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@article{207464,
        author = {Dr.Madhu Gopinath and Vyshnavi J and Tejashwini G S and Vishal},
        title = {Cloud-Based Multimodal Artificial Intelligence Framework for Phishing and Scam Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {902-908},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207464},
        abstract = {More people now bank, shop, and chat online than ever before. That shift? It’s handed scammers a much bigger playground. Fake emails, cloned sites, disguised links they’re being churned out constantly, all aimed at getting someone to hand over a password or card number without thinking twice. The trouble is, most defenses in use today blacklists, fixed rules only recognize a threat once it’s already been seen somewhere. So, a brand-new phishing page? It slips right through. Here, we’ve put together a cloud-hosted multimodal AI system that checks a phishing attempt from three different directions at the same time. Email text goes through a BERT model. The URL itself passes through a GRU network. And a screenshot of the webpage gets analyzed with a CNN. None of these three verdicts gets trusted alone. We merge them with a weighted fusion step and get back one phishing-probability score. Because the whole thing runs on the cloud, updating the models, watching how they perform, and scaling out to more users can all be done from one place. Without touching anything on the user’s end. Pulling evidence from three independent angles rather than betting on one gives the system a real shot at catching attacks a single-signal detector would miss. It helps keep false alarms down too.},
        keywords = {Phishing Detection, Cybersecurity, Cloud Computing, Artificial Intelligence, Deep Learning, BERT, CNN, GRU, Multimodal Learning},
        month = {August},
        }