Finding Malware at Web Scale
Speaker: Ben Livshits, Microsoft Research (Redmond)
Date/Time: 18 December 2015, Friday, 03:00 PM to 04:30 PM
Venue: Seminar Room 2 (SR2), COM1-02-04
Chaired by: Saxena Prateek, School of Computing (firstname.lastname@example.org)
Refreshment provided; Limited seats: 40 pax; First come first serve
A number of these academic projects have been successfully deployed within Bing and have been used daily to find and block malicious web sites, constituting one of the largest-scale deployments of such techniques. This talk will focus on the complex interplay between static and runtime analyses and outline some of the lessons learned in migrating research ideas to real-world products.
These systems all share two characteristics that are key to their deployability: they are fast and extremely precise. For example, Zozzle’s false positive rate is about one in a million, while Nozzle’s is close to one in a billion.
Ben Livshits is a research scientist at Microsoft Research in Redmond, WA and an affiliate professor at the University of Washington. Originally from St. Petersburg, Russia, he received a bachelor’s degree in Computer Science and Math from Cornell University in 1999, and his M.S. and Ph.D. in Computer Science from Stanford University in 2002 and 2006, respectively. His research interests include application of sophisticated static and dynamic analysis techniques to finding errors in programs.
Ben has published papers at PLDI, POPL, Oakland Security, Usenix Security, CCS, SOSP, ICSE, FSE, and many other venues. He is known for his work in software reliability and especially tools to improve software security, with a primary focus on approaches to finding buffer overruns in C programs and a variety of security vulnerabilities (cross-site scripting, SQL injections, etc.) in Web-based applications. He is the author of several dozen academic papers and patents. Lately, he has been focusing on topics ranging from security and privacy to crowdsourcing an augmented reality. Ben generally does not speak of himself in the third person.