PPT-Principled Sampling for Anomaly Detection

Brendan Juba Christopher Musco Fan Long Stelios SidiroglouDouskos and Martin Rinard Anomaly detection tradeoff Catch maliciousproblematic inputs before they reach target application. 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. Jimeng. Sun, . Huiming. . Qu. , . Deepayan. . Chakrabarti. & Christos . Faloutsos. Presented By. Bhavana. . Dalvi. Outline. Motivation. Problem Definition. Neighborhood formation. Anomaly detection. . Outline. Theory- . Prosch. , Rucker and . Bharadwaj. Robert . Burrowes. - dimensionality of nonviolence. Gandhi. King . The role of leadership. Exceptions. Exam questions. Moral grounds of civil disobedience and its limits. 2. /86. Contents. Statistical . methods. parametric. non-parametric (clustering). Systems with learning. 3. /86. Anomaly detection. Establishes . profiles of normal . user/network behaviour . Compares . 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. Psych209. January 25, 2013. A Problem For . the. Interactive . Activation Model. Data from many experiments give rise to a pattern corresponding to ‘logistic . additivity. ’. And we expect such a pattern from a Bayesian point of view.. 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. Authors. Bo Sun, Fei Yu, Kui Wu, Yang Xiao, and Victor C. M. Leung.. . Presented by . Aniruddha Barapatre. Introduction. Importance of Cellular phones.. Due to the open radio transmission environment and the physical vulnerability of mobile devices , . 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.