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M. Laner, P. Svoboda, M. Rupp:
"Detecting M2M Traffic in Mobile Cellular Networks";
Talk: International Conference on Signals, Systems and Image Processing, Dubrovnik, Croatia; 05-12-2014 - 05-15-2014; in: "International Conference on Signals, Systems and Image Processing", (2014), ISSN: 2157-8672; 159 - 162.

Service visibility is a major part in traffic engineering
and security. The recent rise of Machine-to-Machine
Communication (M2M) nodes in cellular mobile networks and
their impact on up-link resources draw the attention to automatic
identification of this traffic class. However, the traditional traffic
classification does not deliver accuracy the operators need.
We present a method for detecting M2M traffic with an
accuracy of up to 99% within the IP packet stream of a mobile
operator. Traffic classification is based on features extracted from
the packet level traces. Our main contribution is the extensive
analysis of a large set of features where we showed that M2M
can be classified very well using only nine features per node. In
the supervised case we get a high level of accuracy starting at
2,5% of training data. In the unsupervised case we can cluster
with a very good performance above 95% based on the extracted
features.
In this paper we are showing that it is possible to detecting
M2M inside the traffic stream of a mobile cellular network at
high accuracy, for both supervised and unsupervised machine
learning.

Service visibility is a major part in traffic engineering
and security. The recent rise of Machine-to-Machine
Communication (M2M) nodes in cellular mobile networks and
their impact on up-link resources draw the attention to automatic
identification of this traffic class. However, the traditional traffic
classification does not deliver accuracy the operators need.
We present a method for detecting M2M traffic with an
accuracy of up to 99% within the IP packet stream of a mobile
operator. Traffic classification is based on features extracted from
the packet level traces. Our main contribution is the extensive
analysis of a large set of features where we showed that M2M
can be classified very well using only nine features per node. In
the supervised case we get a high level of accuracy starting at
2,5% of training data. In the unsupervised case we can cluster
with a very good performance above 95% based on the extracted
features.
In this paper we are showing that it is possible to detecting
M2M inside the traffic stream of a mobile cellular network at
high accuracy, for both supervised and unsupervised machine
learning.

M2M, MTC

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6837655

Project Head Philipp Svoboda:
Darwin4