PPT-Effective Data-Race Detection for the Kernel

John Erickson Madanlal Musuvathi Sebastian Burckhardt Kirk Olynyk Microsoft Research Motivations Need for race detection in Kernel modules Also must detect race conditions between hardware and Kernel. 1 Hilbert Space and Kernel An inner product uv can be 1 a usual dot product uv 2 a kernel product uv vw where may have in64257nite dimensions However an inner product must satisfy the following conditions 1 Symmetry uv vu uv 8712 X 2 Bilinearity Debugging as Engineering. Much of your time in this course will be spent debugging. In industry, 50% of software dev is debugging. Even more for kernel development. How do you reduce time spent debugging?. Osck. Owen Hofmann, Alan Dunn, . Sangman. Kim, . Indrajit Roy*, Emmett Witchel. UT Austin. *HP Labs. Rootkits are dangerous. Adversary exploits insecure system. Leave backdoor . to facilitate long-term access. with Multiple Labels. Lei Tang. , . Jianhui. Chen and . Jieping. Ye. Kernel-based Methods. Kernel-based methods . Support Vector Machine (SVM). Kernel Linear Discriminate Analysis (KLDA). Demonstrate success in various domains. . Dr. M. . Asaduzzaman. . Professor. Department  of  Mathematics . University . of . Rajshahi. Rajshahi. -6205, Bangladesh. E-mail: md_asaduzzaman@hotmail.com. Definition. Let . H. be a Hilbert space comprising of complex valued . Rootkits. with lightweight Hook Protection. Authors: . Zhi. Wang, . Xuxian. Jiang, . Weidong. Cui, . Peng. . Ning. Presented by: . Purva. . Gawde. Outline. Introduction. Prior research. Problem overview. Swarnendu Biswas. , UT Austin. Man Cao. , Ohio State University. Minjia Zhang. , Microsoft Research. Michael D. Bond. , Ohio State University. Benjamin P. Wood. , Wellesley College. CC 2017. A Java Program With a Data Race. Presented by:. Nacer Khalil. Table of content. Introduction. Definition of robustness. Robust Kernel Density Estimation. Nonparametric . Contamination . Models. Scaled project Kernel Density Estimator. Syscall. Hijacking. Jeremy Fields. Intro. Ubuntu 14.04 in Hyper-V. Linux-lts-vivid-3.19.0-69. Compile vanilla kernel & load. Create basic module for learning. Kernel Module. Kernel Module . Let’s do some statistics on speed in kernel space vs user space. Machine Learning. March 25, 2010. Last Time. Basics of the Support Vector Machines. Review: Max . Margin. How can we pick which is best?. Maximize the size of the margin.. 3. Are these really . “equally valid”?. Machine Learning. March 25, 2010. Last Time. Recap of . the Support Vector Machines. Kernel Methods. Points that are . not. linearly separable in 2 dimension, might be linearly separable in 3. . Kernel Methods. Kernel Structure and Infrastructure David Ferry, Chris Gill, Brian Kocoloski CSE 422S - Operating Systems Organization Washington University in St. Louis St. Louis, MO 63130 1 Kernel vs. Application Coding Serdar . Tasiran. Koc University, Istanbul, . Turkey. Microsoft Research, Redmond. Hassan . Salehe. . Matar. ,. . Ismail . Kuru. , . Koc University, Istanbul, Turkey. Roman . Dementiev. Intel, Munich, Germany. Slobodan Vucetic * Vladimir Coric Zhuang Wang Department of Computer and Information Sciences Temple University Philadelphia, PA 19122, USA * t , y t ), t = 1…T}, where x t -dimensional inp