Title:

Nonparametric Detection of Anomalous Data via Maximum Mean Discrepancy

Abstract:

This talk will focus on a type of problems, the goal of which is to
detect existence of an anomalous object over a network. An anomalous
object, if it exists, corresponds to a cluster of nodes in the network
that take data samples generated by an anomalous distribution q
whereas all other nodes in the network receive samples generated by a
distinct distribution p. Such a problem models a variety of
applications such as detection of an anomalous intrusion via sensor
networks and detection of an anomalous segment in a DNA sequence. All
previous studies of this problem have taken parametric models, i.e.,
distributions p and q are known. Our work studies the nonparametric
model, in which distributions can be arbitrary and unknown a priori.

In this talk, I will first introduce the approach that we apply, which
is based on mean embedding of distributions into a reproducing kernel
Hilbert space (RKHS). In particular, we adopt the quantity of maximum
mean discrepancy (MMD) as a metric of distance between mean embeddings
of two distributions. I will then present our construction of
MMD-based tests for anomalous detection over networks and our analysis
of consistency of the proposed tests. I will finally present a number
of numerical results to demonstrate our results. Towards the end of
the talk, I will discuss some related problems and conclude with a few
future directions.

Bio:

Dr. Yingbin Liang received the Ph.D. degree in Electrical Engineering
from the University of Illinois at Urbana-Champaign in 2005. In
2005-2007, she was working as a postdoctoral research associate at
Princeton University. In 2008-2009, she was an assistant professor at
the University of Hawaii. Since December 2009, she has been on the
faculty at Syracuse University, where she is an associate professor.
Dr. Liang's research interests include information theory, wireless
communications and networks, and machine learning.

Dr. Liang was a Vodafone Fellow at the University of Illinois at
Urbana-Champaign during 2003-2005, and received the Vodafone-U.S.
Foundation Fellows Initiative Research Merit Award in 2005. She also
received the M. E. Van Valkenburg Graduate Research Award from the ECE
department, University of Illinois at Urbana-Champaign, in 2005. In
2009, she received the National Science Foundation CAREER Award, and
the State of Hawaii Governor Innovation Award. More recently, her
paper titled “compound wiretap channels” received the 2014 EURASIP
Best Paper Award for the EURASIP Journal on Wireless Communications
and Networking.