PPT-Anomaly detection

Author : myesha-ticknor | Published Date : 2016-09-08

Problem motivation Machine Learning Anomaly detection example Aircraft engine features heat generated vibration intensity vibration heat Dataset New engine Density

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Anomaly detection: Transcript

Problem motivation Machine Learning Anomaly detection example Aircraft engine features heat generated vibration intensity vibration heat Dataset New engine Density estimation. -. Traffic Video Surveillance. Ziming. Zhang, . Yucheng. Zhao and . Yiwen. Wan. Outline. Introduction. &Motivation. Problem Statement. Paper Summeries. Discussion and Conclusions. What are . Anomalies?. 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. 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). Anomaly-based . Network Intrusion . Detection (A-NIDS). by Nitish Bahadur, Gulsher Kooner, . Caitlin Kuhlman. 1. PALANTIR CYBER An End-to-End Cyber Intelligence Platform for Analysis & Knowledge Management [Online]. Available: . &. 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. 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. DETECTION. Scholar: . Andrew . Emmott. Focus: . Machine Learning. Advisors: . Tom . Dietterich. , Prasad . Tadepalli. Donors: . Leslie and Mark Workman. Acknowledgements:. Funding for my research is . 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. 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. 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. 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.

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