主题：Spectral Methods for Detecting Random Link Attacks and Subtle Anomalies in Social Networks
主讲人：Dr. Xintao Wu, University of North Carolina at Charlotte
时间：2011 年 11 月 22 日（周二）上午 9:30
地点：张江校区软件楼 105，IBM 会议室
Abstract
Social networks are vulnerable to both large-scale attacks (e.g., spam, denial of services, Sybil attacks) and subtle anomalies. Traditional topology-based detection methods often fail to effectively identify those collaborative attacks and subtle anomalies. In this talk, we present a spectrum based fraud detection framework in which we examine the eigenvectors of the adjacency matrix of the underlying graph topology. We conduct theoretical analysis to show attacking nodes locate in a different region of the spectral space from regular nodes. In particular, we focus on 1) Random Link Attacks in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack a large number of randomly chosen users of the network; and 2) subtle anomalies that are often embedded within a community but are structurally dissimilar to the background. We develop two spectral methods to detect them by examining the eigen-subspaces formed by principal eigenvectors and minor vectors respectively. Empirical evaluations on both synthetic data and real social networks show that our spectral methods are very effective in detecting those attacks and outperform techniques previously published.
Short Biography
Dr. Xintao Wu is an Associate Professor in the Department of Software and Information Systems at the University of North Carolina at Charlotte, USA. He got his Ph.D. degree in Information Technology from George Mason University in 2001. He received his BS degree in Information Science from the University of Science and Technology of China in 1994, an ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997. His major research interests include data mining, data privacy and security, and social network analysis. His recent research work has been to apply spectral analysis for fraud detection in social networks and develop privacy preserving data mining techniques for linked data in social networks. Dr. Wu is an editor of Journal of Intelligent Information Systems, Transaction on Data Privacy, and International Journal of Social Network Mining. He served on program committees of top international conferences, including ACM KDD, IEEE ICDM, SIAM SDM, PKDD, and PAKDD. Dr. Wu is a recipient of NSF Career Award and college's Faculty Research Award from UNC Charlotte.
