Evolutionary Model for Virus Propagation on Networks

Arnold Adimabua Ojugo; Fidelis Obukowho Aghware; Rume Elizabeth Yoro; Mary Oluwatoyin Yerokun; Andrew Okonji Eboka; Christiana Nneamaka Anujeonye; Fidelia Ngozi Efozia

doi:doi:10.11648/j.acis.20150304.12

Evolutionary Model for Virus Propagation on Networks

Arnold Adimabua Ojugo, Fidelis Obukowho Aghware, Rume Elizabeth Yoro, Mary Oluwatoyin Yerokun, Andrew Okonji Eboka, Christiana Nneamaka Anujeonye, Fidelia Ngozi Efozia

Published in Automation, Control and Intelligent Systems (Volume 3, Issue 4)

Received: 11 July 2015 Accepted: 21 July 2015 Published: 31 July 2015

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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).

Published in	Automation, Control and Intelligent Systems (Volume 3, Issue 4)
DOI	10.11648/j.acis.20150304.12
Page(s)	56-62
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Stochastic, Immunize, Network, Vertices, SIS, SIR, Search Space, Solution, Models

References

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[6]	Cohen, R., Havlin, S and Ben-Avraham, D., (2003). Efficient immunization strategies for computer networks and populations. Phys Rev Letters, p232.
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[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.
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APA Style

Arnold Adimabua Ojugo, Fidelis Obukowho Aghware, Rume Elizabeth Yoro, Mary Oluwatoyin Yerokun, Andrew Okonji Eboka, et al. (2015). Evolutionary Model for Virus Propagation on Networks. Automation, Control and Intelligent Systems, 3(4), 56-62. https://doi.org/10.11648/j.acis.20150304.12

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ACS Style

Arnold Adimabua Ojugo; Fidelis Obukowho Aghware; Rume Elizabeth Yoro; Mary Oluwatoyin Yerokun; Andrew Okonji Eboka, et al. Evolutionary Model for Virus Propagation on Networks. Autom. Control Intell. Syst. 2015, 3(4), 56-62. doi: 10.11648/j.acis.20150304.12

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AMA Style

Arnold Adimabua Ojugo, Fidelis Obukowho Aghware, Rume Elizabeth Yoro, Mary Oluwatoyin Yerokun, Andrew Okonji Eboka, et al. Evolutionary Model for Virus Propagation on Networks. Autom Control Intell Syst. 2015;3(4):56-62. doi: 10.11648/j.acis.20150304.12

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@article{10.11648/j.acis.20150304.12,
  author = {Arnold Adimabua Ojugo and Fidelis Obukowho Aghware and Rume Elizabeth Yoro and Mary Oluwatoyin Yerokun and Andrew Okonji Eboka and Christiana Nneamaka Anujeonye and Fidelia Ngozi Efozia},
  title = {Evolutionary Model for Virus Propagation on Networks},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {4},
  pages = {56-62},
  doi = {10.11648/j.acis.20150304.12},
  url = {https://doi.org/10.11648/j.acis.20150304.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150304.12},
  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).},
 year = {2015}
}

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TY  - JOUR
T1  - Evolutionary Model for Virus Propagation on Networks
AU  - Arnold Adimabua Ojugo
AU  - Fidelis Obukowho Aghware
AU  - Rume Elizabeth Yoro
AU  - Mary Oluwatoyin Yerokun
AU  - Andrew Okonji Eboka
AU  - Christiana Nneamaka Anujeonye
AU  - Fidelia Ngozi Efozia
Y1  - 2015/07/31
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150304.12
DO  - 10.11648/j.acis.20150304.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 56
EP  - 62
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150304.12
AB  - 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).
VL  - 3
IS  - 4
ER  -

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Author Information

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

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APA Style

Arnold Adimabua Ojugo, Fidelis Obukowho Aghware, Rume Elizabeth Yoro, Mary Oluwatoyin Yerokun, Andrew Okonji Eboka, et al. (2015). Evolutionary Model for Virus Propagation on Networks. Automation, Control and Intelligent Systems, 3(4), 56-62. https://doi.org/10.11648/j.acis.20150304.12

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ACS Style

Arnold Adimabua Ojugo; Fidelis Obukowho Aghware; Rume Elizabeth Yoro; Mary Oluwatoyin Yerokun; Andrew Okonji Eboka, et al. Evolutionary Model for Virus Propagation on Networks. Autom. Control Intell. Syst. 2015, 3(4), 56-62. doi: 10.11648/j.acis.20150304.12

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AMA Style

Arnold Adimabua Ojugo, Fidelis Obukowho Aghware, Rume Elizabeth Yoro, Mary Oluwatoyin Yerokun, Andrew Okonji Eboka, et al. Evolutionary Model for Virus Propagation on Networks. Autom Control Intell Syst. 2015;3(4):56-62. doi: 10.11648/j.acis.20150304.12

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@article{10.11648/j.acis.20150304.12,
  author = {Arnold Adimabua Ojugo and Fidelis Obukowho Aghware and Rume Elizabeth Yoro and Mary Oluwatoyin Yerokun and Andrew Okonji Eboka and Christiana Nneamaka Anujeonye and Fidelia Ngozi Efozia},
  title = {Evolutionary Model for Virus Propagation on Networks},
  journal = {Automation, Control and Intelligent Systems},
  volume = {3},
  number = {4},
  pages = {56-62},
  doi = {10.11648/j.acis.20150304.12},
  url = {https://doi.org/10.11648/j.acis.20150304.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150304.12},
  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).},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Evolutionary Model for Virus Propagation on Networks
AU  - Arnold Adimabua Ojugo
AU  - Fidelis Obukowho Aghware
AU  - Rume Elizabeth Yoro
AU  - Mary Oluwatoyin Yerokun
AU  - Andrew Okonji Eboka
AU  - Christiana Nneamaka Anujeonye
AU  - Fidelia Ngozi Efozia
Y1  - 2015/07/31
PY  - 2015
N1  - https://doi.org/10.11648/j.acis.20150304.12
DO  - 10.11648/j.acis.20150304.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 56
EP  - 62
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20150304.12
AB  - 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).
VL  - 3
IS  - 4
ER  -

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