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. 2016 Jun 22:6:28289.
doi: 10.1038/srep28289.

Impact of Information based Classification on Network Epidemics

Bimal Kumar Mishra  1 , Kaushik Haldar  1 , Durgesh Nandini Sinha  2
Affiliations

Impact of Information based Classification on Network Epidemics

Bimal Kumar Mishra et al. Sci Rep. .

Abstract

Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. DifEpGos Architecture.
Figure 2
Figure 2. The SEIR (Susceptible-Exposed-Infectious-Recovered) framework.
Figure 3
Figure 3. Schematic Representation of the D-SEIR Model.
Figure 4
Figure 4. Local stability of infection free equilibrium.
Figure 5
Figure 5. Global stability of infection free equilibrium.
Figure 6
Figure 6. Global stability of endemic equilibrium point when R0 > 1 depicted in S − I1 phase plane.
Figure 7
Figure 7. Global stability of endemic equilibrium point when R0 > 1 depicted in S − E1 phase plane.
Figure 8
Figure 8. Performance of the D-SEIR model on the Oregon datasets.
Figure 9
Figure 9. Characterization of Attack Categories.
Figure 10
Figure 10. Characterization of Defense Categories
Figure 11
Figure 11
(a) Dynamical behavior of system for a strong attack in class 1 (b). Impact of Model on Dynamical Behavior of System for a Strong Attack in Class 1
See this image and copyright information in PMC

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