Abstract

Network Traffic Monitoring and Analysis (NTMA) applications strongly rely on the guidance and knowledge provided by a human network operator, limiting their ability for self-management. Critical NTMA applications such as the detection of network attacks, service anomalies and in general on-line monitoring tasks require fast mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. The high-dimensionality of network data provided by current network monitoring systems opens the door to the massive application of machine learning approaches to improve the analysis of network measurements, but these higher dimensionality and data volume come with an extra data processing overhead. In this talk I will present and discuss multiple different approaches for enhanced network traffic monitoring and analysis based on the systematic application of different machine learning paradigms and techniques, from supervised to unsupervised models, from stream to batch processing approaches, from shallow to deep learning architectures, and from simple to ensemble learning techniques. I will focus on the development of machine-learning-based approaches for cyber security and 0-day attacks detection, capable of functioning with a limited guidance or previous knowledge. I will present and discuss Big-DAMA, a flexible big data analytics platform for network monitoring and analysis, capable to analyze and store big amounts of both structured and unstructured heterogeneous data sources, with both stream and batch processing capabilities. Taking a broader look into the application of AI for Networking (Ai4NETS), I will conclude my talk by elaborating on some of the major showstoppers hindering a natural application of machine learning in the networking applied field.

Bio

Dr. Pedro Casas (http://pcasas.info/) is Scientist in ICT Security and Information Management at the AIT Austrian Institute of Technology in Vienna. He received an Electrical Engineering degree from Universidad de la República, Uruguay in 2005, and a Ph.D. degree in Computer Science from Institut Mines-Télécom, Télécom Bretagne, France in 2010. He was Postdoctoral Research Fellow at the LAAS-CNRS lab in Toulouse between 2010 and 2011, and Senior Researcher at the Telecommunications Research Center Vienna (FTW) between 2011 and 2015. He works as project manager and technical work leader in different networking-related initiatives, including research projects and commercial solutions. His work focuses on machine-learning and data mining based approaches for Networking, big data analytics and platforms, Internet network measurements, network security and anomaly detection, as well as QoE modeling, assessment and monitoring. He has published more than 135 Networking research papers in major international conferences and journals, received 12 awards for his work - including 7 best paper awards, and he is general chair for different conferences, workshops and leading actions in network measurement and analysis, including the IEEE ComSoc ITC Special Interest Group on Network Measurements and Analytics. He is leading the Big-DAMA project on Big Data analytics for network traffic monitoring and analysis.