(2010). Markov random fields for malware propagation: The case of chain networks. IEEE communications letters, 14(9), 875–877. https://doi.org/10.1109/LCOMM.2010.072910.100866
Περίληψη
Epidemic and stochastic models have been employed for describing the dynamic behavior of malware outbreaks. However, most of them lack a holistic treatment of the problem. In this work, we model malware propagation as a Markov Random Field and employ Gibbs sampling for the analysis of the system. We demonstrate the proposed framework for the case of a chain network, a model often emerging in both wired and wireless multi-hop networks. © 2010 IEEE.
- DOI
- 10.1109/LCOMM.2010.072910.100866
- Τύπος
- Άρθρο σε Περιοδικό
- Έτος
- 2010
Σύνδεσμοι
BibTeX
@article{karyotis2010markov,
title = {Markov random fields for malware propagation: The case of chain networks},
author = {Vasileios Karyotis},
url = {https://doi.org/10.1109/LCOMM.2010.072910.100866},
doi = {10.1109/LCOMM.2010.072910.100866},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
journal = {IEEE communications letters},
volume = {14},
number = {9},
pages = {875--877},
publisher = {IEEE},
abstract = {Epidemic and stochastic models have been employed for describing the dynamic behavior of malware outbreaks. However, most of them lack a holistic treatment of the problem. In this work, we model malware propagation as a Markov Random Field and employ Gibbs sampling for the analysis of the system. We demonstrate the proposed framework for the case of a chain network, a model often emerging in both wired and wireless multi-hop networks. © 2010 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}