Report of the Third Workshop on the Usage of NetFlow/IPFIX in Network Management

The Network Management Research Group (NMRG) organized in 2010 the Third Workshop on the Usage of NetFlow/IPFIX in Network Management, as part of the 78th IETF Meeting in Maastricht. Yearly organized since 2007, the workshop is an opportunity for people from both academia and industry to discuss the latest developments of the protocol, possibilities for new applications, and practical experiences. This report summarizes the presentations and the main conclusions of the workshop

Robust URL Classification With Generative Adversarial Networks

Classifying URLs is essential for different applications, such as parental control, URL filtering and Ads/tracking protection. Such systems historically identify URLs by means of regular expressions, even if machine learning alternatives have been proposed to overcome the time-consuming maintenance of classification rules. Classical machine learning algorithms, however, require large samples of URLs to train the models, covering the diverse classes of URLs (i.e., a ground truth), which somehow limits the applicability of the approach. We here give a first step towards the use of Generative Adversarial Neural Networks (GANs) to classify URLs. GANs are attractive for this problem for two reasons. First, GANs can produce samples of URLs belonging to specific classes even if exposed to a limited training set, outputting both synthetic traces and a robust discriminator. Second, a GAN can be trained to discriminate a class of URLs without being exposed to all other URLs classes – i.e., GANs are robust even if not exposed to uninteresting URL classes during training. Experiments on real data show that not only the generated synthetic traces are somehow realistic, but also the URL classification is accurate with GANs. © is is held held by by author/owner(s). author/owner(s)

Utilización y analisis de herramientas de Big Data para el estudio de registros de conexiones a internet

El objetivo de la tecnología Big Data es el estudio de grandes cantidades de datos cuyo análisis tomaría demasiado tiempo en una base de datos tradicional. En este trabajo se emplean estas tecnologías para realizar el estudio de una red de tráfico Internet. Para ello se utilizan las herramientas de Big Data pertenecientes a Apache Hadoop: MapReduce, Spark y Hive. Estas herramientas se encuentran funcionando sobre un cluster de ordenadores ubicado en la Universidad Politécnica de Turín. En esta red se genera una monitorización del tráfico perteneciente al protocolo internet (IP) mediante un sniffer que crea los registros de tráfico sobre los que se trabaja. El primer problema que se plantea es realizar un estudio de las características de las herramientas pertenecientes a Apache Hadoop en su uso para el análisis de los registros de tráfico de red almacenados. Para ello se realizan una serie de pruebas que permiten comprobar sus resultados frente a diferentes tipos de análisis. Al finalizar el estudio de estas herramientas, se realiza un análisis sobre el tráfico IP almacenado para caracterizar los protocolos utilizados en la red y el tráfico generado. Debido a que la mayoría del tráfico registrado pertenece al protocolo de transferencia de hipertexto (HTTP), se estudia la relación que tiene en los servicios web modernos el dominio visitado y las direcciones IP utilizadas

Report from the 6th PhD School on Traffic Monitoring and Analysis (TMA)

This is a summary report by the organizers of the 6th TMA PhD school held in Louvain-la-Neuve on 5-6 April 2016. The insight and feedback received about the event might turn useful for the organization of future editions and similar events targeting students and young researchers

Impact of Access Line Capacity on Adaptive Video Streaming Quality - A Passive Perspective

Adaptive streaming over HTTP is largely used to deliver live and on-demand video. It works by adjusting video quality according to network conditions. While QoE for different streaming services has been studied, it is still unclear how access line capacity impacts QoE of broadband users in video sessions. We make a first step to answer this question by characterizing parameters influencing QoE, such as frequency of video adaptations. We take a passive point of view, and analyze a dataset summarizing video sessions of a large population for one year. We first split customers based on their estimated access line capacity. Then, we quantify how the latter affects QoE metrics by parsing HTTP requests of Microsoft Smooth Streaming (MSS) services. For selected services, we observe that at least 3~Mbps of downstream capacity is needed to let the player select the best bitrate, while at least 6~Mbps are required to minimize delays to retrieve initial fragments. Surprisingly, customers with faster access lines obtain limited benefits, hinting to restrictions on the design of services