@inproceedings {Gri2013, title = {Self-Learning Classifier for Internet Traffic}, booktitle = {The 5th IEEE International Traffic Monitoring and Analysis Workshop (TMA 2013)}, year = {2013}, abstract = {

Network visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative\ to automatically identify classes of traffic. However, the accuracy\ achieved so far does not allow to use them for traffic classification\ in practical scenario.
In this paper, we propose SeLeCT, a Self-Learning Classifier\ for Internet traffic. It uses unsupervised algorithms along with\ an adaptive learning approach to automatically let classes of\ traffic emerge, being identified and (easily) labeled. SeLeCT\ automatically groups flows into pure (or homogeneous) clusters\ using alternating simple clustering and filtering phases to remove\ outliers. SeLeCT uses an adaptive learning approach to boost its\ ability to spot new protocols and applications. Finally, SeLeCT\ also simplifies label assignment (which is still based on some\ manual intervention) so that proper class labels can be easily\ discovered.
We evaluate the performance of SeLeCT using traffic traces\ collected in different years from various ISPs located in 3\ different continents. Our experiments show that SeLeCT achieves\ overall accuracy close to 98\%. Unlike state-of-art classifiers, the\ biggest advantage of SeLeCT is its ability to help discovering\ new protocols and applications in an almost automated fashion.

}, doi = {10.1109/INFCOMW.2013.6562900}, author = {Luigi Grimaudo and Marco Mellia and Elena Baralis and Ram Keralapura} }