PPT-CS548 Spring 2015 Anomaly Detection Showcase

Anomalybased Network Intrusion Detection ANIDS by Nitish Bahadur Gulsher Kooner Caitlin Kuhlman 1 PALANTIR CYBER An EndtoEnd Cyber Intelligence Platform for Analysis amp Knowledge Management Online Available . Jimeng. Sun, . Huiming. . Qu. , . Deepayan. . Chakrabarti. & Christos . Faloutsos. Presented By. Bhavana. . Dalvi. Outline. Motivation. Problem Definition. Neighborhood formation. Anomaly detection. 2. /86. Contents. Statistical . methods. parametric. non-parametric (clustering). Systems with learning. 3. /86. Anomaly detection. Establishes . profiles of normal . user/network behaviour . Compares . Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. SHOWCASING. your Revit Model. Ryan B. Cameron,. AIA, LEED AP, EDAC, NCARB. . ryan@ryanbakercameron.com. Architectural Consultant . . www.ryanbakercameron.com. Lincoln, Nebraska . DETECTION. Scholar: . Andrew . Emmott. Focus: . Machine Learning. Advisors: . Tom . Dietterich. , Prasad . Tadepalli. Donors: . Leslie and Mark Workman. Acknowledgements:. Funding for my research is . By Ashley and Teighgan. CHoir. The school choir is excited to be performing at Laycock Street Theatre on the 24. th. of August as part of a mini mass choir with Ettalong and Niagara . Park Public Schools. . Showcase . by . Nichole Etienne, Rohitpal Singh, Suchithra Balakrishnan, Yousef Fadila. Showcasing work by Bowen Du, Chuaren Liu, Wenjun Zhou, Zhenshan Hou, Hui Xiong on “ . Catch Me If You Can - Detecting Pickpocket Suspects from Large-Scale Transit Records. 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. 14. . World-Leading Research with Real-World Impact!. CS 5323. Outline. Anomaly detection. Facts and figures. Application. Challenges. Classification. Anomaly in Wireless.  . 2. Recent News. Hacking of Government Computers Exposed 21.5 Million People. 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 . Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly.