1 A Network Traffic Classification based on Coupled Hidden Markov Models Fei Zhang, Wenjun Wu zhangfei,[email protected] National Lab of Software Development Environment Beihang University, Beijing, China
Dec 25, 2015
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A Network Traffic Classification based on Coupled Hidden Markov Models
Fei Zhang, Wenjun Wuzhangfei,[email protected] Lab of Software Development EnvironmentBeihang University, Beijing, China
Two HMM chains
Take as example
• S : discrete hidden state set• π : represents the initial rate of state• A : transition matrix • B : continuous conditional distribution(GMM), which means
the observed variable’s conditional probability under state
Maintain the Assessing Formula
We propose a statistic model using (IPT, PS) sequences set as input and calculate the assessing value using joint Viterbi path and transition matrix. In order to avoid the problem that assessing value is too small, we compute sum of logs instead of doing multiplication.
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summarized through a confusion matrix, the results of the classification performed on the test sets. Each row represents the classification correctness (in percentage) over a different application test set
Results show that our PLCHMMs based traffic classifier can achieve more than 90% accuracy, in classifying almost every test dataset, which outperforms other HMM based traffic classifiers using different probability distribution.