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14th IEEE Computer Security Foundations Workshop (CSFW'01)
Markov Chains, Classifiers, and Intrusion Detection
Cape Breton, Novia Scotia, Canada
June 11-June 13
ISBN: 0-7695-1146-5
ASCII Text
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S. Jha, K. Tan, R.A. Maxion, "Markov Chains, Classifiers, and Intrusion Detection," Computer Security Foundations Workshop, IEEE, pp. 0206, 14th IEEE Computer Security Foundations Workshop (CSFW'01), 2001.
 
 
BibTex
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@article{ 10.1109/CSFW.2001.930147,
author = {S. Jha and K. Tan and R.A. Maxion},
title = {Markov Chains, Classifiers, and Intrusion Detection},
journal ={Computer Security Foundations Workshop, IEEE},
volume = {0},
year = {2001},
isbn = {0-7695-1146-5},
pages = {0206},
doi = {http://doi.ieeecomputersociety.org/10.1109/CSFW.2001.930147},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Security Foundations Workshop, IEEE
TI - Markov Chains, Classifiers, and Intrusion Detection
SN - 0-7695-1146-5
SP
EP
A1 - S. Jha,
A1 - K. Tan,
A1 - R.A. Maxion,
PY - 2001
VL - 0
JA - Computer Security Foundations Workshop, IEEE
ER -
 
S. Jha, University of Wisconsin
K. Tan, Carnegie Mellon University
R.A. Maxion, Carnegie Mellon University
Abstract: This paper presents a statistical anomaly detection algorithm based on Markov chains. Our algorithm can be directly applied for intrusion detection by discovering anomalous activities. Our framework for constructing anomaly detectors is very general and can be used by other researchers for constructing Markov-chain-based anomaly detectors. We also present performance metrics for evaluating the effectiveness of anomaly detectors. Extensive experimental results clearly demonstrate the effectiveness of our algorithm. We discuss several future directions for research based on the framework presented in this paper.
Citation:
S. Jha, K. Tan, R.A. Maxion, "Markov Chains, Classifiers, and Intrusion Detection," csfw, pp.0206, 14th IEEE Computer Security Foundations Workshop (CSFW'01), 2001
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