Volume 3, Issue 4, August 2015, Page: 56-62
Evolutionary Model for Virus Propagation on Networks
Arnold Adimabua Ojugo, Dept. of Math/Computer, Federal University of Petroleum Resources Effurun, Delta State, Nigeria
Fidelis Obukowho Aghware, Dept. of Computer Science Education, College of Education, Agbor, Delta State, Nigeria
Rume Elizabeth Yoro, Dept. of Computer Sci., Delta State Polytechnic, Ogwashi-Uku, Delta State, Nigeria
Mary Oluwatoyin Yerokun, Dept. of Computer Sci. Education, Federal College of Education (Technical), Asaba, Delta State, Nigeria
Andrew Okonji Eboka, Dept. of Computer Sci. Education, Federal College of Education (Technical), Asaba, Delta State, Nigeria
Christiana Nneamaka Anujeonye, Dept. of Computer Sci. Education, Federal College of Education (Technical), Asaba, Delta State, Nigeria
Fidelia Ngozi Efozia, Prototype Engineering Development Institute, Fed. Ministry of Science Technology, Osun State, Nigeria
Received: Jul. 11, 2015;       Accepted: Jul. 21, 2015;       Published: Jul. 31, 2015
DOI: 10.11648/j.acis.20150304.12      View  3411      Downloads  66
Abstract
The significant research activity into the logarithmic analysis of complex networks will yield engines that will minimize virus propagation over networks. This task of virus propagation is a recurring subject and design of complex models will yield solutions used in a number of events not limited to and include its propagation, network immunization, resource management, capacity service distribution, dataflow, adoption of viral marketing amongst others. Machine learning, stochastic models are successfully employed to predict virus propagation and its effects on networks. This study employs SI-models for independent cascade and the dynamic models with Enron dataset (of e-mail addresses) and presents comparative result using varied machine models. It samples 25,000 e-mails of Enron dataset with Entropy and Information Gain computed to address issues of blocking, targeting and extent of virus spread on graphs. Study addressed the problem of the expected spread immunization and the expected epidemic spread minimization; but not the epidemic threshold (for space constraint).
Keywords
Stochastic, Immunize, Network, Vertices, SIS, SIR, Search Space, Solution, Models
To cite this article
Arnold Adimabua Ojugo, Fidelis Obukowho Aghware, Rume Elizabeth Yoro, Mary Oluwatoyin Yerokun, Andrew Okonji Eboka, Christiana Nneamaka Anujeonye, Fidelia Ngozi Efozia, Evolutionary Model for Virus Propagation on Networks, Automation, Control and Intelligent Systems. Vol. 3, No. 4, 2015, pp. 56-62. doi: 10.11648/j.acis.20150304.12
Reference
[1]
Alpaydin, E., (2010). Introduction to Machine Learning, McGraw Hill publications, ISBN: 0070428077, New Jersey.
[2]
Aspnes, J., Chang, K and Yampolskiy, A., (2005). Inoculation strategies for victims of viruses and the sum-of-squares partition problem. In SODA.
[3]
Barabasi, A.L and Albert, R., (1999). Emergence of scaling in random networks. Science, 286, p23.
[4]
Barthelemy, M., Barrat, A., Pastor-Satorras, R and Vespignani, A. (2005). Dynamical patterns of epidemic outbreaks in complex heterogeneous networks. Journal of Theoretical Biology, p54.
[5]
Boguna, M., Pastor-Satorras, R and Vespignani, A., (2003). Epidemic spreading in complex networks with degree correlations. Statistical Mechanics of Complex Networks, p36.
[6]
Cohen, R., Havlin, S and Ben-Avraham, D., (2003). Efficient immunization strategies for computer networks and populations. Phys Rev Letters, p232.
[7]
Dezso, Z and Barabasi, A.L., (2002). Halting viruses in scale-free networks. Phys. Rev. E 66, p67.
[8]
Filiol, E., (2005). Computer Viruses: from Theory to Applications, Springer, ISBN 10: 2287-23939-1.
[9]
Ganesh, A., Massouli, L and Towsley, D., (2005). The effect of network topology on the spread of epidemics. In IEEE INFOCOM.
[10]
Harrington, P., (2012). Machine Learning in action, Manning publications, ISBN: 9781617290183, NY.
[11]
Kempe, D., Kleinberg, J and Tardos, E., (2003). Maximizing the spread of influence through a social network. In SIGKDD.
[12]
Kermack, W and McKendrick, A., (1927). A contribution to the mathematical theory of epidemics. Proceedings Royal Society London.
[13]
Mitchell, T.M., (1997). Machine Learning, McGraw Hill publications, ISBN: 0070428077, New Jersey.
[14]
Newman, M.E., (2003). The structure and function of complex networks. SIAM Reviews, 45(2), p167.
[15]
Ojugo, A., Eboka, A., Okonta, E., Yoro, R and Aghware, F., (2012). GA rule-based intrusion detection system, J. of Computing and Information Systems, 3(8), p1182.
[16]
Ojugo, A.A., and Yoro, R., (2013a). Computational intelligence in stochastic solution for Toroidal Queen task, Progress in Intelligence Computing Applications, 2(1), 10.4156/pica.vol2.issue1.4, p46.
[17]
Ojugo, A.A., Emudianughe, J., Yoro, R.E., Okonta, E.O and Eboka, A.O., (2013b). Hybrid artificial neural network gravitational search algorithm for rainfall, Progress in Intelligence Computing and Applications, 2(1), 10.4156/pica.vol2.issue1.2, p22.
[18]
Pastor-Satorras, R and Vespignani, A., (2002). Epidemics and immunization in scale-free networks. Handbook of Graphs and Networks: From the Genome to the Internet.
[19]
Singhal, P and Raul, N., (2012). Malware detection module using machine learning algorithm to assist centralized security in Enterprise networks, Int. J. Network Security and Applications, 4(1), doi: 10.5121/ijnsa.2012.4106, p61.
[20]
Szor, P., (2005). The Art of Computer Virus Research and Defense, Addison Wesley Symantec Press. ISBN-10: 0321304543, New Jersey.
[21]
Wang, Y., Chakrabarti, D., Wang, C and Faloutsos, C., (2003). Epidemic spreading in real networks: An eigenvalue viewpoint. In SRDS.
[22]
Watts, D.J., (1999). Networks, dynamics and the small world phenomenon. American Journal of Sociology, 105, p234-245.
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