Multi-Layer AI Defense Models Against Real-Time Phishing and Deepfake Financial Fraud
DOI:
https://doi.org/10.63397/ISCSITR-IJBI_05_02_02Keywords:
AI Security, Phishing Detection, Deepfake Fraud, Multi-layer Defense, Financial Cyberse-curity, Real-Time Threat Response, NLP, Adversarial Attacks, Fraudulent Identity Detec-tion, Behavioral AIAbstract
The rise of AI-powered cyberattacks—especially deepfake-driven financial scams and sophisticated phishing schemes—demands an equally advanced defense paradigm. This research proposes a multi-layer AI defense model that integrates real-time threat detec-tion, behavioral analysis, adversarial learning, and biometric authentication to counter phishing and deepfake financial fraud. Drawing from both classical and modern machine learning models, the study explores hybrid architectures combining Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs). The system is tested against synthetic fraud scenarios, showing promising pre-cision, adaptability, and low false positives. This paper also presents a comprehensive literature review, architecture design, sequence diagrams, and comparative analysis to reinforce the effectiveness of multi-layered defense in real-world financial Systems.
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