Deep Generative Machine Learning Models for Real-Time Adversarial Threat Identification and Self-Adaptive Cybersecurity Framework for Financial Services

Abstract

The increasing sophistication of cyber threats targeting financial institutions, including retail banks, investment firms, FinTech platforms, and payment infrastructure providers, has created an urgent need for intelligent, real-time, and adaptive cybersecurity defense mechanisms tailored to the financial sector. Contemporary adversaries exploit an expanding attack surface comprising advanced persistent threats, SWIFT-targeted malware, banking trojans, account takeover campaigns, and AI-enabled attacks involving synthetic media and deepfake technologies. These threats increasingly employ voice cloning, manipulated video content, forged documents, and fraudulent transaction authorizations to bypass conventional authentication procedures, identity verification systems, and social engineering safeguards. Traditional cybersecurity approaches often struggle to identify such sophisticated adversarial behaviors, particularly when deepfake-driven Business Email Compromise schemes and synthetic identity fraud are specifically designed to evade both human judgment and automated detection systems. This study proposes a deep generative artificial intelligence-based cybersecurity framework that integrates Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based generative architectures to enable real-time adversarial attack detection, threat analysis, and adaptive defense in financial environments. The proposed framework learns complex attack distributions from heterogeneous financial datasets, identifies anomalous transactional and behavioral activities, detects synthetic media artifacts across multimodal authentication channels, and generates diverse adversarial scenarios to improve the robustness of cybersecurity models against evolving threats. Data acquisition incorporates banking network telemetry, core banking system logs, payment gateway records, SWIFT transaction monitoring systems, deepfake audio-visual datasets, media authentication integrity records, and financial threat intelligence sources aligned with regulatory and industry standards, including FS-ISAC, PCI-DSS, DORA, and SAMA cybersecurity frameworks. The preprocessing stage includes data normalization, domain-specific financial feature engineering, multimodal signal analysis, noise filtering, and dimensionality reduction techniques to preserve critical transactional, behavioral, and authentication characteristics. Furthermore, an adaptive response layer continuously updates detection thresholds, enforces media authentication policies, prioritizes high-risk incidents, and triggers automated mitigation workflows in accordance with financial sector incident response procedures and regulatory reporting requirements. Experimental evaluation demonstrates the effectiveness of the proposed framework, achieving an attack detection accuracy of 98.4%, precision of 97.9%, recall of 98.1%, and an F1-score of 98.0%, while maintaining a false positive rate of 2.6% and a real-time detection latency of 0.42 s. The adaptive defense mechanism improved threat response efficiency by 21.7% and enhanced zero-day attack identification capability by 18.5%. Additionally, the deepfake detection module achieved an accuracy of 96.8% across voice, video, and document forgery modalities. These findings demonstrate that deep generative artificial intelligence can substantially strengthen cybersecurity resilience within financial services by facilitating real-time adversarial threat detection, synthetic media authentication, continuous behavioral threat analysis, and automated adaptive defense against the diverse and rapidly evolving cyber threats confronting modern financial institutions.