Software-Based Fault Injection Attacks Against Intel SGX
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System Programming Guide
Special Edition for CSEDU12 Students Students TOUCH -N-PASS EXAM CRAM GUIDE SERIES SYSTEM PROGRAMMING Prepared By Sharafat Ibn Mollah Mosharraf CSE, DU 12 th Batch (2005-2006) TABLE OF CONTENTS APIS AT A GLANCE ....................................................................................................................................................................... 1 CHAPTER 1: ASSEMBLER, LINKER & LOADER ................................................................................................................................ 4 CHAPTER 2: KERNEL ..................................................................................................................................................................... 9 CHAPTER 3: UNIX ELF FILE FORMAT ............................................................................................................................................ 11 CHAPTER 4: DEVICE DRIVERS ...................................................................................................................................................... 16 CHAPTER 5: INTERRUPT .............................................................................................................................................................. 20 CHAPTER 6: SYSTEM CALL ........................................................................................................................................................... 24 CHAPTER 7: FILE APIS ................................................................................................................................................................ -
UNIT 8B a Full Adder
UNIT 8B Computer Organization: Levels of Abstraction 15110 Principles of Computing, 1 Carnegie Mellon University - CORTINA A Full Adder C ABCin Cout S in 0 0 0 A 0 0 1 0 1 0 B 0 1 1 1 0 0 1 0 1 C S out 1 1 0 1 1 1 15110 Principles of Computing, 2 Carnegie Mellon University - CORTINA 1 A Full Adder C ABCin Cout S in 0 0 0 0 0 A 0 0 1 0 1 0 1 0 0 1 B 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 C S out 1 1 0 1 0 1 1 1 1 1 ⊕ ⊕ S = A B Cin ⊕ ∧ ∨ ∧ Cout = ((A B) C) (A B) 15110 Principles of Computing, 3 Carnegie Mellon University - CORTINA Full Adder (FA) AB 1-bit Cout Full Cin Adder S 15110 Principles of Computing, 4 Carnegie Mellon University - CORTINA 2 Another Full Adder (FA) http://students.cs.tamu.edu/wanglei/csce350/handout/lab6.html AB 1-bit Cout Full Cin Adder S 15110 Principles of Computing, 5 Carnegie Mellon University - CORTINA 8-bit Full Adder A7 B7 A2 B2 A1 B1 A0 B0 1-bit 1-bit 1-bit 1-bit ... Cout Full Full Full Full Cin Adder Adder Adder Adder S7 S2 S1 S0 AB 8 ⁄ ⁄ 8 C 8-bit C out FA in ⁄ 8 S 15110 Principles of Computing, 6 Carnegie Mellon University - CORTINA 3 Multiplexer (MUX) • A multiplexer chooses between a set of inputs. D1 D 2 MUX F D3 D ABF 4 0 0 D1 AB 0 1 D2 1 0 D3 1 1 D4 http://www.cise.ufl.edu/~mssz/CompOrg/CDAintro.html 15110 Principles of Computing, 7 Carnegie Mellon University - CORTINA Arithmetic Logic Unit (ALU) OP 1OP 0 Carry In & OP OP 0 OP 1 F 0 0 A ∧ B 0 1 A ∨ B 1 0 A 1 1 A + B http://cs-alb-pc3.massey.ac.nz/notes/59304/l4.html 15110 Principles of Computing, 8 Carnegie Mellon University - CORTINA 4 Flip Flop • A flip flop is a sequential circuit that is able to maintain (save) a state. -
Assembly Language Programming for X86 Processors Pdf
Assembly language programming for x86 processors pdf Continue The x86, 7e collection language is for use in undergraduate programming courses in a speaking language and introductory courses in computer systems and computer architecture. This name is also suitable for built-in systems of programmers and engineers, communications specialists, game programmers and graphic programmers. It is recommended to know another programming language, preferably Java, C or C. Written specifically for the 32- and 64-bit Intel/Windows platform, this complete and complete learning of the acoustics language teaches students to write and fine-tune programs at the machine level. This text simplifies and demystiifies concepts that students need to understand before they laugh at more advanced courses in computer architecture and operating