PPT-Anomaly detection for ECAL DQM

Author : shangmaxi | Published Date : 2020-08-28

Nabarun Dev 1 Colin Jessop 1 Nancy Marinelli 1 Maurizio Pierini 2 08 11 2017 1 University of Notre Dame 2 CERN DQMML meeting Outline NDev University of Notre

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Anomaly detection for ECAL DQM: Transcript

Nabarun Dev 1 Colin Jessop 1 Nancy Marinelli 1 Maurizio Pierini 2 08 11 2017 1 University of Notre Dame 2 CERN DQMML meeting Outline NDev University of Notre Dame DQMML Meeting 081117. 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 . &. Intrusion . Detection Systems. 1. Intruders. Three classes of intruders:. Examples of Intrusion. Performing a remote root compromise of an e-mail server. Defacing a Web server. Guessing and cracking passwords. for . eRetailer Web application. Ramya Ramalinga Moorthy, . EliteSouls Consulting Services . Contents. Introduction . Need for Performance Anomaly Detection & Forecasting Models. ERetailer Problem Space Overview. . for. A. utomated. D. ata. . A. nalysis (. IADA). Antanas . Norkus,Vilnius. University, . . email: antanas.norkus@cern.ch. Danilo. . Piparo. , CERN. Requirements for the product. Automatic submission of data analyses workflows. 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 . Nathalie . Japkowicz. , Colin . Bellinger. , . Shiven. Sharma, Rodney Berg, Kurt . Ungar. . University of Ottawa, Northern Illinois University. Radiation Protection Bureau, Health Canada. M. anagement (. DQM. ) . P. rogram. Midwest . Weda. Chapter. 26 . APR . 2012. Vern Gwin. Director, National DQM Center. . . Program Status. Advancements. Future. New DQM Website. Annual Goals. Integrate Customer input in tool scoping. 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. Project Lead: . Farokh. . Bastani. , I-Ling Yen, . Latifur. Khan. Date: April 7, 2011. 2010/Current Project Overview. Self-Detection of Abnormal Event Sequences. 2. Tasks:. Prepare Cisco event sequence data for analysis tools.. “Anomaly Detection: A Tutorial”. Arindam. . Banerjee. , . Varun. . Chandola. , . Vipin. Kumar, Jaideep . Srivastava. , . University of Minnesota. Aleksandar. . Lazarevic. , . United Technology Research Center. Hierarchical Temporal Memory (and LSTM). Jaime Coello de Portugal. Many thanks to . Jochem. . Snuverink. Motivation. Global outlier. Level change. Pattern deviation. Pattern change. Plots from: Ted .

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