AN INTELLIGENT IOT-ENABLED DEEP LEARNING ARCHITECTURE FOR REAL-TIME FAULT DIAGNOSIS AND CYBER-ATTACK MITIGATION IN MODERN BANKING INFRASTRUCTURE

Abstract

The rapid digital transformation of the banking sector has led to the widespread adoption of Internet of Things (IoT) technologies to enable real-time monitoring, automation, and intelligent decision-making across modern banking infrastructure. While IoT integration enhances operational efficiency and service availability, it simultaneously introduces new vulnerabilities in the form of system faults and sophisticated cyber-attacks. These challenges pose significant risks to the reliability, security, and resilience of banking operations, particularly in environments that demand continuous availability and strict data integrity. Conventional rule-based fault detection and security mechanisms are often inadequate for handling the scale, heterogeneity, and dynamic behavior of IoT-enabled banking systems, necessitating more intelligent and adaptive solutions. This paper proposes an intelligent IoT-enabled deep learning architecture for real-time fault diagnosis and cyber-attack mitigation in modern banking infrastructure. The proposed framework integrates distributed IoT sensing devices with advanced deep learning models to continuously monitor system behavior, transaction flows, network traffic, and operational parameters. By learning complex temporal and spatial patterns from high-dimensional data streams, the deep learning module enables early identification of abnormal system states caused by hardware faults, software malfunctions, or malicious cyber intrusions. Unlike traditional threshold-based approaches, the proposed architecture is capable of autonomously distinguishing between benign operational anomalies and adversarial cyber-attacks, thereby reducing false alarms and improving detection accuracy. The framework adopts a multi-layer design that combines data acquisition, feature learning, anomaly detection, and response orchestration. Real-time inference allows the system to trigger adaptive mitigation strategies, such as fault isolation, system reconfiguration, or security enforcement actions, ensuring minimal disruption to banking services. The proposed solution emphasizes scalability, robustness, and cyber resilience, making it suitable for deployment in large-scale, heterogeneous banking environments. Extensive experimental evaluation is conducted using representative fault scenarios and cyber-attack models to validate the effectiveness of the architecture. Performance metrics, including detection accuracy, response latency, and system reliability, demonstrate that the proposed approach significantly outperforms conventional machine learning and rule-based methods. Overall, this research contributes a unified and intelligent framework that bridges the gap between operational fault diagnosis and cybersecurity in IoT-enabled banking systems. By leveraging deep learning for real-time monitoring and mitigation, the proposed architecture offers a promising pathway toward secure, resilient, and autonomous banking infrastructure capable of withstanding both operational failures and evolving cyber threats.