PPT-Anomalous Payload Based Worm Detection

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. Levine Center for Intelligent Machines McGill University Montreal QC Canada javancimmcgillca levinecimmcgillca Abstract We present a novel approach for video parsing and si multaneous online learning of dominant and anomalous behaviors in surveillan Vol 47 No2 2005 2013 JATIT LLS All rights reserved ISSN 1992 8645 wwwjatitorg ISSN 1817 3195 514 A NOVEL APPROACH FOR DETECTING SMART CAMOUFLAGING WORM JEEVAAKATIRAVAN DHEMAPRIYADHARSHINI CCHELLAPAN RDHANALAKSHMI Assistant Professor PG Scholar COS 116, . Spring . 2012. Adam Finkelstein. Encryption . (topic late in class). Encryption strongly protects data en route. You. Amazon.com. Today. '. s . story: Attacker can compromise your computer. 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. UTSA. Moheeb Abu Rajab, Lucas Ballard, Nav Jagpal, Panayiotis Mavrommatis,. Daisuke Nojiri, Niels Provos, Ludwig Schmidt. Present by Li Xu. 2. Detecting Malicious Web Sites. Which pages are safe URLs for end users?. Siddharth Gupta. 1. , Casey Hanson. 2. , Carl A Gunter. 3. , Mario Frank. 4. , David Liebovitz. 4. , Bradley . Malin. 6. 1,2,3,4. Department of Computer Science, . 3,5. Department of Medicine, . 6. Department of Biomedical Informatics. Computer Security Techniques. Patricia Roy. Manatee Community College, Venice, FL. ©2008, Prentice Hall. Operating Systems:. Internals and Design Principles, 6/E. William Stallings. Authentication. Basis for most type of access control and accountability. 1. Viruses don’t break into your computer – they . are invited by you. They cannot spread unless you run infected application or click on infected attachment. Early viruses spread onto different applications on your computer. Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. Based on slides from Computer Security: Principles and Practices by William Stallings and Lawrie Brown. CSC230: C and Software Tools © NC State University Computer Science Faculty. 1. Malware. [SOUP13] defines malware as:. Presenter: Dave McDonald. Rosco Vision Systems. Agenda. Background. Cameron Gulbransen Kids Transportation Safety Act of 2007. Abigail’s Law – New Jersey. Current Technologies. Electronic Based Detection. Milad Ghaznavi. 1. Outline. Introduction. Dataset. Multi Layer Perceptron. Convolutional Neural Network. Evaluation. Related Work. Conclusion. 2. Introduction. Intrusion Detection. Background. 3. Intrusion Detection. “Anomaly Detection: A Tutorial”. Arindam. . Banerjee. , . Varun. . Chandola. , . Vipin. Kumar, Jaideep . Srivastava. , . University of Minnesota. Aleksandar. . Lazarevic. , . United Technology Research Center. By. Harshith Reddy . Sarabudla. Anomaly detection approaches. Command-centric – focus on attack syntax. Mostly capture attack queries that have similar columns but process or display different row contents from those of normal queries.