A protocol anomaly detection method based on optimized hidden Markov model

DOI

10.2991/icmmcce-15.2015.314How to use a DOI?

Keywords

Intrusion detection; protocol anomaly; genetic algorithm; hidden Markov model; parameter optimization

Abstract

As to solve the issues of insufficient training data and initial parameters sensitive in existing protocol anomaly detection based on hidden Markov model, presenting a new protocol anomaly detection method based on improved genetic algorithm and hidden Markov model. First, the local competitive selection strategy, arithmetic crossover and adaptive non-uniform mutation operator were used to improve the genetic algorithm, in order to avoid the "premature" and "stagnation" problem in traditional genetic algorithm; then, the improved genetic algorithm was recommended to optimize the initial parameters of hidden Markov model to avoid the initial model parameters sensitive issue; and finally, the keyword and keyword interval were taken as training observations, describe the behavior of protocol details to expand the training sample space. Experimental results on DARPA 1999 data set show that the method has a high detection rate and low false alarm rate.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wei Qiu
AU  - Yingjie Yang
AU  - Yongwei Wang
AU  - Dexian Chang
AU  - Jiang Liu
AU  - Hao Hu
PY  - 2015/12
DA  - 2015/12
TI  - A protocol anomaly detection method based on optimized hidden Markov model
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.314
DO  - 10.2991/icmmcce-15.2015.314
ID  - Qiu2015/12
ER  -