E-UDE: A Scientific Machine Learning Model for Malware Propagation Dynamics

Abstract

Understanding the malware propagation dynamics over the Internet is crucial for effectively controlling its spread. Existing mechanistic and data-driven models in the context of malware propagation dynamics, have some issues with either interpretability or predictive accuracy. This study presents an Enhanced Universal Differential Equation (E-UDE) framework that explicitly incorporates more detailed key factors that influence malware propagation. Evaluation on real-world malware propagation data shows that the E-UDE achieves the best overall performance, attaining an overall RMSE of 783.70 and MAE of 421.65. Compared with the Classical ODE and the UDE models, the proposed approach reduces RMSE by approximately 66 percent and 39 percent, respectively. Its robustness to predictive capability is further evaluated. In this experiment, the E-UDE achieves a Test RMSE of 634.6, a Test MAE of 419.3, and a Test Correlation of 0.964. The E-UDE maintains relatively stable prediction performance as the noise level increases, showing limited degradation compared with the baseline models. Furthermore, this E-UDE model enables us to perform several scenario analyses under different cybersecurity scenarios, providing a useful framework for better understanding the effective control factors. To study the wide applicability of the E-UDE framework, the model performance is assessed by two different datasets, namely, Code Red dataset and Witty Worm dataset.