讲座摘要：

Detecting anomalous traffic is a crucial task of managing networks. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix- based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this work takes an initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). We have conducted extensive experiments using Internet traffic trace data Abilene and GE` ANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-TR can achieve significantly lower False Positive Rate and higher True  Positive Rate.

主讲人简介：

Dr. Wang is currently the director of the Wireless Networking and Systems lab of the department of Electrical and Computer Engineering of the State University of New York (SUNY) at Stony Brook. She was a Member of Technical Staff in the area of mobile and wireless networking at Bell Labs Research, Lucent Technologies, New Jersey between 2001 and 2003.  Dr. Wang has been conducting and leading research work in the design of network architectures, protocols and algorithms.  The work of her groups falls into a few directions, including advanced wireless network architecture, mobile cloud computing and distributed computing, and networked sensing, detection, and data fusion, and big data analysis and deep learning.  Dr. Wang obtained her PhD from Columbia University, BS and MS from Beijing University of Post and Telecommunications, respectively. She is a recipient of NSF career award in 2005 and the ONR Chief of Naval Research (CNR) Challenge award in 2011.

She currently serves as an associate editor of IEEE Transactions of Mobile Computing (TMC). She also serves TPC chair or program committee members in many technical conferences, including ACM MobiCom, IEEE Infocom, IEEE ICDCS, and IEEE PerCom. Her research group has published more than 100 papers in highly reputed conferences and journals, including ACM Sigmetrics, ACM MobiCom, USENIX NSDI, IEEE ICNP, IEEE Infocom, IEEE ICDCS, IEEE Percom, IEEE TON, IEEE TMC, IEEE JSAC, IEEE TC, and IEEE TDSC.

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