Computer Virus Propagation in a Network Organization: The Interplay between Social and Technological Networks

This paper proposes a holistic view of a network organization’s computing environment to examine computer virus propagation patterns. We empirically examine a large-scale organizational network consisting of both social network and technological network. By applying information retrieval techniques, we map nodes in the social network to nodes in the technological network to construct the composite network of the organization. We apply social network analysis to study the topologies of social and technological networks in this organization. We statistically test the impact of the interplay between social and technological network on computer virus propagation using a susceptible-infective-recovered epidemic process. We find that computer viruses propagate faster but reach lower level of infection through technological network than through social network, and viruses propagate the fastest and reach the highest level of infection through the composite network. Overlooking the interplay of social network and technological network underestimates the virus propagation speed and the scale of infection.

[1]  M. Newman,et al.  Epidemics and percolation in small-world networks. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  R. May,et al.  Infection dynamics on scale-free networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Kathleen M. Carley,et al.  The impact of countermeasure propagation on the prevalence of computer viruses , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Maureen S. Battistella,et al.  Connections: New Ways of Working in the Networked Organization , 1991 .

[6]  Mark E. J. Newman,et al.  Technological Networks and the Spread of Computer Viruses , 2004, Science.

[7]  Alessandro Vespignani,et al.  Large scale networks fingerprinting and visualization using the k-core decomposition , 2005, NIPS.

[8]  Marshall W. van Alstyne,et al.  The State of Network Organization: A Survey in Three Frameworks , 1997, J. Organ. Comput. Electron. Commer..

[9]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Jeffrey O. Kephart,et al.  Measuring and modeling computer virus prevalence , 1993, Proceedings 1993 IEEE Computer Society Symposium on Research in Security and Privacy.

[11]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[12]  Jeffrey O. Kephart,et al.  Directed-graph epidemiological models of computer viruses , 1991, Proceedings. 1991 IEEE Computer Society Symposium on Research in Security and Privacy.