(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
- ISSN
- 10897798
- Τύπος
- Journal Article
- Έτος
- 2010
Σύνδεσμοι
BibTeX
@article{Karyotis2010875,
title = {Markov Random Fields for malware propagation: The case of chain networks},
author = {V. Karyotis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957705451&doi=10.1109%2fLCOMM.2010.072910.100866&partnerID=40&md5=0aa0a66743c86b11f726b5b39925249c},
doi = {10.1109/LCOMM.2010.072910.100866},
issn = {10897798},
year = {2010},
date = {2010-01-01},
journal = {IEEE Communications Letters},
volume = {14},
number = {9},
pages = {875-877},
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.},
note = {cited By 13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}