systems. Students practice theory using machine-level software, creating an unforgettable experience that gives them confidence to work in any OS/machine-oriented environment. Additional learning and learning tools are available on the author's website in where both teachers and students can access chapter goals, debugging tools, additional files, starting work with the MASM and Visual Studio 2012 tutorial, and more. Learning and Learning Experience This program represents the best learning and learning experience for you and your students. This will help: Teach Effective Design Techniques: Top-Down Demonstration Design Program and Explanation allows students to apply techniques for multiple programming courses. Put Theory into practice: Students will write software at machine level, preparing them to work in any OS/machine oriented environment. Tailor text to fit your course: Instructors can cover additional chapter themes in different order and depth. -
Hiding Process Memory Via Anti-Forensic Techniques
DIGITAL FORENSIC RESEARCH CONFERENCE Hiding Process Memory via Anti-Forensic Techniques By: Frank Block (Friedrich-Alexander Universität Erlangen-Nürnberg (FAU) and ERNW Research GmbH) and Ralph Palutke (Friedrich-Alexander Universität Erlangen-Nürnberg) From the proceedings of The Digital Forensic Research Conference DFRWS USA 2020 July 20 - 24, 2020 DFRWS is dedicated to the sharing of knowledge and ideas about digital forensics research. Ever since it organized the first open workshop devoted to digital forensics in 2001, DFRWS continues to bring academics and practitioners together in an informal environment. As a non-profit, volunteer organization, DFRWS sponsors technical working groups, annual conferences and challenges to help drive the direction of research and development. https://dfrws.org Forensic Science International: Digital Investigation 33 (2020) 301012 Contents lists available at ScienceDirect Forensic Science International: Digital Investigation journal homepage: www.elsevier.com/locate/fsidi DFRWS 2020 USA d Proceedings of the Twentieth Annual DFRWS USA Hiding Process Memory Via Anti-Forensic Techniques Ralph Palutke a, **, 1, Frank Block a, b, *, 1, Patrick Reichenberger a, Dominik Stripeika a a Friedrich-Alexander Universitat€ Erlangen-Nürnberg (FAU), Germany b ERNW Research GmbH, Heidelberg, Germany article info abstract Article history: Nowadays, security practitioners typically use memory acquisition or live forensics to detect and analyze sophisticated malware samples. Subsequently, malware authors began to incorporate anti-forensic techniques that subvert the analysis process by hiding malicious memory areas. Those techniques Keywords: typically modify characteristics, such as access permissions, or place malicious data near legitimate one, Memory subversion in order to prevent the memory from being identified by analysis tools while still remaining accessible. -
Enclave Security and Address-Based Side Channels
Graz University of Technology Faculty of Computer Science Institute of Applied Information Processing and Communications IAIK Enclave Security and Address-based Side Channels Assessors: A PhD Thesis Presented to the Prof. Stefan Mangard Faculty of Computer Science in Prof. Thomas Eisenbarth Fulfillment of the Requirements for the PhD Degree by June 2020 Samuel Weiser Samuel Weiser Enclave Security and Address-based Side Channels DOCTORAL THESIS to achieve the university degree of Doctor of Technical Sciences; Dr. techn. submitted to Graz University of Technology Assessors Prof. Stefan Mangard Institute of Applied Information Processing and Communications Graz University of Technology Prof. Thomas Eisenbarth Institute for IT Security Universit¨atzu L¨ubeck Graz, June 2020 SSS AFFIDAVIT I declare that I have authored this thesis independently, that I have not used other than the declared sources/resources, and that I have explicitly indicated all material which has been quoted either literally or by content from the sources used. The text document uploaded to TUGRAZonline is identical to the present doctoral thesis. Date, Signature SSS Prologue Everyone has the right to life, liberty and security of person. Universal Declaration of Human Rights, Article 3 Our life turned digital, and so did we. Not long ago, the globalized commu- nication that we enjoy today on an everyday basis was the privilege of a few. Nowadays, artificial intelligence in the cloud, smartified handhelds, low-power Internet-of-Things gadgets, and self-maneuvering objects in the physical world are promising us unthinkable freedom in shaping our personal lives as well as society as a whole. Sadly, our collective excitement about the \new", the \better", the \more", the \instant", has overruled our sense of security and privacy. -
