PPT-Automated Anomaly Detection, Data Validation and Correction

Machine Learning Techniques wwwaquaticinformaticscom 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. 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. 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 . 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 . 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. see in the data they learn continuously so new patterns replace old patterns in the same way you remember recent events better than old events And if a new pattern is different but similar to previous 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. 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):. 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.