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@article{205063,
        author = {Pallavi C and Samrudhi H M and Bhoomika K M and Athmik R Ameen},
        title = {Intelligent Cybersecurity Framework for banking phishing and fraud detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5027-5032},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205063},
        abstract = {The rapid adoption of online banking and digital payment services has transformed the financial sector by providing faster and more convenient transactions. However this growth has also increased exposure by cybersecurity threats such as phishing attacks, fraudulent transactions unauthorized account access.
This study presents an Intelligent Cybersecurity Framework for Banking Phishing and Fraud Detection that combines Artificial Intelligence and Machine Learning techniques to strengthen banking security. 
The framework evaluates multiple parameters, including URL characteristics, transaction information, login locations, user behaviour, and account activity, to identify suspicious actions. Machine learning models such as Random Forest, Decision Tree, Logistic Regression, and deep learning approaches are incorporated to enhance detection performance. 
The framework also supports real-time monitoring and alert generation, enabling timely responses to potential threats. Publicly available phishing and banking fraud datasets are utilized for model training and evaluation. The proposed approach aims to improve detection accuracy, reduce financial risks, and support secure digital banking operations.},
        keywords = {},
        month = {June},
        }