Hidden Markov Models for Automated Protocol Learning

Sean Whalen; Matt Bishop; James Crutchfield

Security and Privacy in Communication Networks. 6th Iternational ICST Conference, SecureComm 2010, Singapore, September 7-9, 2010. Proceedings

Research Article

Hidden Markov Models for Automated Protocol Learning

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@INPROCEEDINGS{10.1007/978-3-642-16161-2_24,
    author={Sean Whalen and Matt Bishop and James Crutchfield},
    title={Hidden Markov Models for Automated Protocol Learning},
    proceedings={Security and Privacy in Communication Networks. 6th Iternational ICST Conference, SecureComm 2010, Singapore, September 7-9, 2010. Proceedings},
    proceedings_a={SECURECOMM},
    year={2012},
    month={5},
    keywords={Statistical Inference Reverse Engineering Network Protocols Markov Models Computational Mechanics},
    doi={10.1007/978-3-642-16161-2_24}
}

Sean Whalen
Matt Bishop
James Crutchfield
Year: 2012
Hidden Markov Models for Automated Protocol Learning
SECURECOMM
Springer
DOI: 10.1007/978-3-642-16161-2_24

Sean Whalen^,*, Matt Bishop¹^,*, James Crutchfield^,*

1: University of California

*Contact email: whalen@cs.ucdavis.edu, bishop@cs.ucdavis.edu, chaos@cse.ucdavis.edu

Abstract

Hidden Markov Models (HMMs) have applications in several areas of computer security. One drawback of HMMs is the selection of appropriate model parameters, which is often ad hoc or requires domain-specific knowledge. While algorithms exist to find local optima for some parameters, the number of states must always be specified and directly impacts the accuracy and generality of the model. In addition, domain knowledge is not always available or may be based on assumptions that prove incorrect or sub-optimal.

Keywords: Statistical Inference Reverse Engineering Network Protocols Markov Models Computational Mechanics

Published: 2012-05-28

: http://dx.doi.org/10.1007/978-3-642-16161-2_24