Noise-resistant statistical traffic classification

Network traffic classification plays a significant role
in cyber security applications and management scenarios. Conventional
statistical classification techniques rely on the assumption
that clean labelled samples are available for building classifi-
cation models. However, in the big data era, mislabelled training
data commonly exist due to the introduction of new applications
and lack of knowledge. Existing statistical traffic classification
techniques do not address the problem of mislabelled training
data, so their performance become poor in the presence of
mislabelled training data. To meet this challenge, in this paper,
we propose a new scheme, Noise-resistant Statistical Traffic
Classification (NSTC), which incorporates the techniques of noise
elimination and reliability estimation into traffic classification.
NSTC estimates the reliability of the remaining training data
before it builds a robust traffic classifier. Through a number of
traffic classification experiments on two real-world traffic data
sets, the results show that the new NSTC scheme can effectively
address the problem of mislabelled training data. Compared with
the state of the art methods, NSTC can significantly improve the
classification performance in the context of big unclean data.