Fortifying Network Security: Pioneering Digital Twin Technology for Proactive Anomaly Detection

Amidst the growing cybersecurity challenges of our digital age, this study leverages Digital Twin technology to fortify network security. We've meticulously developed a Keras-based Digital Twin model to replicate complex network behaviors within a simulated environment. In a landscape where cyber threats evolve rapidly, this endeavor is pivotal. It addresses current cybersecurity trends and offers proactive digital realm protection. The model enables real-time anomaly detection, em-powering organizations to strengthen their digital infrastructure. Rigorous evaluation, employing the confusion matrix, highlights exceptional performance. The model boasts an Accuracy of 99.43%, a Precision of 85.36%, a Recall of 99.58%, and an F1 Score of 91.93 %. These metrics accentuate its precision and effectiveness in identifying network anomalies. Additionally, it achieves a remarkable True Positive count of 134,694, effectively detecting positive cases while maintaining low False Positives (Type-I Error) at 774. It also has a False Negative (Type II Error) count of 19 and a True Negative count of 4,514. This success underscores Digital Twin technology's potential to revolutionize network security, providing preemptive defense against dynamic threats. Our work signifies a transformative leap towards a safer, resilient digital lands cane fortified by cutting-edge technology,