Effect of terminal boundary protection on the spread of computer viruses: modeling and simulation

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. Introduction

The campus network provides high-speed and efficient network connections for the school, supports a variety of network protocols and management strategies, and meets the different needs of its users. It also provides various network services to facilitate the administrative management and the use of faculty and students. Characterized by high bandwidth, wide coverage, intensive information interaction, and many users, the campus network has become an indispensable part of academic and campus life. However, due to these characteristics, computer viruses such as Trojan horses, malware, and worms spread quickly in the campus network, over a wide range, and with strong hidden and destructive characteristics (Husain and Abubakar, 2015; Husain and Suleiman, 2015; Odule and Kaka, 2018; Almiani et al., 2020; Yang LX et al., 2021a; Chen et al., 2023). When facing the lateral spread of computer viruses, universities usually suppress and block the spread by strengthening the security policy of network security equipment, updating terminal system patches, and installing terminal anti-virus software (Yang LX et al., 2016, 2021b; Zhang XL and Gan, 2017; Lanz et al., 2019; Bahashwan and Al-Tuwairqi, 2021; Epiphaniou et al., 2023). Currently, most universities rely on only the above technical means to carry out network security protection, and do not have a deeper level to explore the cross-spread characteristic of computer viruses in the campus network.

Epidemic models, spreading dynamics, and computer virus spreading are hot topics in academic circles (Tanaka et al., 2014; Zhang HF et al., 2014; Wu and Chen, 2017; Cao et al., 2020). Yang XF and Yang (2012) proposed the SLBS model based on the typical computer virus spreading process. Gan et al. (2014) developed a dynamic model with two kinds of generic nonlinear probabilities (incidence rate and vaccination probability), pointing out that the generic nonlinear vaccination helps strengthen computer security. Based on the delay-varying SIRC model, Ren et al. (2013) introduced an isolation mechanism to maintain a relatively high number of recovered nodes and a low number of infected nodes to suppress the spread of computer viruses. Zhang CM (2018) proposed a new linear computer virus spread model on multilayer networks based on the SLBS model. Fatima et al. (2018) proposed an SLBQS computer virus dynamics. Jackson and Chen-Charpentier (2017) constructed the SIR time-delay diffusion model. To further combat virus spread, Zhang XL and Li (2020) addressed a dynamic model that incorporates nonlinear countermeasure probability and infected removable storage media. Nian et al. (2022) studied the propagation relationship of Weibo users and the classical infectious disease model, and proposed the mechanism, effects (impulse effect, clock effect, and herding effect), and scale of virus propagation in online information dissemination. Liu and Wang (2016) proposed an SIQR epidemic model with a nonlinear incidence rate and two time delays. They employed this model to analyze the local stability and the existence of Hopf bifurcation ...