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@article{195990,
        author = {K.Sheshanth Reddy and G.Nava Manideep and N.Praveen and J.Manoj Kumar and D.Mamatha},
        title = {JARVIS-C: An Intelligent AI-Driven Cyber Defense Assistant for Real-Time Threat Detection and Automated Response},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1230-1235},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195990},
        abstract = {Phishing attacks have become one of the most prevalent cybersecurity threats, targeting users through malicious URLs and fake websites. Traditional detection techniques such as blacklist-based systems and signature-based approaches are ineffective against newly generated phishing websites and zero-day attacks. This paper presents JARVIS-C, an AI-based phishing detection system that leverages machine learning techniques to identify malicious URLs in real time. The system extracts multiple URL-based features and uses a trained Random Forest model to classify websites as safe or phishing. Additionally, the system integrates a Chrome extension and a Flask-based API for real-time detection and user alerts. The proposed system achieves an accuracy of approximately 94%, demonstrating its effectiveness in detecting phishing attempts. The architecture is modular and scalable, allowing easy integration with other cybersecurity tools. This approach enhances user safety by providing instant warnings and reducing dependency on traditional detection mechanisms.},
        keywords = {Phishing Detection, Machine Learning, Random Forest, URL Analysis, Cybersecurity, Chrome Extension, Real-Time Detection, SSL Verification, Flask API},
        month = {April},
        }