I.J.B. Iyawa

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

E-mail: iyawaben@hotmail.com


Research Interests: Software Creation and Management, Software Development Process, Software Organization and Properties, Computer systems and computational processes, Data Structures and Algorithms


Iyawa, Ifeyinwa Jane received BSc from Univ. of Lagos in 2000 and MSc from NnamdiAzikiwe Univ. Awka in 2005 and currently a PhD student at Ebonyi State Uni. Abakiliki (in Computer Sci.). Currently lectures at Dept of Computer, Federal College of Education (Technical) Asaba, Delta State. Her re-search interests in: Software Evolution and Data Communica-tions. She is a member of: International Association of Engi-neers (IAENG), Nigerian Computer Society (NCS) and Com-puter Professionals of Nigeria (CPN). Her details are: +2348163165807 / iyawaben@hotmail.com

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

By A.A. Ojugo E. Ben-Iwhiwhu O. Kekeje M.O. Yerokun I.J.B. Iyawa

DOI: https://doi.org/10.5815/ijmecs.2014.08.04, Pub. Date: 8 Aug. 2014

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).

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