PPT-Neighborhood Formation and Anomaly Detection in Bipartite G

Jimeng Sun Huiming Qu Deepayan Chakrabarti amp Christos Faloutsos Presented By Bhavana Dalvi Outline Motivation Problem Definition Neighborhood formation Anomaly detection. Introduction and Use Cases. Derick . Winkworth. , Ed Henry and David Meyer. Agenda. Introduction and a Bit of History. So What Are Anomalies?. Anomaly Detection Schemes. Use Cases. Current Events. Q&A. Machine Learning . Techniques. www.aquaticinformatics.com | . 1. Touraj. . Farahmand. - . Aquatic Informatics Inc. . Kevin Swersky - . Aquatic Informatics Inc. . Nando. de . Freitas. - . Department of Computer Science – Machine Learning University of British Columbia (UBC) . Anomaly Detection for. Cyber Security. Presentation by Mike Calder . Anomaly Detection. Used for cyber security. Detecting threats using network data. Detecting threats using host-based data. In some domains, anomalies are detected so that they can be removed/corrected. By Zhangzhou. Introduction&Background. Time-Series Data. Conception & Examples & Features. Time-Series Model. Static model. Y. t. = β. 0. + β. z. t. + . μ. t. Finite Distributed Lag . 2. /86. Contents. Statistical . methods. parametric. non-parametric (clustering). Systems with learning. 3. /86. Anomaly detection. Establishes . profiles of normal . user/network behaviour . Compares . Craig Buchanan. University of Illinois at Urbana-Champaign. CS 598 MCC. 4/30/13. Outline. K-Nearest Neighbor. Neural Networks. Support Vector Machines. Lightweight Network Intrusion Detection (LNID). Detection. Carolina . Ruiz. Department of Computer Science. WPI. Slides based on . Chapter 10 of. “Introduction to Data Mining”. textbook . by Tan, Steinbach, Kumar. (all figures and some slides taken from this chapter. Other Graph Algorithms. Bipartite graph and Bipartite matching. Bipartite graph. Divide into two groups, A and B. All edges are from something in group A to something in group B. Bipartite matching. Want to uniquely match one item from group A with one item in group B. System Log Analysis for Anomaly Detection. Shilin . He. ,. . Jieming. Zhu, . Pinjia. . He,. and Michael R. . Lyu. Department of Computer Science and Engineering, . The Chinese University of Hong Kong, Hong . Lecture Notes for Chapter 10. Introduction to Data Mining. by. Tan, Steinbach, Kumar. New slides have been added and the original slides have been significantly modified by . Christoph F. . Eick. Lecture Organization . Showcase by . Abhishek Shah, Mahdi Alouane. , Marie Solman. , Satishraju Rajendran and Eno-Obong Inyang. . Showcasing work by Cai Lile, Li Yiqun On. . ANOMALY DETECTION IN THERMAL IMAGES USING DEEP NEURAL NETWORKS. Shilin . He. ,. . Jieming. Zhu, . Pinjia. . He,. and Michael R. . Lyu. Department of Computer Science and Engineering, . The Chinese University of Hong Kong, Hong Kong. 2016/10/26. Background & Motivation. “Anomaly Detection: A Tutorial”. Arindam. . Banerjee. , . Varun. . Chandola. , . Vipin. Kumar, Jaideep . Srivastava. , . University of Minnesota. Aleksandar. . Lazarevic. , . United Technology Research Center. Authors. Bo Sun, Fei Yu, Kui Wu, Yang Xiao, and Victor C. M. Leung.. . Presented by . Aniruddha Barapatre. Introduction. Importance of Cellular phones.. Due to the open radio transmission environment and the physical vulnerability of mobile devices , .