@article {grimaudoTNSM2014, title = {SeLeCT: Self-Learning Classifier for Internet Traffic}, journal = {IEEE Transactions on Network and Service Management}, volume = {11}, year = {2014}, month = {06/2014}, chapter = {144}, abstract = {

Network visibility is a critical part of traffic engineering, network management, and security. The most popular\ current solutions - Deep Packet Inspection (DPI) and statistical\ classification, deeply rely on the availability of a training set.\ Besides the cumbersome need to regularly update the signatures,\ their visibility is limited to classes the classifier has been trained\ for. 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.

To address the above issues, we propose SeLeCT, a Self-Learning Classifier for Internet Traffic. It uses unsupervised algorithms along with an adaptive seeding approach to automatically\ let classes of traffic emerge, being identified and labeled. Unlike\ traditional classifiers, it requires neither a-priori knowledge of\ signatures nor a training set to extract the signatures. Instead,\ SeLeCT automatically groups flows into pure (or homogeneous)\ clusters using simple statistical features. SeLeCT simplifies label\ assignment (which is still based on some manual intervention) so\ that proper class labels can be easily discovered. Furthermore,\ SeLeCT uses an iterative seeding approach to boost its ability to\ cope with new protocols and applications.

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\ excellent precision and recall, with overall accuracy close to 98\%.\ Unlike state-of-art classifiers, the biggest advantage of SeLeCT\ is its ability to discover new protocols and applications in an\ almost automated fashion.

}, keywords = {clustering, self-seeding, Traffic Classification, unsupervised machine learning}, author = {Luigi Grimaudo and Marco Mellia and Elena Baralis and Ram Keralapura} } @inproceedings {Fia2013, title = {HTTPTag: A Flexible On-line HTTP Classification System for Operational 3G Networks}, booktitle = {INFOCOM{\textquoteright}2013 Demo/Poster Session (INFOCOM{\textquoteright}2013 - Demo/Poster Session)}, year = {2013}, address = {Turin, Italy}, abstract = {

The popularity of web-based services and applications like YouTube and Facebook has taken HTTP back to the pole position on end-user traffic consumption. We present HTTPTag, a flexible on-line HTTP classification system based on pattern matching and tagging. HTTPTag recognizes on the fly and tracks the evolution of more than 280 applications running on top of HTTP in an operational 3G network, representing more than 70\% of the total HTTP traffic volume consumed by its customers. HTTPTag improves the network traffic visibility of an operator, performing tasks such as top-services ranking, long-term monitoring of applications popularity, and trend analysis among others.

}, keywords = {3G Networks, HTTP, Pattern Matching, Traffic Classification}, author = {Pierdomenico Fiadino and A B{\"a}r and P. Casas} }