<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Ram Keralapura</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SeLeCT: Self-Learning Classifier for Internet Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Network and Service Management</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">self-seeding</style></keyword><keyword><style  face="normal" font="default" size="100%">Traffic Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">unsupervised machine learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Network visibility is a critical part of traffic engineering, network management, and security. The most popular&amp;nbsp;current solutions - Deep Packet Inspection (DPI) and statistical&amp;nbsp;classification, deeply rely on the availability of a training set.&amp;nbsp;Besides the cumbersome need to regularly update the signatures,&amp;nbsp;their visibility is limited to classes the classifier has been trained&amp;nbsp;for. Unsupervised algorithms have been envisioned as a viable&amp;nbsp;alternative to automatically identify classes of traffic. However,&amp;nbsp;the accuracy achieved so far does not allow to use them for traffic&amp;nbsp;classification in practical scenario.&lt;/p&gt;&lt;p&gt;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&amp;nbsp;let classes of traffic emerge, being identified and labeled. Unlike&amp;nbsp;traditional classifiers, it requires neither a-priori knowledge of&amp;nbsp;signatures nor a training set to extract the signatures. Instead,&amp;nbsp;SeLeCT automatically groups flows into pure (or homogeneous)&amp;nbsp;clusters using simple statistical features. SeLeCT simplifies label&amp;nbsp;assignment (which is still based on some manual intervention) so&amp;nbsp;that proper class labels can be easily discovered. Furthermore,&amp;nbsp;SeLeCT uses an iterative seeding approach to boost its ability to&amp;nbsp;cope with new protocols and applications.&lt;/p&gt;&lt;p&gt;We evaluate the performance of SeLeCT using traffic traces&amp;nbsp;collected in different years from various ISPs located in 3&amp;nbsp;different continents. Our experiments show that SeLeCT achieves&amp;nbsp;excellent precision and recall, with overall accuracy close to 98%.&amp;nbsp;Unlike state-of-art classifiers, the biggest advantage of SeLeCT&amp;nbsp;is its ability to discover new protocols and applications in an&amp;nbsp;almost automated fashion.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">144</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniele Apiletti</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Tania Cerquitelli</style></author><author><style face="normal" font="default" size="100%">Silvia Chiusano</style></author><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SEARUM: a cloud-based SErvice for Association RUle Mining</style></title><secondary-title><style face="normal" font="default" size="100%">The 11th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA-13)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">association rule mining</style></keyword><keyword><style  face="normal" font="default" size="100%">cloud-based service</style></keyword><keyword><style  face="normal" font="default" size="100%">distributed computing model</style></keyword><keyword><style  face="normal" font="default" size="100%">network data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SEARUM, to efficiently mine association rules on a distributed computing model. SEARUM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of&amp;nbsp;SEARUM in mining association rules on a distributed computing model.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author><author><style face="normal" font="default" size="100%">Marco Mellia</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Ram Keralapura</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Learning Classifier for Internet Traffic</style></title><secondary-title><style face="normal" font="default" size="100%">The 5th IEEE International Traffic Monitoring and Analysis Workshop (TMA 2013)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Network visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative&amp;nbsp;to automatically identify classes of traffic. However, the accuracy&amp;nbsp;achieved so far does not allow to use them for traffic classification&amp;nbsp;in practical scenario.&lt;br /&gt;In this paper, we propose SeLeCT, a Self-Learning Classifier&amp;nbsp;for Internet traffic. It uses unsupervised algorithms along with&amp;nbsp;an adaptive learning approach to automatically let classes of&amp;nbsp;traffic emerge, being identified and (easily) labeled. SeLeCT&amp;nbsp;automatically groups flows into pure (or homogeneous) clusters&amp;nbsp;using alternating simple clustering and filtering phases to remove&amp;nbsp;outliers. SeLeCT uses an adaptive learning approach to boost its&amp;nbsp;ability to spot new protocols and applications. Finally, SeLeCT&amp;nbsp;also simplifies label assignment (which is still based on some&amp;nbsp;manual intervention) so that proper class labels can be easily&amp;nbsp;discovered.&lt;br /&gt;We evaluate the performance of SeLeCT using traffic traces&amp;nbsp;collected in different years from various ISPs located in 3&amp;nbsp;different continents. Our experiments show that SeLeCT achieves&amp;nbsp;overall accuracy close to 98%. Unlike state-of-art classifiers, the&amp;nbsp;biggest advantage of SeLeCT is its ability to help discovering&amp;nbsp;new protocols and applications in an almost automated fashion.&lt;/p&gt;</style></abstract></record></records></xml>