Modeling the Spread of Virus on a Network for Optimum Network Configuration

Catherine Roy Alimboyong


In the era of cloud computing, many internet users are concerned about the security of personal information and whether it is used appropriately. Email, for instance, is one of the most convenient and indispensable communication media borne out of advancement in this epoch of modernization. The advent of such innovation has also prompted the conception of the so-called computer virus or malware which has quickly evolved. The spread of computer virus is analogous to the spread of an infectious epidemic disease in a population. It has the ability to spread rapidly over the networks by various means such as access to online social networking sites like twitter, Facebook, and emails, exploiting vulnerabilities, and etc. Infections can go from being little dangerous to significantly harmful for a network. However, to my knowledge, no simulation model that address the spread of computer virus on a network using NetLogo. This paper proposed a simulation model that can predict the propagation of virus including the trend and the average infection rate using NetLogo. Observed and simulated data sets were validated using chi-square tests. The results of the experiment have demonstrated accurate performance of the proposed model. To this regard, the model could be very helpful for network administrators to apply practicable measures to constrain the spread of computer virus other than the usual prevention scheme particularly the use of antivirus software and inclusion of firewall security.


Computer Virus, NetLogo, Network Security, SIR


Amador, J. (2016). The SEIQS stochastic epidemic model with external source of infection. Applied Mathematical Modelling, 40(19–20), 8352–8365.

Bamaarouf, O., Ould Baba, A., Lamzabi, S., Rachadi, A., & Ez-Zahraouy, H. (2017). Effects of maximum node degree on computer virus spreading in scale-free networks. International Journal of Modern Physics B, 31(26), 1–10.

Chen, L., Hattaf, K., & Sun, J. (2015). Optimal control of a delayed SLBS computer virus model. Physica A: Statistical Mechanics and Its Applications, 427, 244–250.

Liang, X., Pei, Y., & Lv, Y. (2018). Modeling the state dependent impulse control for computer virus propagation under media coverage. Physica A: Statistical Mechanics and Its Applications, 491, 516–527.

Liu, W., Liu, C., Liu, X., Cui, S., & Huang, X. (2016). Modeling the spread of malware with the influence of heterogeneous immunization. Applied Mathematical Modelling, 40(4), 3141–3152.

Long, L., Zhong, K., & Wang, W. (2018). Malicious viruses spreading on complex networks with heterogeneous recovery rate. Physica A: Statistical Mechanics and Its Applications, 509, 746–753.

Maji, G., Mandal, S., & Sen, S. (2020). A systematic survey on influential spreaders identification in complex networks with a focus on K-shell based techniques. Expert Systems with Applications, 161, 113681.

Ren, J., Yang, X., Zhu, Q., Yang, L. X., & Zhang, C. (2012). A novel computer virus model and its dynamics. Nonlinear Analysis: Real World Applications, 13(1), 376–384.

Riedl, P., Mayrhofer, R., Möller, A., Kranz, M., Lettner, F., Holzmann, C., & Koelle, M. (2015). Only play in your comfort zone: interaction methods for improving security awareness on mobile devices. Personal and Ubiquitous Computing, 19(5–6), 941–954.

Tissue, S., & Wilensky, U. (2004). Netlogo: A simple environment for modeling complexity. Conference on Complex Systems, 1–10. Retrieved from

Upadhyay, R. K., & Singh, P. (2020). Modeling and control of computer virus attack on a targeted network. Physica A: Statistical Mechanics and Its Applications, 538, 122617.

Tissue S., Wilensky U. (2004). Netlogo: a simple environment for modeling complexity, in Proceedings of the International Conference on Complex Systems (Boston, MA: The Pennsylvania State University; ), 1–10

Wilensky, U., (2007) NetLogo Solid Diffusion model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Wu, Y., Li, P., Yang, L. X., Yang, X., & Tang, Y. Y. (2017). A theoretical method for assessing disruptive computer viruses. Physica A: Statistical Mechanics and Its Applications, 482, 325–336.

Yang, L. X., & Yang, X. (2012). Propagation behavior of virus codes in the situation that infected computers are connected to the internet with positive probability. Discrete Dynamics in Nature and Society, 2012.

Yang, L. X., & Yang, X. (2014a). The pulse treatment of computer viruses: A modeling study. Nonlinear Dynamics, 76(2), 1379–1393.

Yang, L. X., & Yang, X. (2014b). The spread of computer viruses over a reduced scale-free network. Physica A: Statistical Mechanics and Its Applications, 396, 173–184.

Zhang, B., Zhang, L., Mu, C., Zhao, Q., Song, Q., & Hong, X. (2019). A most influential node group discovery method for influence maximization in social networks: A trust-based perspective. Data and Knowledge Engineering, 121(November 2018), 71–87.

Zhang, M., Song, G., & Chen, L. (2016). A state feedback impulse model for computer worm control. Nonlinear Dynamics, 85(3), 1561–1569.

Dubey, R., Bharadwaj, S., Zafar, M. I., Bhushan Sharma, V., and Biswas, S.: Collaborative noise mapping using smartphone, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 253–260,, 2020.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics