The application of epidemiology to computer viruses
Computers and Security
A mathematical theory for the spread of computer viruses
Computers and Security
On computer viral infection and the effect of immunization
ACSAC '00 Proceedings of the 16th Annual Computer Security Applications Conference
Short Term and Total Life Impact analysis of email worms in computer systems
Decision Support Systems
Scalable modeling of real graphs using Kronecker multiplication
Proceedings of the 24th international conference on Machine learning
Epidemic thresholds in real networks
ACM Transactions on Information and System Security (TISSEC)
RTG: a recursive realistic graph generator using random typing
Data Mining and Knowledge Discovery
Towards controlling virus propagation in information systems with point-to-group information sharing
Decision Support Systems
An Economic Analysis of the Software Market with a Risk-Sharing Mechanism
International Journal of Electronic Commerce
On the Vulnerability of Large Graphs
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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This paper proposes a self-similar selection method as an alternative to existing immunization strategies for online networks. Given the self-similar characteristics of online networks which are shown to have fractal and scale-free structure, we presume that the self-similar selection which is well developed in physics outperforms random or targeted vaccination based on incoming or outgoing connections. We examine the effectiveness of the proposed self-similar selection method with random vaccination and other different types of targeted vaccination strategies in terms of delaying the spread of computer virus over a scale-free computer network constructed using real-world World Wide Web data. Our computer simulation results indicate that the self-similar selection method is more effective in deterring virus propagation than the existing vaccination strategies. In addition, vaccination based on self-similar selection is practical since it does not require detailed information about network morphology at the individual node level, which is often not easy to observe. Our findings have significant implications for both policy makers and network security providers.