Malware Propagation on Social Time Varying Networks: A Comparative Study of Machine Learning Frameworks

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A.A. Ojugo 1,* E. Ben-Iwhiwhu 1 O. Kekeje 1 M.O. Yerokun 2 I.J.B. Iyawa 2

1. Department of Mathematics/Computer Science, Federal University of Petroleum Resources Effurun, Nigeria

2. Department of Computer Sci., Federal College of education (Technical) Asaba, Delta State, Nigeria

* Corresponding author.


Received: 16 May 2014 / Revised: 21 Jun. 2014 / Accepted: 10 Jul. 2014 / Published: 8 Aug. 2014

Index Terms

Stochastic, immunize, network, graph, SIS, SIR.


Significant research into the logarithmic analysis of complex networks yields solution to help minimize virus spread and propagation over networks. This task of virus propagation is been a recurring subject, and design of complex models will yield modeling solutions used in a number of events not limited to and include propagation, dataflow, network immunization, resource management, service distribution, adoption of viral marketing etc. Stochastic models are successfully used to predict the virus propagation processes and its effects on networks. The 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. Study samples 25,000 emails 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).

Cite This Paper

A.A. Ojugo, E. Ben-Iwhiwhu, O. Kekeje, M.O. Yerokun, I.J.B. Iyawa, "Malware Propagation on Social Time Varying Networks: A Comparative Study of Machine Learning Frameworks", International Journal of Modern Education and Computer Science (IJMECS), vol.6, no.8, pp. 25-33, 2014. DOI:10.5815/ijmecs.2014.08.04


[1]E. Alpaydin, Introduction to Machine Learning, McGraw Hill publications, ISBN: 0070428077, New Jersey, 2010.
[2]J. Aspnes, K. Chang and A. Yampolsky, Inoculation Strategies for Victims of Viruses and the Sum-of-Squares Partition Problem. In SODA, 2005.
[3]A. L. Barabasi and R. Albert, Emergence of scaling in random network. Science, 286, p23, 1999.
[4]M. Barthelemy, A. Barrat, R. Pastor-Satorras and A. Vespignani, “Dynamical patterns of epidemic outbreaks in complex heterogeneous networks”. Journal of Theoretical Biology, p54, 2005.
[5]C.M. Bishop, Pattern Recognition and Machine Learning, ISBN-13: 978-0387-31073-2, Springer Science and Business Media, LLC, 2006.
[6]M. Boguna, R. Pastor-Satorras and A. Vespignani, Epidemic Spreading in Complex Networks with Degree Correlations. Statistical Mechanics of Complex Networks, p36, 2003.
[7]R. Cohen, S. Havlin and D. Ben-Avraham, Efficient Immunization Strategies for Computer Networks and Populations. Phys. Rev Letters, p232, 2003.
[8]R. Dawkins, The Selfish Gene (2nd edition) Oxford University Press, 1989.
[9]R. Dawkins, Viruses of the Mind in B. Dahlbom (Ed.) Dennett and his Critics: Demystifying the Mind, Blackwell, USA, p12, 1993.
[10]P. Desai, Towards an Undetectable Computer Virus, Masters Thesis, Department of Computer Science, San Jose State University, 2008.
[11]Z. Dezso and A. Barabasi, Halting Virus in Scale-free Networks. Phys. Rev E66, p67, 2002.
[12]E. Filiolel, Computer Viruses: Theory to Applications, Springer, ISBN: 2287-23939-1, 2005.
[13]A. Ganesh, L. Massouli and D. Towsley, “The Effect of Network Topology on the Spread of Epidemics”. In IEEE INFOCOM, 2005.
[14]C. Gao, J. Liu and N. Zhong, Network Immunization and Virus Propagation in Emails Network: Experiment and Evaluation Analysis, Knowledge and Information Systems, 27(2), p253-279, 2011.
[15]G. Giakkoupis, A. Gionis, E. Terzi and P. Tsaparas Models and Algorithms for Network Immunization, Engr. Letters, 243, p89, 2010.
[16]P. Harrington, Machine Learning in Action, Manning publications, ISBN: 9781617290183, New York, 2012.
[17]D. Kempe, J. Kleinberg and E. Tardos, Maximizing the Spread of Influence through a Social Network. In SIGKDD, 2003.
[18]W. Kermack and A. McKendrick, A Contribution to the Mathematical Theory of Epidemics. Proceedings Royal Society London, 1927.
[19]J. Kleinberg, Cascading Behavior in Networks: Algorithmic Economic issues. Algorithmic game theory, NY, 2007.
[20]M. Lahiri, A.S. Maiya, R. Sulo, K. Habiba and T. Y. Berger-Wolf, “The Impact of Structural Changes on Predictions of Diffusion in Networks”. In IEEE ICDM Workshops, p939, 2008.
[21]M. Lahiri and M. Cebrain, The Genetic Algorithm as a General Diffusion Model for Social Networks, Association for the Advancement of Artificial Intelligence (, 2010.
[22]T. M. Mitchell, Machine Learning, McGraw Hill publications, ISBN: 0070428077, New Jersey, 1997.
[23]M. E. J. Newman, The Structure and Function of Complex Networks. SIAM Reviews, 45(2), p167–256, 2003.
[24]A. Ojugo, A. Eboka, E. Okonta, R. Yoro and F. Aghware, “GA Rule-based Intrusion Detection System”, Journal of Computing and Information Systems, 3(8), p1182, 2012a.
[25]A. A. Ojugo, M. Yerokun, A. Eboka and E. Ugboh, Malware Propagation on Networks: Analysis, Propagation and Detection, Technical-Report, Centre for High Performance and Dynamic Computing, TRON-02-2012-01, Federal Univ.of Petroleum Resource, Nigeria, p45, 2012b.
[26]A. A. Ojugo, Virus Propagation on Time Varying Graphs, Technical-Report, Centre for High Performance and Dynamic Computing,TRON-03-2013-01, Federal Univ. of Petroleum Resources, Nigeria, p24-37, 2013a.
[27]A. A. Ojugo, and R. Yoro, Computational Intelligence in Stochastic Solution of Toroidal Queen, Progress in Intelligence Computing Applications, 2(1), doi: 10.4156/pica.vol2.issue1.4, p46, 2013b.
[28]A. A. Ojugo, J. Emudianughe, R. E. Yoro, E. O. Okonta and A. O. Eboka, “Hybrid Artificial Neural Network Gravitational Search Algorithm for Rainfall Runoff”, Progress in Intelligence Computing and Applications, 2(1), doi: 10.4156/pica.vol2.issue1.2, p22, 2013c.
[29]A. A. Ojugo, M. Yerokun, A. Eboka and E. Ugboh, Virus Propagation on a Time Varying Network: Analysis and Detection, Technical-Report, Centre for High Performance and Dynamic Computing, TRON-03-2013-12, Federal Univ. of Petroleum Resources, Nigeria, p234, 2013d.
[30]R. Pastor-Satorras and A. Vespignani, Epidemics and Immunization in Scale-free Networks. Handbook of Graphs and Networks: From the Genome to the Internet, 2002.
[31]P. Singhal and N. Raul, “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, 2012.
[32]P. Szor, The Art of Computer Virus Research and Defense, Addison Wesley Symantec Press. ISBN-10: 0321304543, New Jersey, 2005.
[33]Y. Wang, D. Chakrabarti, C. Wang and C. Falousos, Epidemic Spreading in Real Networks: An Eigenvalue viewpoint. In SRDS, 2003.
[34]D. J. Watts, Networks, Dynamics and the Small World Phenomenon. American Journal of Sociology, 105, p234-245, 1999.