Name
Abstract | ||
|---|---|---|
Scanning worms grow with different local velocities in different areas, because of non-uniformities that are present in real networks. The present work introduces a new stochastic model elaborating the classical epidemiological model for random scanning strategies. More specifically, random effects in worm spreading velocity are modeled by means of a stochastic differential equation where an explicit expression quantifying randomness is proposed. Furthermore, we explore whether deterministic or stochastic models are appropriate in order to describe the worm propagation phenomenon. To this end we introduce the scale of observation as a crucial parameter. Simulation results are presented validating the proposed analytical results. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/PCI.2009.20 | Panhellenic Conference on Informatics |
Keywords | Field | DocType |
i. introduction,worm propagation phenomenon,modeling random scanning worms,classical epidemiological model,different local velocity,random effect,present work,novel stochastic approach,proposed analytical result,stochastic model,stochastic differential equation,different area,new stochastic model,computer worms,stochastic models,random effects,stochastic processes,bandwidth,random processes,internet,differential equations,computational modeling,mathematical model | Data mining,Random effects model,Mathematical optimization,Stochastic optimization,Computer science,Stochastic neural network,Stochastic process,Algorithm,Computer worm,Stochastic differential equation,Stochastic modelling,Randomness | Conference |
ISBN | Citations | PageRank |
978-0-7695-3788-7 | 1 | 0.35 |
References | Authors | |
19 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markos Avlonitis | 1 | 37 | 10.98 |
| Emmanouil Magkos | 2 | 217 | 24.01 |
| Michalis Stefanidakis | 3 | 36 | 7.17 |
| Vassilis Chrissikopoulos | 4 | 38 | 2.86 |