Professor Mingyan Liu
Electrical Engineering and Computer Science Department
University of Michigan
To address a myriad of threats ranging from the unintentional (misconfiguration and mismanagement) to intentional (SPAM, DDoS, botnets, etc.), network operators are increasingly relying on blacklisting/whitelisting of hosts and filtering accordingly. Commonly used host reputation lists focus on individual hosts, i.e., IP addresses, and while they reveal that certain parts of the Internet are consistently bad (blacklisted) or consistently good (not blacklisted), a large part is neither: these addresses come and go in a highly transient and dynamic fashion. This calls for a much more careful understanding of this unknown region and a more nuanced approach to evaluating their quality; a simple binary treatment of these addresses cannot be safely and consistently applied due to their dynamic nature. In this talk we advocate the idea of moving away from this microscopic/host-level view of the Internet, and instead focusing on the reputation of a larger entity (e.g., a prefix) and across different applications/data types, which we believe leads to more stable and consistent behavior captured by a set of aggregate reputation measures. In this talk I present two research efforts within this context. I start with the question of how the availability of these reputation measures could incentivize selfish networks to increase their investment/effort in strengthening its health condition. I then examine whether it is possible to incentivize networks to participate in a collective effort to achieve accurate estimates of these reputation measures, by contributing local observations/assessments about each other.
Our alumna Mingyan Liu received her Ph.D. Degree in Electrical Engineering from the University of Maryland in 2000 and has since been with the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, where she is currently a Professor. Her research interests are in optimal resource allocation, performance modeling and analysis, and energy efficient design of wireless, mobile ad hoc, and sensor networks. She is the recipient of the 2002 NSF CAREER Award, the University of Michigan Elizabeth C. Crosby Research Award in 2003, the 2010 EECS Department Outstanding Achievement Award, and a Best Paper Award at the International Conference on Information Processing in Sensor Networks (IPSN) in 2012. She serves/has served on the editorial boards of IEEE/ACM Trans. Networking, IEEE Trans. Mobile Computing, and ACM Trans. Sensor Networks.