A Reliable numerical scheme for a computer virus propagation model

In this paper, we apply the Mickens’methodology to construct a dynamically consistentnonstandard finite difference (NSFD) scheme for acomputer virus propagation model. It is proved thatthe constructed NSFD scheme correctly preservesessential mathematical features of the continuous-timemodel, which are positivity, boundedness and asymptotic stability. Consequently, we obtain an effectivenumerical scheme that can provide reliable approximations for the computer virus propagation model.Meanwhile, some typical standard finite differenceschemes fail to preserve the essential properties ofthe computer virus propagation model; hence, theycan generate numerical approximations which arenot only negative but also unstable. Finally, a setof numerical experiments is performed to supportthe theoretical results as well as to demonstrate theadvantage of the NSFD scheme over standard ones.As we expected, there is a good agreement betweenthe numerical results and theoretical assertions.

Effects of employee monitoring notification policies on HR manager opinions

As a variety of electronic monitoring methods such as global positioning systems are available, monitoring employees without notice is a consideration even though several laws ban it and ethical questions remain. Monitoring without notice has risks that Human Resources (HR) managers should consider when they set monitoring policies to enhance knowledge management. A total of 174 HR managers were asked about their top reasons to electronically monitor employees with or without notice. About half received information that a company did not notify employees of electronic monitoring and the other half received the opposite information. Prospect theory was the basis for collecting data to understand the importance of risk in setting policies. It states that people in perceived good conditions avoid risk because they feel there is more to lose than to gain. The leading reason to electronically monitor employees for both groups was computer virus and malware protection. Organizational threats associated with legal issues showed more HR support for monitoring without notice. Opportunities associated with employee productivity indicated relatively more support for monitoring with notice. As a result of this research, perceived threats in the workplace are significant reasons why HR managers might not provide notice of monitoring in the workplace. This has potential legal and ethical implications.

Stability Analysis of a Fractional-Order SEIR-KS Computer Virus-Spreading Model with Two Delays

In this paper, the stability and Hopf bifurcation of a fractional-order model of the Susceptible-Exposed-Infected-Kill Signals Recovered (SEIR-KS) computer virus with two delays are studied. The sufficient conditions for solving the stability and the occurrence of Hopf bifurcation of the system are established by using Laplace transform, stability theory, and Hopf bifurcation theorem of fractional-order differential systems. The research shows that time delays and fractional order q have an important effect on the stability and the emergence of Hopf bifurcation of the fractional computer virus model. In addition, the validity of the theoretical analysis is verified by selecting appropriate system parameters for numerical simulation and the biological correlation of the equilibrium point is discussed. The results show that the bifurcation point of the model increases with the decrease in the model fractional order q. Under the same fractional order q, the effects of different types of delays on bifurcation points are obviously different.

Analysis of Optimal Control Strategies for Preventing Computer Virus Infection and Reduce Program Files Damage with Other Symptoms

Program files damage and other computer virus symptoms has become a very threatening issue to computer performance. This paper considered an model with incidence of infected and program files damaged computers and saturated incidence of vaccination and treatment function. Two control functions have been used; one for vaccinating the susceptible computer population and the other for the treatment of the program files damaged computer population. The Pontryagin’s Maximum Principle has been used to characterize the optimal control whose numerical results show the positive impact of the two controls used for controlling the infection dynamics of computer virus. Actually the intention of this study is to minimize the number of infected and program files damaged computer systems and at the same time minimize the cost associated to the controls. Efficiency analysis is also studied to determine the best control strategy among vaccination and treatment. Numerical simulations were carried out in this model to demonstrate the analytical results and it was revealed that combination of vaccination and treatment is the most successful way to minimize the incidence of program files damage.

Predicting Spread Probability of Learning-Effect Computer Virus

With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments.