Dieses Lehrbuch führt den Leser in konkrete Techniken und Begriffe der Analysis ein, mit deren Hilfe sich komplexe quantitative Zusammenhänge vereinfachen und verstehen lassen. Es richtet sich in erster Linie an Studierende der Informatik, ist aber auch für Studierende der Mathematik und Physik mit Interesse an diskreten Strukturen eine willkommene ergänzende Einführung in so grundlegende analytische Werkzeuge wie Abschätzung, Approximation und Asymptotik. Besonderheiten der Darstellung sind (a) die Betonung von Ideenbildung und Argumentationshierarchien (von der Graphik zum Beweis), (b) der Einsatz von Computeralgebra-Systemen für rein kalkulatorische Aufgaben, (c) das wiederholte Aufgreifen von Beispielen mit verfeinerten Techniken und veränderten Blickwinkeln und (d) die Motivation anhand von Problemen aus der Informatik. Das Buch wird von einer hyperverlinkten PDF-Version begleitet, die Verweise auf Begriffserklärungen, biografische Daten und weiterführendes Material enthält.
Stop manually analyzing binary! Practical Binary Analysis is the first book of its kind to present advanced binary analysis topics, such as binary instrumentation, dynamic taint analysis, and symbolic execution, in an accessible way. As malware increasingly obfuscates itself and applies anti-analysis techniques to thwart our analysis, we need more sophisticated methods that allow us to raise that dark curtain designed to keep us out--binary analysis can help. The goal of all binary analysis is to determine (and possibly modify) the true properties of binary programs to understand what they really do, rather than what we think they should do. While reverse engineering and disassembly are critical first steps in many forms of binary analysis, there is much more to be learned. This hands-on guide teaches you how to tackle the fascinating but challenging topics of binary analysis and instrumentation and helps you become proficient in an area typically only mastered by a small group of expert hackers. It will take you from basic concepts to state-of-the-art methods as you dig into topics like code injection, disassembly, dynamic taint analysis, and binary instrumentation. Written for security engineers, hackers, and those with a basic working knowledge of C/C++ and x86-64, Practical Binary Analysis will teach you in-depth how binary programs work and help you acquire the tools and techniques needed to gain more control and insight into binary programs. Once you´ve completed an introduction to basic binary formats, you´ll learn how to analyze binaries using techniques like the GNU/Linux binary analysis toolchain, disassembly, and code injection. You´ll then go on to implement profiling tools with Pin and learn how to build your own dynamic taint analysis tools with libdft and symbolic execution tools using Triton. You´ll learn how to: - Parse ELF and PE binaries and build a binary loader with libbfd - Use data-flow analysis techniques like program tracing, slicing, and reaching definitions analysis to reason about runtime flow of your programs - Modify ELF binaries with techniques like parasitic code injection and hex editing - Build custom disassembly tools with Capstone - Use binary instrumentation to circumvent anti-analysis tricks commonly used by malware - Apply taint analysis to detect control hijacking and data leak attacks - Use symbolic execution to build automatic exploitation tools With exercises at the end of each chapter to help solidify your skills, you´ll go from understanding basic assembly to performing some of the most sophisticated binary analysis and instrumentation. Practical Binary Analysis gives you what you need to work effectively with binary programs and transform your knowledge from basic understanding to expert-level proficiency.
Program analysis concerns static techniques for computing reliable approximate information about the dynamic behaviour of programs. Applications include compilers (for code improvement), software validation (for detecting errors in algorithms or breaches of security) and transformations between data representation (for solving problems such as the Y2K problem). This book is unique in giving an overview of the four major approaches to program analysis: data flow analysis, constraint based analysis, abstract interpretation, and type and effect systems. The presentation demonstrates the extensive similarities between the approaches; this will aid the reader in choosing the right approach and in enhancing it with insights from the other approaches. The book covers basic semantic properties as well as more advanced algorithmic techniques. The book is aimed at M.Sc. and Ph.D. students but will be valuable also for experienced researchers and professionals.
Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now - at least in principle - solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of ´´drowning in information, but starving for knowledge´´ the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one´s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.
This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapter topics include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The book is augmented by a wealth of online material.
Marco Russo and Alberto Ferrari walk students step-by-step through creating powerful data models, and then illuminate advanced features such as optimization, deployment, and scalability. Tabular Modeling in Microsoft SQL Server Analysis Services will be indispensable for everyone moving to Analysis Services Tabular, regardless of their previous experience with tabular-style models or with Microsoft´s older Analysis Services offerings. It will also be an essential follow-up for every reader of the authors´ highly-praised Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model.
Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses ´´C++´´ as the programming language.
Mathematik für Informatik und BioInformatik ist eine speziell auf das Informatik- und BioInformatik-Studium zugeschnittene breite Einführung in die Mathematik im Umfang der ersten drei bis vier Semester an Universitäten. Der klassische Stoff von Analysis und Linearer Algebra ist auf das Wesentliche konzentriert. Zusätzlich enthalten sind speziell für Informatik und BioInformatik wichtige Gebiete der Diskreten Mathematik und Logik sowie der Stochastik und teilweise auch der Numerik. Unter der URL min.informatik.uni-tuebingen.de werden begleitend interaktive Übungen und Illustrationen sowie eine Verfilmung der entsprechenden Vorlesung zum Selbststudium angeboten.