On the Past, Presence and Future of “Big (Internet) Data”
Dr. Walter Willinger, Chief Scientist at NIKSUN Inc., Princeton, New Jersey, USA
External Presentation (External Speaker)
With peta-bytes of data that are continuously collected about various aspects of the Internet, how hard can it be to obtain an accurate picture of its traffic, its physical topology (i.e., router-level Internet), its logical overlays (e.g., the Web, online social networks), or its “dark” sides and associated activities (i.e. cyber crimes)? In this talk, I will first use some well-known examples of “big (Internet) data” to illustrate what this data does and doesn’t tell us about the Internet’s traffic and its physical topology. Moving on to the problem of cyber security, I will then discuss why and how future Internet measurement studies have to change so that the much-heralded “big data” approach to Internet research can achieve its full potential.
About Walter Willinger
Walter Willinger is Chief Scientist at NIKSUN, Inc., the world leader in real-time monitoring and cyber forensics solutions. Before joining NIKSUN, he worked at AT&T Labs-Research in Florham Park, NJ from 1996 to 2013 and at Bellcore Applied Research from 1986 to 1996. Dr. Willinger received his Dipl. Math. from the ETH Zurich and his M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. He is a Fellow of ACM (2005), Fellow of IEEE (2005), AT&T Fellow (2007), and Fellow of SIAM (2009), and co-recipient of the 1995 IEEE Communications Society W.R. Bennett Prize Paper Award, the 1996 IEEE W.R.G. Baker Prize Award, and the 2005 ACM/Sigcomm Test-of-Time Paper Award. His paper “On the Self-Similar Nature of Ethernet Traffic” is featured in “The Best of the Best – Fifty Years of Communications and Networking Research,” a 2007 IEEE Communications Society book compiling the most outstanding papers published in the communications and networking field in the last half century.
This event will be conducted in English
Imagen: Pixabay | Gerd Altmann