=Paper=
{{Paper
|id=Vol-1755/237-238
|storemode=property
|title=A Remedial Pre-Quarantine Perspective to Worm Propagation Defense Modeling for Wireless Sensor Networks Using a Combination of Differential Equation and Agent-Based Approaches
|pdfUrl=https://ceur-ws.org/Vol-1755/237-238.pdf
|volume=Vol-1755
|authors=Chukwunonso Nwokoye,Virginia Ejiofor
|dblpUrl=https://dblp.org/rec/conf/cori/NwokoyeE16
}}
==A Remedial Pre-Quarantine Perspective to Worm Propagation Defense Modeling for Wireless Sensor Networks Using a Combination of Differential Equation and Agent-Based Approaches==
https://ceur-ws.org/Vol-1755/237-238.pdf
A Remedial Pre-Quarantine Perspective to Worm
Propagation Defense Modeling for Wireless Sensor
Networks Using a Combination of Differential Equation
and Agent-Based Approaches
Chukwunonso H. Nwokoye Virginia E. Ejiofor
Nnamdi Azikiwe University, Awka. Nnamdi Azikiwe University, Awka.
explode2kg@yahoo.com virguche2004@yahoo.com
ABSTRACT nodes might carry a worm. So are these models; SEIR [4] and
Investigations have shown that recent models that characterize SEIRS-V [3] etc.
spread of malicious codes have failed to account for certain
characteristics of a real network which can be exploited to aid
faster containment of worms. Specifically, we identified the
absence of uniform random distribution (i.e. sensor deployment)
and disease status check for incoming nodes into the sensor field
(i.e. access control). Advancing these models (using the
epidemic theory) to include these features for Wireless Sensor
Networks (WSNs) underpins our research. We would use the
differential equation and agent-based modeling paradigms to
represent time-related and spatial dynamics of worm
propagation.
CCS Concepts
• Hardware ➝ Communication hardware, interfaces and
storage ➝Wireless integrated network sensors
Keywords
Wireless sensors, Agent-based modeling, differential equation
1. PROBLEM STATEMENT 2. RELEVANCY
The extensive use of WSN and its deployment in harsh Our analyses on uniform random distribution (URD) would
unreachable terrains make them easy prey for worm attack. inform organizations using WSN on the best way to deploy
Recent models that didn’t account for sensor deployment and sensors in order to inhibit faster worm propagation. It would
control which would constitute our research are SEIRS-V[3]; also elicit information on the particular deployment area that
SEIQR[2] and SEIQRS-V[5]. There is no information on the encourages the spread of worms thereby impacting sensor
effects of distribution density and communication range (r) and deployment decisions.
sensor deployment area types on Exposed, Quarantined and
Vaccinated nodes. Figure 1 shows the range between sensor Since network access control (NAC) hasn’t been settled for
nodes. WSN, we embark on our study in order to add to what is already
in existence using the epidemic theory. It is our hope that adding
Although [9] built a maintenance mechanism that performs NAC (through our pre-quarantine mechanism) we can harden
“infection check”; their work modeled a closed population with the sensor network, prevent worm attacks, and eliminate
unauthorized access by illegitimate nodes.
no node inclusion or node loss (due to infection/hardware
failure). The model also ignored the possibility that immigrant 3. BACKGROUND AND RELATED WORK
The journey of developing analytical models for disease
propagation started with SIR [1]. Since then other models has
been developed to address issues. These models include SIS,
CoRI’16, Sept 7–9, 2016, Ibadan, Nigeria. SEIR, SEIRS-V, SEIQR, SEIQRS-V etc. Here, technological
networks are treated like a dynamical system. Its stages include;
model formulation; finding its equilibrium points, deriving the
Reproduction number, showing proof of stability; performing
simulation experiments.
237
multi-group model. Pursuit of other mathematical objectives
4. RESEARCH METHODOLOGY such as performing global stability analyses can ensue.
We would apply the differential equation and agent-based Providing survey reports for usage of epidemic models in P2P
modeling approaches. The equation approach would
networks would constitute our future work.
characterize the temporal parameters while the agent oriented
programming would represent spatial parameters existent in a
real world sensor network. Our key innovation is the 9. REFERENCES
introduction of a pre-quarantine mechanism to check disease
status for incoming nodes and to provide remedial measures 1. Kermack, W. O. and McKendrick, A. G. 1927. A
(NAC). Contribution to the Mathematical Theory of Epidemics.
Proc. R. Soc. A Math. Phys. Eng. Sci. 115, 772, 700–721.
5. PRELIMINARY RESULTS 2. Mishra, B. K. and Jha, N. 2010. SEIQRS model for the
Firstly, we produced a survey report on the usage (and transmission of malicious objects in computer network.
weaknesses) of known epidemic models of computer and Appl. Math. Model. 34, 3, 710–715.
wireless networks [6]. Secondly, we highlighted the impact of 3. Mishra, B. K. and Keshri, N. 2013. Mathematical model on
URD for a circular strip sensor field [7]. To improve recovery the transmission of worms in wireless sensor network.
rate of infectious nodes, we applied the pre-quarantine Appl. Math. Model. 37, 6, 4103–4111.
mechanisms in SEIR and SEIRS-V model modifying them to 4. Mishra, B. K. and Pandey, S. K. 2011. Dynamic model of
QSEIR and QSEIR-V [8]. worms with vertical transmission in computer network.
Appl. Math. Comput. 217, 21, 8438–8446.
6. EVALUATION PLAN 5. Mishra, B. K. and Tyagi, I. 2014. Defending against
We would compare the simulation experiments of both Malicious Threats in Wireless Sensor Network: A
modeling approaches. Thereafter, we would compare the results Mathematical Model. IJIT. Comput. Sci. 6, 3, 12–19.
of our modified models with results of the original models. 6. Nwokoye, C. H, Ejiofor, V. E., Ozoegwu, C. G. 2016. A
Using the SEIRS-V model we would also perform comparative survey of classical SI-based analytical epidemic models for
analysis with expressions for sensor URD i.e. ( for a malicious objects’ spread in prevailing network
circular area [9] and ⁄ ) for a square area. environments. ACM Comput. Surv. Under review (2016).
7. Nwokoye, C. H., Orji, R. and Mbeledogu, N, Umeh, I.
7. EXPECTED CONTRIBUTION 2016. Investigating the Effect of Uniform Random
Our work would enhance better understanding of the factors that Distribution of Nodes in Wireless Sensor Networks using
aid worm propagation. It would present a formalized an Epidemic Worm Model. CoRI 2016. Accepted.
mathematical treatment for NAC in WSN literature. It would 8. Nwokoye, C. H, Ozoegwu, C. G, Ejiofor, V. E. 2016. Pre-
derive more accurate Reproduction numbers for worm Quarantine Approach for Defense against Propagation of
extinction in models mentioned above. And show how/why our Malicious Objects in Networks. FESE. Under Review.
models exhibit non-vanishing recovery at the Disease Free 9. Tang, S. and Mark, B. L. 2009. Analysis of virus spread in
Equilibrium contrary to several works in literature. The study wireless sensor networks: An epidemic model. DRCN 2009
would provide theoretical foundation for controlling/forecasting (2009), 86–91.
of worms in the presence of NAC.
8. REFLECTIONS
Research can arise by finding and applying expressions for other
categories of sensor deployment aside the “Fixed, no control”
type described with URD. URD can be applied to a
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