scholarly journals Predicting Spread Probability of Learning-Effect Computer Virus

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wei-Chang Yeh ◽  
Edward Lin ◽  
Chia-Ling Huang
Keyword(s):  
Rapid Development ◽  
Computer Virus ◽  
Node Degree ◽  
Scale Free Network ◽  
Computer Viruses ◽  
Scale Free ◽  
Power Rule ◽  
Fast Pace ◽  
Free Network ◽  
High Connectivity

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.

scholarly journals Effects of Resource Limitations and Cost Influences on Computer Virus Epidemic Dynamics and Tipping Points

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Chung-Yuan Huang ◽  
Chuen-Tsai Sun
Keyword(s):  
Computer Virus ◽  
Tipping Point ◽  
Tipping Points ◽  
Scale Free Network ◽  
Virus Infections ◽  
Scale Free ◽  
Epidemic Dynamics ◽  
Resource Limitations ◽  
Free Network ◽  
Network Power

One of the most important assessment indicators of computer virus infections is epidemic tipping point. Although many researchers have focused on the effects of scale-free network power-law connectivity distributions on computer virus epidemic dynamics and tipping points, few have comprehensively considered resource limitations and costs. Our goals for this paper are to show that (a) opposed to the current consensus, a significant epidemic tipping point does exist when resource limitations and costs are considered and (b) it is possible to control the spread of a computer virus in a scale-free network if resources are restricted and if costs associated with infection events are significantly increased.

TIME-DELAY ROBUSTNESS OF CONSENSUS PROBLEMS IN REGULAR AND COMPLEX NETWORKS

2007 ◽  
Vol 18 (08) ◽  
pp. 1339-1350 ◽  
Author(s):  
ZHENGPING WU ◽  
ZHI-HONG GUAN
Keyword(s):  
Time Delay ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Small World ◽  
Node Degree ◽  
Small World Network ◽  
Scale Free Network ◽  
Star Network ◽  
Scale Free ◽  
Free Network

Recent advances in complex network research have stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. Based on the theory of complex networks and computer simulation, we analyze the robustness to time-delay in linear consensus problem with different network topologies, such as global coupled network, star network, nearest-neighbor coupled network, small-world network, and scale-free network. It is found that global coupled network, star network, and scale-free network are vulnerable to time-delay, while nearest-neighbor coupled network and small-world network are robust to time-delay. And it is found that the maximum node degree of the network is a good predictor for time-delay robustness. And it is found that the robustness to time-delay can be improved significantly by a decoupling process to a small part of edges in scale-free network.

scholarly journals The Fractional Preferential Attachment Scale-Free Network Model

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 509
Author(s):  
Rafał Rak ◽  
Ewa Rak
Keyword(s):  
Network Model ◽  
Real World ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Scale Free Network ◽  
Scale Free ◽  
Degree Correlation ◽  
Free Network

Many networks generated by nature have two generic properties: they are formed in the process of preferential attachment and they are scale-free. Considering these features, by interfering with mechanism of the preferential attachment, we propose a generalisation of the Barabási–Albert model—the ’Fractional Preferential Attachment’ (FPA) scale-free network model—that generates networks with time-independent degree distributions p ( k ) ∼ k − γ with degree exponent 2 < γ ≤ 3 (where γ = 3 corresponds to the typical value of the BA model). In the FPA model, the element controlling the network properties is the f parameter, where f ∈ ( 0 , 1 ⟩ . Depending on the different values of f parameter, we study the statistical properties of the numerically generated networks. We investigate the topological properties of FPA networks such as degree distribution, degree correlation (network assortativity), clustering coefficient, average node degree, network diameter, average shortest path length and features of fractality. We compare the obtained values with the results for various synthetic and real-world networks. It is found that, depending on f, the FPA model generates networks with parameters similar to the real-world networks. Furthermore, it is shown that f parameter has a significant impact on, among others, degree distribution and degree correlation of generated networks. Therefore, the FPA scale-free network model can be an interesting alternative to existing network models. In addition, it turns out that, regardless of the value of f, FPA networks are not fractal.

Effects of maximum node degree on computer virus spreading in scale-free networks

2017 ◽  
Vol 31 (26) ◽  
pp. 1750182 ◽  
Author(s):  
O. Bamaarouf ◽  
A. Ould Baba ◽  
S. Lamzabi ◽  
A. Rachadi ◽  
H. Ez-Zahraouy
Keyword(s):  
Computer Virus ◽  
Network Size ◽  
Node Degree ◽  
Scale Free ◽  
Maximum Value ◽  
Free Network ◽  
Use Of The Internet ◽  
Internet Networks ◽  
Virus Spreading ◽  
High Degree

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 [Formula: see text], 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.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network
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