Ransomware attacks posed a major cybersecurity challenge to smart cities, threatening critical infrastructure and data integrity. In this research developed a Digital Twin–Based Real-Time Detection and Prevention Framework to address this challenge. I implemented the system using python and other libraries like tensorflow, keras, pandas, NumPy, and Matplotlib, with training performance tracked through accuracy and loss graphs. The framework utilized Digital Twin Models to replicate the real network in a virtual environment, enabling safe monitoring of both normal and malicious activities. Real-time synchronization allowed proactive detection, while honeypots acted as decoys to divert ransomware traffic and collect attack data for refining detection patterns. Detection was achieved by analyzing network traffic parameters, including packet size distribution, connection frequency, data transfer rate, entropy levels, file access frequency, encryption patterns, CPU utilization, and memory usage. The system flagged anomalies by comparing traffic with baseline patterns, effectively isolating malicious activity. Experimental results showed strong performance, achieving 92% accuracy, 91% precision, 90% recall, and a 90.5% F1-score. Detection graphs confirmed the system’s responsiveness across low- and high-intensity attack ranges, while evaluation against normal traffic produced stable results with minimal false positives. The proposed framework demonstrated high accuracy, reliability, and resilience in real-time ransomware detection and prevention. The integration of digital twins and honeypots strengthened predictive modeling and deception-based defense, confirming the framework’s suitability for securing smart cities against ransomware threats.

The experimental results demonstrated that the framework achieved high accuracy, precision, recall, and F1-score, confirming its ability to classify normal traffic and ransomware activity with minimal error. The training and validation performance showed stable learning, while the detection graphs confirmed effective ransomware identification across different attack intensities. The integration of Digital Twin Models played a critical role by replicating real network environments and enabling safe, proactive monitoring of traffic behavior. Additionally, honeypots acted as intelligent decoys, attracting and analyzing malicious requests while reducing the risk of undetected intrusions. The system was able to detect ransomware attacks by monitoring network traffic features, comparing them with baseline patterns, and identifying deviations in real time. This approach not only ensured rapid detection but also supported effective prevention measures. This outcome highlighted its potential application in enhancing the cybersecurity posture of smart cities and protecting critical infrastructure.

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