PPT-Anomaly Detection Some slides taken or adapted from:

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Anomaly Detection A Tutorial Arindam Banerjee Varun Chandola Vipin Kumar Jaideep Srivastava University of Minnesota Aleksandar Lazarevic United Technology

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Anomaly Detection Some slides taken or adapted from:: Transcript

Anomaly Detection A Tutorial Arindam Banerjee Varun Chandola Vipin Kumar Jaideep Srivastava University of Minnesota Aleksandar Lazarevic United Technology Research Center. -. Traffic Video Surveillance. Ziming. Zhang, . Yucheng. Zhao and . Yiwen. Wan. Outline. Introduction. &Motivation. Problem Statement. Paper Summeries. Discussion and Conclusions. What are . Anomalies?. 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) . 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 . Jimeng. Sun, . Huiming. . Qu. , . Deepayan. . Chakrabarti. & Christos . Faloutsos. Presented By. Bhavana. . Dalvi. Outline. Motivation. Problem Definition. Neighborhood formation. Anomaly detection. Structure. How will search and querying on these three types of data differ?. A generic. web page. containing text. A movie . review. [English]. [SQL]. [XML]. Semi-Structured. An employee . record. Slides adapted from Rao (ASU) & Franklin (Berkeley). Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. 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). for . eRetailer Web application. Ramya Ramalinga Moorthy, . EliteSouls Consulting Services . Contents. Introduction . Need for Performance Anomaly Detection & Forecasting Models. ERetailer Problem Space Overview. 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 . Vishwanath Saragadam . , Jian Wang, . Xin Li, . Aswin. . Sankaranarayanan. 1. Hyperspectral images. Information as a function of space and wavelength. Wavelength. Space. Data from . SpecTIR. 2. . 400nm. 9. Introduction to Data Mining, . 2. nd. Edition. by. Tan. , Steinbach, Karpatne, . Kumar. With additional slides and modifications by Carolina Ruiz, WPI. 11/20/2018. Introduction to Data Mining, 2nd Edition. Yasin. Yilmaz, . Mahsa. Mozaffari. Secure and Intelligent Systems Lab. sis.eng.usf.edu. Department of Electrical Engineering. University of South Florida, Tampa, FL. S. u. leyman. . Uluda. g. Department of . Kai Shen, Christopher Stewart, . Chuanpeng Li, and Xin Li. 6/16/2009. SIGMETRICS 2009. 1. University of Rochester. Performance Anomalies. 6/16/2009. SIGMETRICS 2009. 2. Complex software systems (like operating systems and distributed systems):. 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 , .

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