Pere Barlet-Ros

Learn More
The traffic classification problem has recently attracted the interest of both network operators and researchers. Several machine learning (ML) methods have been proposed in the literature as a promising solution to this problem. Surprisingly, very few works have studied the traffic classification problem with Sampled NetFlow data. However, Sampled NetFlow(More)
Monitoring and mining real-time network data streams are crucial operations for managing and operating data networks. The information that network operators desire to extract from the network traffic is of different size, granularity and accuracy depending on the measurement task (e.g., relevant data for capacity planning and intrusion detection are very(More)
Detecting network traffic anomalies is crucial for network operators as it helps to identify security incidents and to monitor the availability of networked services. Although anomaly detection has received significant attention in the literature, the automatic classification of network anomalies still remains an open problem. In this paper, we introduce a(More)
During the last years, the Advanced Broadband Communications Center (CCABA) of the UPC has been involved in several projects related to Internet traffic monitoring and analysis in the Spanish National Research and Education Network (RedIRIS), namely CASTBA, MEHARI [1] and MIRA [2, 3]. As a result of such an experience, a new traffic monitoring and analysis(More)
Application identification in network traffic has recently become a hard challenge for network operators. In this paper, we face this problem with Sampled NetFlow data, which is an extended scenario but scarcely investigated. We present an application identification method that, although being slightly less accurate (≈90%) than previous packetbased methods,(More)
It is commonly believed that file sharing traffic on the Internet is mostly generated by peer-to-peer applications. However, we show that HTTP based file sharing services are also extremely popular. We analyzed the traffic of a large research and education network for three months, and observed that a large fraction of the inbound HTTP traffic corresponds(More)
Counting the number of flows present in network traffic is not trivial, given that the naive approach of using a hash table to track the active flows is too slow for the current backbone network speeds. Several algorithms have been proposed in the recent literature that can calculate an approximate count using small amount of memory and few memory accesses(More)
Tracking and maintaining satisfactory QoE for video streaming services is becoming a greater challenge for mobile network operators than ever before. Downloading and watching video content on mobile devices is currently a growing trend among users, that is causing a demand for higher bandwidth and better provisioning throughout the network infrastructure.(More)
The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of(More)
This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user’s defenses. A significant majority of reviewed articles and web resources are from years 2012 – 2014. Privacy seems to be the Achilles’ heel of today’s web. Web services make(More)