Name
Title | ||
|---|---|---|
Treating scalability and modelling human countermeasures against local preference worms via gradient models |
Abstract | ||
|---|---|---|
A network worm is a specific type of malicious software that self propagates by exploiting application vulnerabilities in
network-connected systems. Worm propagation models are mathematical models that attempt to capture the propagation dynamics
of scanning worms as a means to understand their behaviour. It turns out that the emerged scalability in worm propagation
plays an important role in order to describe the propagation in a realistic way. On the other hand human-based countermeasures
also drastically affect the propagation in time and space. This work elaborates on a recent propagation model (Avlonitis et
al. in J Comput Virol 3, 87–92, 2007) that makes use of Partial Differential Equations in order to treat correctly scalability
and non-uniform behaviour (e.g., local preference worms). The aforementioned gradient model is extended in order to take into
account human-based countermeasures that influence the propagation of local-preference worms in the Internet. Certain aspects
of scalability emerged in random and local preference strategies are also discussed by means of random field considerations.
As a result the size of a critical network that needs to be studied in order to describe the global propagation of a scanning
worm is estimated. Finally, we present simulation results that validate the proposed analytical results and demonstrate the
higher propagation rate of local preference worms compared with random scanning worms. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11416-008-0099-8 | Journal in Computer Virology |
Keywords | Field | DocType |
partial differential equation,mathematical model,random field | Random field,Computer science,Theoretical computer science,Electronic countermeasure,Malware,Mathematical model,Partial differential equation,Intrusion detection system,The Internet,Scalability | Journal |
Volume | Issue | ISSN |
5 | 4 | 2263-8733 |
Citations | PageRank | References |
4 | 0.42 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markos Avlonitis | 1 | 37 | 10.98 |
| Emmanouil Magkos | 2 | 217 | 24.01 |
| Michalis Stefanidakis | 3 | 36 | 7.17 |
| vassilios chrissikopoulos | 4 | 160 | 16.11 |