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

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Author(s)

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.

DOI: https://doi.org/10.5815/ijmecs.2014.08.04

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

Index Terms

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

Abstract

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

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