A Dynamical Modeling of Malware Spreading in Scale‐Free Networks With Controlling Mechanism.
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| Title: | A Dynamical Modeling of Malware Spreading in Scale‐Free Networks With Controlling Mechanism. |
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| Authors: | Mohammadi, Hassan1 (AUTHOR), Hosseini, Soodeh1 (AUTHOR) so_hosseini@uk.ac.ir, Bonopera, Marco1 (AUTHOR) marco.bonopera@unife.it |
| Source: | Modelling & Simulation in Engineering. 6/25/2026, Vol. 2026, p1-10. 10p. |
| Subjects: | Scale-free network (Statistical physics), Computer network security, Epidemiological models, Computer viruses, Internet security |
| Abstract: | The increasing dependence on global online networks has made cybersecurity a major concern for individuals and organizations. Understanding the dynamics of malware propagation is essential for formulating effective countermeasures. This paper proposes the SICRS (sensitive–infected–confined–recovery–sensitive) epidemic model to analyze malware propagation in heterogeneous and scale‐free networks. Unlike traditional models, SICRS explicitly considers the impact of maintenance actions on infected and vulnerable nodes. To optimize defense strategies, we evaluate node importance using degree centrality and prioritize the protection of highly connected nodes. Simulation results show that this targeted control mechanism reduces the final epidemic size by more than 34% compared with models without such strategic intervention, while delaying the peak of infection sharply compared with other methods. Furthermore, we derive the epidemic threshold (R0), which shows that in scale‐free networks, the threshold approaches zero, indicating high vulnerability. In the comparative analysis, the proposed SICRS model has more than 15% improvement in prediction accuracy compared with the traditional SIS, SIR, and SEIR models according to the corresponding graphs. These quantitative findings highlight the effectiveness of degree‐based protection and provide practical insights for improving real‐world cybersecurity protocols and reducing potential economic losses from large malware outbreaks. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | The increasing dependence on global online networks has made cybersecurity a major concern for individuals and organizations. Understanding the dynamics of malware propagation is essential for formulating effective countermeasures. This paper proposes the SICRS (sensitive–infected–confined–recovery–sensitive) epidemic model to analyze malware propagation in heterogeneous and scale‐free networks. Unlike traditional models, SICRS explicitly considers the impact of maintenance actions on infected and vulnerable nodes. To optimize defense strategies, we evaluate node importance using degree centrality and prioritize the protection of highly connected nodes. Simulation results show that this targeted control mechanism reduces the final epidemic size by more than 34% compared with models without such strategic intervention, while delaying the peak of infection sharply compared with other methods. Furthermore, we derive the epidemic threshold (R0), which shows that in scale‐free networks, the threshold approaches zero, indicating high vulnerability. In the comparative analysis, the proposed SICRS model has more than 15% improvement in prediction accuracy compared with the traditional SIS, SIR, and SEIR models according to the corresponding graphs. These quantitative findings highlight the effectiveness of degree‐based protection and provide practical insights for improving real‐world cybersecurity protocols and reducing potential economic losses from large malware outbreaks. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 16875591 |
| DOI: | 10.1155/mse/3196684 |
