Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TNSM.2014.011714.130505 | IEEE Transactions on Network and Service Management |
Keywords | Field | DocType |
Internet,computer network management,feature extraction,iterative methods,pattern classification,pattern clustering,protocols,statistical analysis,telecommunication traffic,unsupervised learning,DPI,ISP,Internet traffic,SeLeCT,a-priori knowledge,adaptive seeding approach,class labels,deep packet inspection,homogeneous clusters,iterative seeding approach,label assignment,network management,network visibility,precision and recall,protocols,security,self-learning classifier,signature extraction,statistical classification,statistical features,traffic classification,traffic engineering,traffic traces,training set,unsupervised algorithms,Traffic classification,clustering,self-seeding,unsupervised machine learning | Traffic classification,Data mining,Deep packet inspection,Computer science,Computer network,Artificial intelligence,Network management,Classifier (linguistics),Internet traffic,Precision and recall,Statistical classification,Traffic engineering,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 2 | 1932-4537 |
Citations | PageRank | References |
10 | 0.56 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luigi Grimaudo | 1 | 68 | 7.86 |
| Marco Mellia | 2 | 2748 | 204.65 |
| Elena Baralis | 3 | 1319 | 186.33 |
| Keralapura, R. | 4 | 10 | 0.56 |