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With the advent and worldwide development of Internet, the study and control of malware spreading has become very important. In this sense, some mathematical models to simulate malware propagation have been proposed in the scientific literature, and usually they are based on differential equations exploiting the similarities with mathematical epidemiology. The great majority of these models study the behavior of a particular type of malware called computer worms; indeed, to the best of our knowledge, no model has been proposed to simulate the spreading of a computer virus (the traditional type of malware which differs from computer worms in several aspects). In this sense, the purpose of this work is to introduce a new mathematical model not based on continuous mathematics tools but on discrete ones, to analyze and study the epidemic behavior of computer virus. Specifically, cellular automata are used in order to design such model.

The impact of anti-virus software on the spreading of computer virus is investigated via developing a mathematical model in this paper. Considering the anti-virus software may not be effective, as it may be an outdated version, and then the computers may be infected with a reduced incidence rate. According to the method of next generation matrix, the basic reproduction number is derived. By introducing appropriate Lyapunov function and the Routh stability criterion, acquiring the stability conditions of the virus-free equilibrium and virus equilibrium. The effect of anti-virus software and disconnecting rate on the spreading of virus are also analyzed. When combined with the numerical results, a set of suggestions are put forward for eradicating virus effectively.

In this paper, we propose a new model for computer virus attacks and recovery at the level of information packets. The model we propose is based on one hand on the susceptible-infected (SI) and susceptible-infected-recovered (SIR) stochastic epidemic models for computer virus propagation and on the other hand on the time-discrete Markov chain of the minimal traffic routing protocol. We have applied this model to the scale free Barabasi–Albert network to determine how the dynamics of virus propagation is affected by the traffic flow in both the free-flow and the congested phases. The numerical results show essentially that the proportion of infected and recovered packets increases when the rate of infection $\lambda$ and the recovery rate $\beta$ increase in the free-flow phase while in the congested phase, the number of infected (recovered) packets presents a maximum (minimum) at certain critical value of $\beta$ characterizing a certain competition between the infection and the recovery rates.

In this paper, a delayed computer virus propagation model with a saturation incidence rate and a time delay describing temporary immune period is proposed and its dynamical behaviors are studied. The threshold value ${\Re }_0$ is given to determine whether the virus dies out completely. By comparison arguments and iteration technique, sufficient conditions are obtained for the global asymptotic stabilities of the virus-free equilibrium and the virus equilibrium. Taking the delay as a parameter, local Hopf bifurcations are demonstrated. Furthermore, the direction of Hopf bifurcation and the stabilities of the bifurcating periodic solutions are determined by the normal form theory and the center manifold theorem for functional differential equations (FDEs). Finally, numerical simulations are carried out to illustrate the main theoretical results.

The increase of the use of the Internet networks favors the spread of viruses. In this paper, we studied the spread of viruses in the scale-free network with different topologies based on the Susceptible–Infected–External (SIE) model. It is found that the network structure influences the virus spreading. We have shown also that the nodes of high degree are more susceptible to infection than others. Furthermore, we have determined a critical maximum value of node degree $(K_c)$, below which the network is more resistible and the computer virus cannot expand into the whole network. The influence of network size is also studied. We found that the network with low size is more effective to reduce the proportion of infected nodes.

In this paper, we introduce the effect of neighbors on the infection of packets by computer virus in the SI and SIR models using the minimal traffic routing protocol. We have applied this model to the Barabasi–Albert network to determine how intrasite and extrasite infection rates affect virus propagation through the traffic flow of information packets in both the free-flow and the congested phases. The numerical results show that when we change the intrasite infection rate ${\lambda }_1$ while keeping constant the extrasite infection rate ${\lambda }_2$, we get normal behavior in the congested phase: in the network, the proportion of infected packets increases to reach a peak and then decreases resulting in a simultaneous increase of the recovered packets. In contrast, when the intrasite infection rate ${\lambda }_1$ is kept fixed, an increase of the extrasite infection rate results in two regimes: The first one is characterized by an increase of the proportion of infected packets until reaching some peak value and then decreases smoothly. The second regime is characterized by an increase of infected packets to some stationary value.

This study shows the design of the Meyer wavelet neural networks (WNNs) to perform the numerical solutions of the spread of computer virus with kill signals, i.e. SEIR-KS system. The optimization of the SEIR-KS system is performed by the Meyer WNNs together with the optimization through the genetic algorithm (GA) and sequential quadratic (SQ) programming, i.e. Meyer WNNs-GASQ programming. A sigmoidal-based log-sigmoid function is implemented as an activation function, while 10 numbers of neurons work with 120 variables throughout this study. The correctness of the proposed Meyer WNNs-GASQP programming is observed through the comparison of the obtained and reference numerical solutions. For the consistency and reliability of the Meyer WNNs-GASQ programming, an analysis based on different statistical procedures is performed using 40 numbers of independent executions. Moreover, the use of different statistical operators like mean, median, minimum, standard deviation and semi-interquartile range further validates the correctness of the Meyer WNNs-GASQ programming for solving the SEIR-KS system.

This paper proposes a mathematical model to investigate the dynamic behaviors of a modified computer virus model using the Atangana–Baleanu fractional derivative in the Caputo sense, aiming to elucidate the connection between its parameters and network attributes. By introducing a relatively new numerical method, we address the memory-dependent and nonlocal features of the system. The existence and uniqueness of the model’s solution are confirmed. To explore solution trajectories and assess the impact of various input factors on computer virus dynamics, we employ an efficient numerical technique. Our simulations provide insights into the consequences of fractional order, anti-virus measures, asymptotic fraction, damage rate, and removal rate in the system. These findings illuminate the relationships between model parameters, facilitating the design of networks that minimize the risk of virus outbreaks and prevent future cyber threats. By identifying critical factors involved in the progression of viruses, these results enable the development of more effective defense mechanisms.