Internet malware propagation: Dynamics and control through SEIRV epidemic model with relapse and intervention

• URL: http://arxiv.org/abs/2603.03712v1
• Date: Wed, 04 Mar 2026 04:28:48 GMT
• Title: Internet malware propagation: Dynamics and control through SEIRV epidemic model with relapse and intervention
• Authors: Samiran Ghosh, V Anil Kumar,
• Abstract summary: We propose a generic SEIRV model formulated using ordinary differential equations to study malware spread.<n>We establish the positivity and boundedness of the system, derive the malware propagation threshold, and analyze the local and global stability of the malware-free equilibrium.<n>We propose a hybrid gradient-based global optimization framework to identify effective and cost-efficient control strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Malware attacks in today's vast digital ecosystem pose a serious threat. Understanding malware propagation dynamics and designing effective control strategies are therefore essential. In this work, we propose a generic SEIRV model formulated using ordinary differential equations to study malware spread. We establish the positivity and boundedness of the system, derive the malware propagation threshold, and analyze the local and global stability of the malware-free equilibrium. The separatrix defining epidemic regions in the control space is identified, and the existence of a forward bifurcation is demonstrated. Using normalized forward sensitivity indices, we determine the parameters most influential to the propagation threshold. We further examine the nonlinear dependence of key epidemic characteristics on the transmission rate, including the maximum number of infected, time to peak infection, and total number of infected. We propose a hybrid gradient-based global optimization framework using simulated annealing approach to identify effective and cost-efficient control strategies. Finally, we calibrate the proposed model using infection data from the "Windows Malware Dataset with PE API Calls" and investigated the effect of intervention onset time on averted cases, revealing an exponential decay relationship between delayed intervention and averted cases.

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