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Stream/Area:
-
Subject:
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Paper ID:
IJSREM54825
Volume:
9 |
12 |
Dec 2025
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Matchmatics
Guru Nanak College, Sri Muktsar Sahib
The rapid evolution and interconnectedness of modern digital infrastructure have transformed the paradigm of cybersecurity. Classical epidemiological models, rooted in homogeneous mixing assumptions, fail to accurately predict malware dynamics in real-world networks characterized by complex topological properties. This paper provides a comprehensive review of literature concerning computer virus propagation across complex networks, focusing on scale-free, small-world, and random topologies. We systematically trace the evolution of epidemiological frameworks—from standard SIS and SIR compartmental formulations to degree-based mean-field theories, individual-based node-level approximations, and higher-order topological abstractions. Furthermore, we examine the crucial role played by network metrics, such as degree heterogeneity, spectral radii, clustering coefficients, and community structure, in altering the classical epidemic threshold. Modern defense strategies, including targeted immunization, dynamic patch dissemination, and quarantine mechanisms, are critically evaluated. Finally, we highlight current research gaps and propose future research directions involving temporal-multiplex structures and adaptive malware containment.
KEYWORDS
Modeling computer virus propagation on complex networks has evolved from simple homogeneous differential equations to modern topological and spectral graph frameworks. The discovery of the vanishing epidemic threshold in scale-free topologies revealed fundamental vulnerabilities in digital infrastructure, proving that uniform defenses are inadequate. Effective malware containment requires leveraging graph structure through targeted hub immunization, spectral radius suppression, and dynamic quarantine mechanisms. As digital networks grow more complex, multi-layered, and temporal, containment modeling must adapt to protect future infrastructure against intelligent malware threats.
1. Pastor-Satorras, R., & Vespignani, A. (2001 ). Epidemic spreading in scale-free networks. Physical Review Letters, 86(1 4), 3200–3203. 2. Barabási, A. L., & Albert, R. (1 999). Emergence of scaling in random networks. Science, 286(5439), 509–51 2. 3. Watts, D. J., & Strogatz, S. H. (1 998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440–442. 4. Chakrabarti, D., Wang, Y., Wang, C., Leskovec, J., & Faloutsos, C. (2008). Epidemic thresholds in real networks. ACM Transactions on Information and System Security (TISSEC), 1 0(4), 1 –26. 5. Pastor-Satorras, R., & Vespignani, A. (2002). Epidemic dynamics and immunization in scale-free networks. Physical Review E, 65(3), 0361 04. 6. Cohen, R., Erez, K., Ben-Avraham, D., & Havlin, S. (2003). Efficient immunization strategy for computer networks and populations. Physical Review Letters, 91 (24), 247901 . 7. Van Mieghem, P., Omic, J., & Kooij, R. (2009). Virus spread in networks. IEEE/ACM Transactions on Networking, 1 7(1 ), 1 –1 4. 8. Newman, M. E. (2002). Spread of epidemic disease on networks. Physical Review E, 66(1 ), 01 61 28. 9. Gomez, S., Arenas, A., Borge-Holthoefer, J., Meloni, S., & Moreno, Y. (201 0). Discrete-time Markov chain approach to spreading processes in complex networks. EPL (Europhysics Letters), 89(3), 38009. 1 0. Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (201 4). Multilayer networks. Journal of Complex Networks, 2(3), 203–271 . 11 . Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4-5), 1 75–308. 1 2. Holme, P., & Saramäki, J. (201 2). Temporal networks. Physics Reports, 51 9(3), 97–1 25. 1 3. Prakash, B. A., Chakrabarti, D., Faloutsos, M., Valler, N., & Faloutsos, C. (201 2). Threshold conditions for arbitrary epidemic models on arbitrary networks. IEEE Transactions on Knowledge and Data Engineering, 24(4), 607–620. 1 4. Zhang, H. F., Xie, J. R., Tang, M., & Lai, Y. C. (201 4). Suppressing disease spreading by health awareness on complex networks. Chaos, 24(4), 0431 06. 1 5. Biamonte, J., Faccin, M., & De Domenico, M. (201 9). Complex networks from classical to quantum. Communications Physics, 2(1 ), 53.
Publication Date
9/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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