With Extreme Scale Computing the Rules Have Changed
With Extreme Scale Computing the Rules Have Changed Jack Dongarra University of Tennessee Oak Ridge National Laboratory University of Manchester 11/17/15 1 • Overview of High Performance Computing • With Extreme Computing the “rules” for computing have changed 2 3 • Create systems that can apply exaflops of computing power to exabytes of data. • Keep the United States at the forefront of HPC capabilities. • Improve HPC application developer productivity • Make HPC readily available • Establish hardware technology for future HPC systems. 4 11E+09 Eflop/s 362 PFlop/s 100000000100 Pflop/s 10000000 10 Pflop/s 33.9 PFlop/s 1000000 1 Pflop/s SUM 100000100 Tflop/s 166 TFlop/s 1000010 Tflop /s N=1 1 Tflop1000/s 1.17 TFlop/s 100 Gflop/s100 My Laptop 70 Gflop/s N=500 10 59.7 GFlop/s 10 Gflop/s My iPhone 4 Gflop/s 1 1 Gflop/s 0.1 100 Mflop/s 400 MFlop/s 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2015 1 Eflop/s 1E+09 420 PFlop/s 100000000100 Pflop/s 10000000 10 Pflop/s 33.9 PFlop/s 1000000 1 Pflop/s SUM 100000100 Tflop/s 206 TFlop/s 1000010 Tflop /s N=1 1 Tflop1000/s 1.17 TFlop/s 100 Gflop/s100 My Laptop 70 Gflop/s N=500 10 59.7 GFlop/s 10 Gflop/s My iPhone 4 Gflop/s 1 1 Gflop/s 0.1 100 Mflop/s 400 MFlop/s 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2015 1E+10 1 Eflop/s 1E+09 100 Pflop/s 100000000 10 Pflop/s 10000000 1 Pflop/s 1000000 SUM 100 Tflop/s 100000 10 Tflop/s N=1 10000 1 Tflop/s 1000 100 Gflop/s N=500 100 10 Gflop/s 10 1 Gflop/s 1 100 Mflop/s 0.1 1996 2002 2020 2008 2014 1E+10 1 Eflop/s 1E+09 100 Pflop/s 100000000 10 Pflop/s 10000000 1 Pflop/s 1000000 SUM 100 Tflop/s 100000 10 Tflop/s N=1 10000 1 Tflop/s 1000 100 Gflop/s N=500 100 10 Gflop/s 10 1 Gflop/s 1 100 Mflop/s 0.1 1996 2002 2020 2008 2014 • Pflops (> 1015 Flop/s) computing fully established with 81 systems. -
Theoretical Peak FLOPS Per Instruction Set on Modern Intel Cpus
Theoretical Peak FLOPS per instruction set on modern Intel CPUs Romain Dolbeau Bull – Center for Excellence in Parallel Programming Email: [email protected] Abstract—It used to be that evaluating the theoretical and potentially multiple threads per core. Vector of peak performance of a CPU in FLOPS (floating point varying sizes. And more sophisticated instructions. operations per seconds) was merely a matter of multiplying Equation2 describes a more realistic view, that we the frequency by the number of floating-point instructions will explain in details in the rest of the paper, first per cycles. Today however, CPUs have features such as vectorization, fused multiply-add, hyper-threading or in general in sectionII and then for the specific “turbo” mode. In this paper, we look into this theoretical cases of Intel CPUs: first a simple one from the peak for recent full-featured Intel CPUs., taking into Nehalem/Westmere era in section III and then the account not only the simple absolute peak, but also the full complexity of the Haswell family in sectionIV. relevant instruction sets and encoding and the frequency A complement to this paper titled “Theoretical Peak scaling behavior of current Intel CPUs. FLOPS per instruction set on less conventional Revision 1.41, 2016/10/04 08:49:16 Index Terms—FLOPS hardware” [1] covers other computing devices. flop 9 I. INTRODUCTION > operation> High performance computing thrives on fast com- > > putations and high memory bandwidth. But before > operations => any code or even benchmark is run, the very first × micro − architecture instruction number to evaluate a system is the theoretical peak > > - how many floating-point operations the system > can theoretically execute in a given time. -
The Confinement Principle
