An Integrated SIR-SEIR and Machine Learning Framework for Ransomware Propagation Analysis in Windows Networks

Laxmikant P. Gawande

Abstract: Ransomware attacks have emerged as a major cybersecurity threat that causes severe operational disruption and financial losses for organizations worldwide. Recent ransomware campaigns frequently exploit unpatched software vulnerabilities, enabling malicious code to propagate rapidly across enterprise networks. A notable example is the global outbreak of WannaCry ransomware, which demonstrated the destructive impact of self-propagating malware exploiting the Eternal Blue exploit vulnerability in Microsoft Windows environments. This research proposes a comprehensive analytical framework for investigating ransomware propagation and detection using statistical simulation and machine learning techniques implemented in the R programming language. The proposed approach integrates epidemic-based propagation models, enterprise network simulation, and machine learning-based detection methods. A simulated enterprise network consisting of 1000 nodes was analyzed using classical SIR and SEIR epidemic models to evaluate infection dynamics. Machine learning algorithms including Random Forest, Support Vector Machine, and Logistic Regression were trained using the CIC-MalMem-2022 dataset. Experimental evaluation showed that among the tested classifiers, Random Forest produced the best predictive performance with 92% accuracy, with strong precision and recall performance. Simulation results further demonstrate that increasing patch deployment significantly reduces ransomware propagation within enterprise environments. The proposed framework provides an integrated analytical approach for understanding ransomware attack behavior and improving enterprise cyber defense strategies. The simulation and visualization were implemented using the R statistical programming environment.

Keywords: Ransomware propagation, Cybersecurity, Epidemic modeling, SIR-SEIR model, Machine learning, Ransomware detection

How to Cite?: Laxmikant P. Gawande, "An Integrated SIR-SEIR and Machine Learning Framework for Ransomware Propagation Analysis in Windows Networks", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 1320-1325, https://www.ijsr.net/getabstract.php?paperid=SR26420051813, DOI: https://dx.dx.doi.org/10.21275/SR26420051813

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