PPT-Machine Learning for Spam Filtering

1 Sai Koushik Haddunoori Problem Email provides a perfect way to send millions of advertisements at no cost for the sender and this unfortunate fact is nowadays extensively exploited by several . Raman Sankaran. Saneem. Ahmed. Chandrahas. . Dewangan. . Sachin. . Nagargoje. Disclaimer. Most of the images in this presentation are shamelessly downloaded from Google images. Why is this pic included here ?. Xiaoxiao. . Xu. , and Dr. Arye Nehorai. Department of Electrical and Systems Engineering, Washington University in St. Louis. Email . has become one of the most important forms of communication. In 2013, there were about 180 billion emails sent per day worldwide and 65% of the emails sent were spam emails. Links in spam emails may lead to users to websites with malware or phishing schemes. Therefore, an effective spam filtering technology is a significant contribution to the sustainability of the cyberspace and to our society.. (Machine Learning). COS 116, Spring . 2012. Adam Finkelstein. Artificial Intelligence. Definition of AI (Merriam-Webster). :. The capability of a machine to imitate intelligent human . behavior. Branch . Zhenhai Duan. Department of Computer Science. Florida State University. Outline. Motivation and background. SPOT algorithm on detecting compromised machines. Performance evaluation . Summary. 2. Motivation. Lydia Song, Lauren Steimle, . Xiaoxiao. . Xu. Outline. Introduction to Project . Pre-processing . Dimensionality Reduction. Brief discussion of different algorithms. K-nearest. D. ecision tree. Logistic regression. Hongyu. . Gao. , . Yan . Chen, Kathy Lee, Diana . Palsetia. . and . Alok. . Choudhary. Lab for Internet and Security Technology (LIST). Department of EECS. Northwestern University. Background. 2. Background. Misstear. Spam Filtering. An Artificial Intelligence Showcase. What is Spam. Messages sent indiscriminately to a large number of recipients. We all hate it. Term attributed to a Monty Python skit. Legitimate messages sometimes referred to as “ham. Subvert Your Spam Filter. Blaine Nelson / Marco . Barreno. / . fuching. . Jack Chi / Anthony D. Joseph. Benjamin I. P. Rubinstein / . Udam. . Saini. / Charles Sutton / J.D. . Tygar. / Kai Xia. University of California, Berkeley. Yinzhi Cao. Lehigh University. 1. The reason to forget. Misleading. . worm . signature generators using deliberate noise . injection, in . Proceedings. of the 2006 IEEE Symposium on Security and Privacy, 2006.. Stanford University. Learning. . to improve our lives. Input. Computers Can Learn?. Computers can learn to . predict. Computers can learn to . act. Output. Program. Parameters. Learned to get desired input/output mapping. Massimo . Poesio. INTRO TO MACHINE LEARNING. WHAT IS LEARNING. Memorizing something . Learning facts through observation and exploration . Developing motor and/or cognitive skills through practice . Organizing new knowledge into general, effective representations . Principal Group Program Manager. Microsoft. Protect Your Organization with Exchange Online Protection (EOP). SPR203. How may I protect. my employees from spam and malware. my company from data loss. using Exchange . Protection overview. Wendy Wilkes. Senior Program . M. anager. Microsoft Corporation. OUC-B306. “. SaaS secure web and email gateways frequently provide efficiency and cost advantages, and a growing number of offerings are delivering an improved level of security that exceeds what most organizations can achieve with on-premises software or appliances. Alex Beutel. Joint work with Kenton Murray, . Christos . Faloutsos. , Alex . Smola. April 9, 2014 – Seoul, South Korea. Online Recommendation. 2. 5. Users. Movies. 5. 3. 5. 5. 2. Online Rating Models.