Isolaon The confinement principle Original slides were created by Prof. Dan Boneh 1 Running untrusted code We often need to run buggy/unstrusted code: – programs from untrusted Internet sites: • apps, extensions, plug-ins, codecs for media player – exposed applications: pdf viewers, outlook – legacy daemons: sendmail, bind – honeypots Goal: if application “misbehaves” ⇒ kill it 2 Approach: confinement Confinement: ensure misbehaving app cannot harm rest of system • Can be implemented at many levels: – Hardware: run application on isolated hw (air gap) app 1 app 2 Network 2 air gap network 1 ⇒ difficult to manage, expensive 3 Approach: confinement Confinement: ensure misbehaving app cannot harm rest of system • Can be implemented at many levels: – Virtual machines: isolate OS’s on a single machine What are some of the drawbacks of this approach? app1 app2 OS1 OS2 Virtual Machine Monitor (VMM) 4 Approach: confinement Confinement: ensure misbehaving app cannot harm rest of system • Can be implemented at many levels: – Process: System Call Interposition Isolate a process in a single operating system process 1 process 2 Operang System 5 Approach: confinement Confinement: ensure misbehaving app cannot harm rest of system • Can be implemented at many levels: – Threads: Software Fault Isolation (SFI) • Isolating threads sharing same address space – Application: e.g. browser-based confinement 6 Implementing confinement Key component: reference monitor – Mediates requests from applications • Implements protection policy • Enforces isolation and -
Misleading Performance Reporting in the Supercomputing Field David H
Misleading Performance Reporting in the Supercomputing Field David H. Bailey RNR Technical Report RNR-92-005 December 1, 1992 Ref: Scientific Programming, vol. 1., no. 2 (Winter 1992), pg. 141–151 Abstract In a previous humorous note, I outlined twelve ways in which performance figures for scientific supercomputers can be distorted. In this paper, the problem of potentially mis- leading performance reporting is discussed in detail. Included are some examples that have appeared in recent published scientific papers. This paper also includes some pro- posed guidelines for reporting performance, the adoption of which would raise the level of professionalism and reduce the level of confusion in the field of supercomputing. The author is with the Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Ames Research Center, Moffett Field, CA 94035. 1 1. Introduction Many readers may have read my previous article “Twelve Ways to Fool the Masses When Giving Performance Reports on Parallel Computers” [5]. The attention that this article received frankly has been surprising [11]. Evidently it has struck a responsive chord among many professionals in the field who share my concerns. The following is a very brief summary of the “Twelve Ways”: 1. Quote only 32-bit performance results, not 64-bit results, and compare your 32-bit results with others’ 64-bit results. 2. Present inner kernel performance figures as the performance of the entire application. 3. Quietly employ assembly code and other low-level language constructs, and compare your assembly-coded results with others’ Fortran or C implementations. 4. Scale up the problem size with the number of processors, but don’t clearly disclose this fact. -
Intel Xeon & Dgpu Update
Intel® AI HPC Workshop LRZ April 09, 2021 Morning – Machine Learning 9:00 – 9:30 Introduction and Hardware Acceleration for AI OneAPI 9:30 – 10:00 Toolkits Overview, Intel® AI Analytics toolkit and oneContainer 10:00 -10:30 Break 10:30 - 12:30 Deep dive in Machine Learning tools Quizzes! LRZ Morning Session Survey 2 Afternoon – Deep Learning 13:30 – 14:45 Deep dive in Deep Learning tools 14:45 – 14:50 5 min Break 14:50 – 15:20 OpenVINO 15:20 - 15:45 25 min Break 15:45 – 17:00 Distributed training and Federated Learning Quizzes! LRZ Afternoon Session Survey 3 INTRODUCING 3rd Gen Intel® Xeon® Scalable processors Performance made flexible Up to 40 cores per processor 20% IPC improvement 28 core, ISO Freq, ISO compiler 1.46x average performance increase Geomean of Integer, Floating Point, Stream Triad, LINPACK 8380 vs. 8280 1.74x AI inference increase 8380 vs. 8280 BERT Intel 10nm Process 2.65x average performance increase vs. 5-year-old system 8380 vs. E5-2699v4 Performance varies by use, configuration and other factors. Configurations see appendix [1,3,5,55] 5 AI Performance Gains 3rd Gen Intel® Xeon® Scalable Processors with Intel Deep Learning Boost Machine Learning Deep Learning SciKit Learn & XGBoost 8380 vs 8280 Real Time Inference Batch 8380 vs 8280 Inference 8380 vs 8280 XGBoost XGBoost up to up to Fit Predict Image up up Recognition to to 1.59x 1.66x 1.44x 1.30x Mobilenet-v1 Kmeans Kmeans up to Fit Inference Image up to up up Classification to to 1.52x 1.56x 1.36x 1.44x ResNet-50-v1.5 Linear Regression Linear Regression Fit Inference Language up to up to up up Processing to 1.44x to 1.60x 1.45x BERT-large 1.74x Performance varies by use, configuration and other factors. -
