Detecting Network Outages using Different Sources of Data

Cristel Pelsser, professor at the University of Strasbourg, France

The Internet is a complex ecosystem composed of thousands of Autonomous Systems (ASs) operated by independent organizations; each AS having a very limited view outside its own network. This impedes network operators to finely pinpoint the causes of service degradation or disruption when the problem lies outside of their network.
In this talk we will present different methods to detect remote network outages. Each technique is tailored to a different source of data, with its own properties and coverage. We will contrast these and provide light on the evaluation of outage detection approaches in the face of mostly absent ground truth.

Bio: Cristel Pelsser is a professor at the University of Strasbourg since November 2015. She leads team of researchers focusing on core Internet technologies. Her aim is to facilitate network operations, avoid network disruptions and, when they occur, pinpoint the failure precisely in order to quickly fix the issue. Cristel obtained her PhD from the UcL in Belgium and spent 9 years working for ISPs.

Looking for Hypergiants in PeeringDB

Steve Uhlig, Professor at Queen Mary University of London, UK

Hypergiants, such as Google or Netflix, are important organisations in the Internet ecosystem, due to their sheer impact in terms of traffic volume exchanged. However, beyond naming specific instances, the research community still lacks a sufficiently crisp understanding of them. In this paper we analyse PeeringDB data and identify features that differentiate hypergiants from the other organisations. To this end, we first characterise the organisations present in PeeringDB, allowing us to identify discriminating properties of these organisations. We then use these properties to separate the data in two clusters, differentiating hypergiants from other organisations. We conclude this paper by investigating how hypergiants and other organisations exploit the IXP ecosystem to reach the global IPv4 space.

Bio: Prof. Uhlig obtained a Ph.D. degree in Applied Sciences from the University of Louvain, Belgium, in 2004. From 2004 to 2006, he was a Postdoctoral Fellow of the Belgian National Fund for Scientific Research (F.N.R.S.). His thesis won the annual IBM Belgium/F.N.R.S. Computer Science Prize 2005. Between 2004 and 2006, he was a visiting scientist at Intel Research Cambridge, UK, and at the Applied Mathematics Department of University of Adelaide, Australia. Between 2006 and 2008, he was with Delft University of Technology, the Netherlands. Prior to joining Queen Mary, he was a Senior Research Scientist with Technische Universität Berlin/Deutsche Telekom Laboratories, Berlin, Germany. Since January 2012, he is the Professor of Networks and head of the Networks research group at Queen Mary, University of London. He was a guest professor at the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, between 2012 and 2016. Prof. Uhlig was the general chair of PAM 2017, ACM SIGCOMM 2015 and IMC 2017, as well as TPC chair of PAM 2017, IFIP Networking 2019, Global Internet 2019, and ICNP 2019. He is also area editor for IEEE Transactions on Networking as well as Elsevier Computer Communications. He published over 100 papers in international journals and conferences. His current research interests include Software-Defined Networking (SDN), Internet measurements (active and passive), as well as content delivery.

The 5 Ws of Network Monitoring for SDN-based IDPS

Sandra Scott-Hayward, Lecturer (Assistant Professor) at Queen’s University Belfast (QUB), UK

With the introduction of software-defined networks (SDNs) and network functions virtualization (NFV) come opportunities for efficient network threat detection and protection. SDN’s global view and NFV service distribution provide a means of monitoring and defence across the entire network. However, with distributed attacks involving high traffic volumes, network monitoring is a challenging task. In this talk, we will discuss our lessons learned and recommendations for efficient and proportionate network monitoring; the Who, What, When, Where, and Why (5 Ws) of network monitoring for SDN-based intrusion detection and prevention systems.

Bio: Dr. Sandra Scott-Hayward, CEng CISSP CEH, is a Lecturer (Assistant Professor) at Queen’s University Belfast (QUB). She has experience in both research and industry, having worked as a Systems Engineer and Engineering Group Leader with Airbus before returning to complete her Ph.D. at QUB. In the Centre for Secure Information Technologies at QUB, Sandra leads research and development of network security architectures and security functions for software-defined networks (SDN) and network functions virtualization (NFV). She has presented her research globally, has published a series of IEEE papers on performance and security designs for SDN/NFV, and has received a number of awards for her work. Sandra was elected Vice-Chair of the Open Networking Foundation (ONF) Security Working Group and served as vice-chair from 2015 to 2017. She received Outstanding Technical Contributor and Outstanding Leadership awards from the ONF in 2015 and 2016, respectively.

Distributed Machine Learning over Networks

Francis Bach, researcher at INRIA and adjunct Professor at Ecole Normale Supérieure.

In this talk, I will expose about recent work on distributed algorithms for supervised learning, where the data are stored and processed on distinct nodes on a network. The resulting algorithms combine features from classical machine learning and classical network averaging techniques.

Bio: Francis Bach is a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure, and a joint team between CNRS, ENS and INRIA. Since 2016, he is an adjunct Professor at Ecole Normale Supérieure. He completed his Ph.D. in Computer Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris, he then joined the WILLOW project-team at INRIA/Ecole Normale Superieure/CNRS from 2007 to 2010. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council, and received the Inria young researcher prize in 2012, the ICML test-of-time award in 2014, as well as the Lagrange prize in continuous optimization in 2018. In 2015, he was program co-chair of the International Conference in Machine learning (ICML), and general chair in 2018; he is now co-editor-in-chief of the Journal of Machine Learning Research. Francis Bach is primarily interested in machine learning, and especially in graphical models, sparse methods, kernel-based learning, large-scale convex optimization, computer vision and signal processing.

Deep Learning for Recommender Systems

Alexandros Karatzoglou, Scientific Director at Telefonica Research, Barcelona.

Most of the well known deep neural networks success cases are in tasks in the areas of computer vision, natural language processing and speech recognition. Deep learning is the “next big thing” in recommender systems, and we are starting to see deep neural networks deliver on their potential for dramatic improvement in recommendation systems technology. The aim of the talk is to present the current state-of-the-art collaborative filtering and content-based methods that use deep learning techniques to provide recommendations.

Bio: Alexandros Karatzoglou is a Scientific Director at Telefonica Research in sunny Barcelona, leading a team of Machine Learning, Networks, HCI and Systems researchers. His own research is in the area of Deep Learning, Recommender Systems, and Information Retrieval. His research in the area has been awarded with three best-paper awards at ECML/PKDD 2013, RecSys 2012 and ECML 2008. He currently teaches courses on “Deep Learning” and "Computational Machine Learning" at the Graduate School of Economics Masters Course in Data Science in Barcelona and at the GSE Data Science Summer School. He is also the author of kernlab, a fairly popular Machine Learning package for R. He received his PhD in Machine Learning from the Vienna University of Technology, while also being a frequent visitor at the Statistical Machine Learning group at NICTA in Canberra, Australia. In his spare time he enjoys kite-surfing and snowboarding.

Is Your Phone Spying on You?

David Choffnes, Professor at Northeastern University, USA

Bio: David Choffnes is a professor of Computer Science in the Khoury College at Northeastern University, and a member of the Cybersecurity and Privacy Institute. His research is primarily in the areas of distributed systems and networking, with a recent focus on privacy, security, transparency, and mobile systems. He earned a BA in Physics and French from Amherst College, a PhD from Northwestern, and completed a postdoc at the University of Washington prior to joining Northeastern. He is an NSF CAREER award winner, and his research has been supported by the National Science Foundation, Google, the Data Transparency Lab, Comcast, M-Lab, Arcep, Verizon, and a Computing Innovations Fellowship.