PPT-Network Anomaly Detection Using Autonomous

Author : cheryl-pisano | Published Date : 2017-06-02

System Flow Aggregates Thienne Johnson 12 and Loukas Lazos 1 1 Department of Electrical and Computer Engineering 2 Department of Computer Science University

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Network Anomaly Detection Using Autonomo..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Network Anomaly Detection Using Autonomous: Transcript

System Flow Aggregates Thienne Johnson 12 and Loukas Lazos 1 1 Department of Electrical and Computer Engineering 2 Department of Computer Science University of Arizona 1. 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) . 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). 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: . 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. for . eRetailer Web application. Ramya Ramalinga Moorthy, . EliteSouls Consulting Services . Contents. Introduction . Need for Performance Anomaly Detection & Forecasting Models. ERetailer Problem Space Overview. DETECTION. Scholar: . Andrew . Emmott. Focus: . Machine Learning. Advisors: . Tom . Dietterich. , Prasad . Tadepalli. Donors: . Leslie and Mark Workman. Acknowledgements:. Funding for my research is . 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. 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. “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. Marek . Pawłowski. , Gerard . Frankowski. , . Marcin. . Jerzak. , . Maciej. . Miłostan. , Tomasz Nowak. Poznań. Supercomputing and Networking Center. Agenda. Introduction . System Architecture .

Download Document

Here is the link to download the presentation.
"Network Anomaly Detection Using Autonomous"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.