Chapter 4 Data-Level Parallelism in Vector, SIMD, and GPU Architectures
Computer Architecture A Quantitative Approach, Fifth Edition Chapter 4 Data-Level Parallelism in Vector, SIMD, and GPU Architectures Copyright © 2012, Elsevier Inc. All rights reserved. 1 Contents 1. SIMD architecture 2. Vector architectures optimizations: Multiple Lanes, Vector Length Registers, Vector Mask Registers, Memory Banks, Stride, Scatter-Gather, 3. Programming Vector Architectures 4. SIMD extensions for media apps 5. GPUs – Graphical Processing Units 6. Fermi architecture innovations 7. Examples of loop-level parallelism 8. Fallacies Copyright © 2012, Elsevier Inc. All rights reserved. 2 Classes of Computers Classes Flynn’s Taxonomy SISD - Single instruction stream, single data stream SIMD - Single instruction stream, multiple data streams New: SIMT – Single Instruction Multiple Threads (for GPUs) MISD - Multiple instruction streams, single data stream No commercial implementation MIMD - Multiple instruction streams, multiple data streams Tightly-coupled MIMD Loosely-coupled MIMD Copyright © 2012, Elsevier Inc. All rights reserved. 3 Introduction Advantages of SIMD architectures 1. Can exploit significant data-level parallelism for: 1. matrix-oriented scientific computing 2. media-oriented image and sound processors 2. More energy efficient than MIMD 1. Only needs to fetch one instruction per multiple data operations, rather than one instr. per data op. 2. Makes SIMD attractive for personal mobile devices 3. Allows programmers to continue thinking sequentially SIMD/MIMD comparison. Potential speedup for SIMD twice that from MIMID! x86 processors expect two additional cores per chip per year SIMD width to double every four years Copyright © 2012, Elsevier Inc. All rights reserved. 4 Introduction SIMD parallelism SIMD architectures A. Vector architectures B. SIMD extensions for mobile systems and multimedia applications C. -
Computer Architectures an Overview
Computer Architectures An Overview PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 25 Feb 2012 22:35:32 UTC Contents Articles Microarchitecture 1 x86 7 PowerPC 23 IBM POWER 33 MIPS architecture 39 SPARC 57 ARM architecture 65 DEC Alpha 80 AlphaStation 92 AlphaServer 95 Very long instruction word 103 Instruction-level parallelism 107 Explicitly parallel instruction computing 108 References Article Sources and Contributors 111 Image Sources, Licenses and Contributors 113 Article Licenses License 114 Microarchitecture 1 Microarchitecture In computer engineering, microarchitecture (sometimes abbreviated to µarch or uarch), also called computer organization, is the way a given instruction set architecture (ISA) is implemented on a processor. A given ISA may be implemented with different microarchitectures.[1] Implementations might vary due to different goals of a given design or due to shifts in technology.[2] Computer architecture is the combination of microarchitecture and instruction set design. Relation to instruction set architecture The ISA is roughly the same as the programming model of a processor as seen by an assembly language programmer or compiler writer. The ISA includes the execution model, processor registers, address and data formats among other things. The Intel Core microarchitecture microarchitecture includes the constituent parts of the processor and how these interconnect and interoperate to implement the ISA. The microarchitecture of a machine is usually represented as (more or less detailed) diagrams that describe the interconnections of the various microarchitectural elements of the machine, which may be everything from single gates and registers, to complete arithmetic logic units (ALU)s and even larger elements.