Treating scalability and modelling human countermeasures against local preference worms via gradient models |
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Authors: | Markos Avlonitis Emmanouil Magkos Michalis Stefanidakis Vassilis Chrissikopoulos |
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Affiliation: | (1) Department of Informatics, Ionian University, Platia Tsirigoti 7, 49100 Corfu, Greece |
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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. |
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