PDF-One-Class Training for Masquerade Detection Ke Wang Salvatore J. Stol

This self profile idea is similar to the widely used. Parekh Salvatore J Stolfo Computer Science Department Columbia University 500 West 120 th Street New York NY 10027 Abstract In this paper we present a content anomaly detector that models n 1 designed to detect anomalous and suspicious network pac Parekh Salvatore J Stolfo Computer Science Department Columbia University 500 West 120 th Street New York NY 10027 kewang janak salcscolumbiaedu Abstract In this paper we present Anagram a content anomaly detector that models a mixture of highorder Denomination Pink/Orange yellow $20 green A Jasco FP6200 fluorescence spectrophotometer equipped with a solid sample holder accessory was used for all analyses. The solid sample holder is kin State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Prafulla Dawadi. Topics in Machine Learning. Outline. Part I. Examples. Rare Class, Imbalanced Class, Outliers. Part II. (Rare)Category Detection. Part III. Kernel Density Estimation . Mean Shift and Hierarchal Mean Shift. David Westlund, Rusty Jones. FAA. Stephen Neidigk, Dennis Roach, . Tom . Rice, . Randy . Duvall. Sandia National Labs. FAA Airworthiness Assurance Center. Filament Diameters. Airlines for America NDT – September 2015. Mark Stamp. 1. Masquerade Detection. Masquerade Detection. Masquerader --- someone who makes unauthorized use of a computer. How to detect. . a masquerader. ?. Here, we consider…. Anomaly-based intrusion detection (IDS). Ke Wang, Gabriela Cretu, Salvatore Stolfo. Computer Science, Columbia University. Mike Kopps. CS591. Agenda. The Problem. Existing Solutions. Solution. Methodology. Collaboration. Evaluation. Even . More Problems. History suggests that the now world famous Masquerade Masks originated in Venice, Italy during the 15th Century. They were primarily in use by general public before Masquerade Balls came into existence in 16th Century Renaissance. Further theories suggest that the original usage was meant to conceal identity of the wearer as well as to equalize people from higher and lower classes during public events.. Prafulla Dawadi. Topics in Machine Learning. Outline. Part I. Examples. Rare Class, Imbalanced Class, Outliers. Part II. (Rare)Category Detection. Part III. Kernel Density Estimation . Mean Shift and Hierarchal Mean Shift. PHMM Applications. 1. Mark Stamp. Applications. We consider 2 applications of PHMMs from information security. Masquerade detection. Malware detection. Both show some strengths of PHMMs. Both are somewhat unique . Towards a Masquerade Detection System Based on User’s Tasks J. Benito Camiña , Jorge Rodr íguez, and Raúl Monroy Presentation by Calvin Raines What is a masquerade attack? password123 Hello <Your Name Here> (Courtesy . Boris . Babenko. ). slides adapted from Svetlana . Lazebnik. Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/.