Seminar: Learning Binary Code Represenatations for Security Applications
Associate Professor, University of California
Friday, September, 13, 2019
11:15am - 12:15pm
2150 Torgersen Hall
Learning a numeric representation (also known as embedded vector, or simply embedding) for a piece of binary code (an instruction, a basic block, a function, or even an entire program) has many important security applications, ranging from vulnerability search, plagiarism detection, to malware classification. By reducing a binary code with complex control-flow and data-flow dependencies into a numeric vector using deep learning techniques, we convert complex binary code detection and search problems into search of embeddings, which can be done in O(1) time and often can achieve even higher accuracy than traditional methods. In this talk, I am going to show how we can revolutionize several security applications using this approach, including vulnerability search, malware variant detection, and binary diffing. In the end, I'd like to shed some light on adversarial learning against this new approach.
Dr. Heng Yin is an associate professor in the department of Computer Science and Engineering at University of California, Riverside. He is the director of CRESP (Center for Research and Education in Cyber Security and Privacy) at UCR. He obtained his PhD degree from College of William and Mary in 2009, and MS and BS from Huazhong University of Science and Technology in 2002 and 1999. His research interests lie in computer security, with emphasis on binary code analysis. His publications appear in top-notch technical conferences and journals, such as ACM CCS, USENIX Security, NDSS, TSE,TDSC, etc. His research is sponsored by National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Air Force Office of Scientific Research (AFOSR), and Office of Navy Research (ONR). In 2011, he received prestigious NSF Career award. He was the technical co-lead of CodeJitsu, one of the seven finalists in DARPA Cyber Grand Challenge.