PPT-Kernel Structure and Infrastructure

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. 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 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. Mode, space, and context: the basics. Jeff Chase. Duke University. 64 bytes: 3 ways. p + 0x0. 0x1f. 0x0. 0x1f. 0x1f. 0x0. char p[]. char *p. int p[]. int* p. p. char* p[]. char** p. Pointers (addresses) are 8 bytes on a 64-bit machine.. 0.2 0.4 0.6 0.8 1.0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 kernel(b) kernel(c) kernel(d) (a)blurredimage(b)no-blurredimage0.900.981.001.021.10 (5.35,3.37)(4.80,3.19)(4.71,3.22)(4.93,3.23)(5.03,3.22 KAIST . CySec. Lab. 1. Contents. About Rootkit. Concept and Methods. Examples. Ubuntu Linux (Network Hiding. ). Windows 7 (File Hiding). Android Rootkit Demonstration (DNS Spoofing). Exercise (Rootkit Detection). A B M Shawkat Ali. 1. 2. Data Mining. ¤. . DM or KDD (Knowledge Discovery in Databases). Extracting previously unknown, valid, and actionable information . . . crucial decisions. ¤. . Approach. 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. 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. David Ferry, Chris Gill. Department of Computer Science and Engineering. Washington University, St. Louis MO. davidferry@wustl.edu. 1. Traditional View of Process Execution. However, the kernel is not a traditional process!. Yue . Chen. 1. , . Yulong . Zhang. 2. , . Zhi. . Wang. 1. , . Liangzhao. . Xia. 2. , . Chenfu. . Bao. 2. , . Tao . Wei. 2. Florida State University. 1. Baidu X-Lab. 2. USENIX Security Symposium 2017. David Ferry, Chris Gill. Department of Computer Science and Engineering. Washington University, St. Louis MO. davidferry@wustl.edu. 1. Traditional View of Process Execution. However, the kernel is not a traditional process!. Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . Peter . Feiner. , Angela . Demke. Brown, . Ashvin. . Goel. University of Toronto. Presenter: . Chuong. Ngo. The Origin Story Starting IN medias Res. No parents, uncles, or girlfriends were killed during the creation of this presentation.