
CLC number: TP309.5
On-line Access: 2025-10-13
Received: 2024-03-29
Revision Accepted: 2024-09-18
Crosschecked: 2025-10-13
Cited: 0
Clicked: 1403
Kai GAO, Lixin ZHANG, Yabing YAO, Yang YANG, Fuzhong NIAN. Effect of terminal boundary protection on the spread of computer viruses: modeling and simulation[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1637-1648.
@article{title="Effect of terminal boundary protection on the spread of computer viruses: modeling and simulation",
author="Kai GAO, Lixin ZHANG, Yabing YAO, Yang YANG, Fuzhong NIAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="9",
pages="1637-1648",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400236"
}
%0 Journal Article
%T Effect of terminal boundary protection on the spread of computer viruses: modeling and simulation
%A Kai GAO
%A Lixin ZHANG
%A Yabing YAO
%A Yang YANG
%A Fuzhong NIAN
%J Frontiers of Information Technology & Electronic Engineering
%V 26
%N 9
%P 1637-1648
%@ 2095-9184
%D 2025
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400236
TY - JOUR
T1 - Effect of terminal boundary protection on the spread of computer viruses: modeling and simulation
A1 - Kai GAO
A1 - Lixin ZHANG
A1 - Yabing YAO
A1 - Yang YANG
A1 - Fuzhong NIAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 9
SP - 1637
EP - 1648
%@ 2095-9184
Y1 - 2025
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400236
Abstract: The diversity and complexity of the user population on the campus network increase the risk of computer virus infection during terminal information interactions. Therefore, it is crucial to explore how computer viruses propagate between terminals in such a network. In this study, we establish a novel computer virus spreading model based on the characteristics of the basic network structure and a classical epidemic-spreading dynamics model, adapted to real-world university scenarios. The proposed model contains six groups: susceptible, unisolated latent, isolated latent, infection, recovery, and crash. We analyze the proposed model’s basic reproduction number and disease-free equilibrium point. Using real-world university terminal computer virus propagation data, a basic computer virus infection rate, a basic computer virus removal rate, and a security protection strategy deployment rate are proposed to define the conversion probability of each group and perceive each group’s variation tendency. Furthermore, we analyze the spreading trend of computer viruses in the campus network in terms of the proposed computer virus spreading model. We propose specific measures to suppress the spread of computer viruses in terminals, ensuring the safe and stable operation of the campus network terminals to the greatest extent.
[1]Alhebshi RM, Ahmed N, Baleanu D, et al., 2023. Modeling of computer virus propagation with fuzzy parameters. Comput Mater Contin, 74(3):5663-5678.
[2]Almiani M, AbuGhazleh A, Al-Rahayfeh A, et al., 2020. Deep recurrent neural network for IoT intrusion detection system. Simul Model Pract Theory, 101:102031.
[3]Bahashwan WS, Al-Tuwairqi SM, 2021. Modeling the effect of external computers and removable devices on a computer network with heterogeneous immunity. Int J Differ Equ, 2021:6694098.
[4]Cao JD, Liu Y, Lu JQ, et al., 2020. Complex systems and networks with their applications. Front Inform Technol Electron Eng, 21(2):195-198.
[5]Chen J, Wu DD, Xie RY, 2023. Artificial intelligence algorithms for cyberspace security applications: a technological and status review. Front Inform Technol Electron Eng, 24(8):1117-1142.
[6]Dietz K, 1988. The first epidemic model: a historical note on P.D. EN’KO. Aust J Stat, 30A(1):56-65.
[7]Epiphaniou G, Hammoudeh M, Yuan H, et al., 2023. Digital twins in cyber effects modelling of IoT/CPS points of low resilience. Simul Model Pract Theory, 125:102744.
[8]Fatima U, Ali M, Ahmed N, et al., 2018. Numerical modeling of susceptible latent breaking-out quarantine computer virus epidemic dynamics. Heliyon, 4(5):e00631.
[9]Gan CQ, Yang XF, Zhu QY, 2014. Global stability of a computer virus propagation model with two kinds of generic nonlinear probabilities. Abstr Appl Anal, 2014:735327.
[10]Hoang MT, Ngo TKQ, Tran DH, 2023. Dynamically consistent nonstandard numerical schemes for solving some computer virus and malware propagation models. Math Found Comput, 6(4):704-727.
[11]Husain R, Abubakar M, 2015. A study on friends model of a computer worm defense system. Int J Eng Appl Sci, 2(3):56-59.
[12]Husain R, Suleiman B, 2015. Modeling and simulation of worm propagation and attacks against campus network. Int J Eng Appl Sci, 2(8):57-60.
[13]Jackson M, Chen-Charpentier BM, 2017. Modeling plant virus propagation with delays. J Comput Appl Math, 309:611-621.
[14]Lanz A, Rogers D, Alford TL, 2019. An epidemic model of malware virus with quarantine. J Adv Math Comput Sci, 33(4):1-10.
[15]Liu J, Wang K, 2016. Hopf bifurcation of a delayed SIQR epidemic model with constant input and nonlinear incidence rate. Adv Differ Equ, 2016:168.
[16]Nian FZ, Li JZ, Diao HY, et al., 2022. Weibo core user mining and propagation scale predicting. Chaos Solit Fract, 156:111869.
[17]Odule TJ, Kaka OA, 2018. Understanding and managing the dynamics of computer viruses. Adv Multidiscip Sci Res J, 4(1):113-120.
[18]Ren JG, Xu YH, Zhang CM, 2013. Optimal control of a delay-varying computer virus propagation model. Discr Dynam Nat Soc, 2013:210291.
[19]Tanaka G, Urabe C, Aihara K, 2014. Random and targeted interventions for epidemic control in metapopulation models. Sci Rep, 4:5522.
[20]Wu QC, Chen SF, 2017. Susceptible-infected-recovered epidemics in random networks with population awareness. Chaos, 27(10):103107.
[21]Yang LX, Yang XF, 2016. The effect of network topology on the spread of computer viruses: a modelling study. Int J Comput Math, 94(8):1591-1608.
[22]Yang LX, Draief M, Yang XF, 2016. The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model. Phys A Stat Mech Appl, 450:403-415.
[23]Yang LX, Huang KF, Yang XF, et al., 2021a. Defense against advanced persistent threat through data backup and recovery. IEEE Trans Netw Sci Eng, 8(3):2001-2013.
[24]Yang LX, Li PD, Yang XF, et al., 2021b. Effective quarantine and recovery scheme against advanced persistent threat. IEEE Trans Syst Man Cybern Syst, 51(10):5977-5991.
[25]Yang XF, Yang LX, 2012. Towards the epidemiological modeling of computer viruses. Discr Dynam Nat Soc, 2012:259671.
[26]Zhang CM, 2018. Global behavior of a computer virus propagation model on multilayer networks. Secur Commun Netw, 2018:2153195.
[27]Zhang HF, Xie JR, Tang M, et al., 2014. Suppression of epidemic spreading in complex networks by local information based behavioral responses. Chaos, 24(4):043106.
[28]Zhang XL, Gan CQ, 2017. Optimal and nonlinear dynamic countermeasure under a node-level model with nonlinear infection rate. Discr Dynam Nat Soc, 2017:2836865.
[29]Zhang XL, Li Y, 2020. Modelling and analysis of propagation behavior of computer viruses with nonlinear countermeasure probability and infected removable storage media. Discr Dynam Nat Soc, 2020:8814319.
Open peer comments: Debate/Discuss/Question/Opinion
<1>