining real-time threat detection, adaptive learning, and privacy-preserving local analysis, SafeURL AI Extension offers an effective, scalable, and lightweight solution for enhancing browser-level cybersecurity and ensuring a safer digital environment for everyday users.
