Master the fundamentals of malware analysis for the Windows platform and enhance your anti-malware skill set About This Book Set the baseline towards performing malware analysis on the Windows platform and how to use the tools required to deal with malware Understand how to decipher x86 assembly code from source code inside your favourite development environment A step-by-step based guide that reveals malware analysis from an industry insider and demystifies the process Who This Book Is For This book is best for someone who has prior experience with reverse engineering Windows executables and wants to specialize in malware analysis. The book presents the malware analysis thought process using a show-and-tell approach, and the examples included will give any analyst confidence in how to approach this task on their own the next time around. What You Will Learn Use the positional number system for clear conception of Boolean algebra, that applies to malware research purposes Get introduced to static and dynamic analysis methodologies and build your own malware lab Analyse destructive malware samples from the real world (ITW) from fingerprinting and static/dynamic analysis to the final debrief Understand different modes of linking and how to compile your own libraries from assembly code and integrate the codein your final program Get to know about the various emulators, debuggers and their features, and sandboxes and set them up effectively depending on the required scenario Deal with other malware vectors such as pdf and MS-Office based malware as well as *s and shellcode In Detail Windows OS is the most used operating system in the world and hence is targeted by malware writers. There are strong ramifications if things go awry. Things will go wrong if they can, and hence we see a salvo of attacks that have continued to disrupt the normal scheme of things in our day to day lives. This book will guide you on how to use essential tools such as debuggers, disassemblers, and sandboxes to dissect malware samples. It will expose your innards and then build a report of their indicators of compromise along with detection rule sets that will enable you to help contain the outbreak when faced with such a situation. We will start with the basics of computing fundamentals such as number systems and Boolean algebra. Further, you'll learn about x86 assembly programming and its integration with high level languages such as C++.You'll understand how to decipher disassembly code obtained from the compiled source code and map it back to its original design goals. By delving into end to end analysis with real-world malware samples to solidify your understanding, you'll sharpen your technique of handling destructive malware binaries and vector mechanisms. You will also be encouraged to consider analysis lab safety measures so that there is no infection in the process. Finally, we'll have a rounded tour of various emulations, sandboxing, and debugging options so that you know what is at your disposal when you need a specific kind of weapon in order to nullify the malware. Style and approach An easy to follow, hands-on guide with de*ions and screenshots that will help you execute effective malicious software investigations and conjure up solutions creatively and confidently.

Statistics, big data, and machine learning for Clojure programmers About This Book Write code using Clojure to harness the power of your data Discover the libraries and frameworks that will help you succeed A practical guide to understanding how the Clojure programming language can be used to derive insights from data Who This Book Is For This book is aimed at developers who are already productive in Clojure but who are overwhelmed by the breadth and depth of understanding required to be effective in the field of data science. Whether you’re tasked with delivering a specific analytics project or simply suspect that you could be deriving more value from your data, this book will inspire you with the opportunities–and inform you of the risks–that exist in data of all shapes and sizes. What You Will Learn Perform hypothesis testing and understand feature selection and statistical significance to interpret your results with confidence Implement the core machine learning techniques of regression, classification, clustering and recommendation Understand the importance of the value of simple statistics and distributions in exploratory data analysis Scale algorithms to web-sized datasets efficiently using distributed programming models on Hadoop and Spark Apply suitable analytic approaches for text, graph, and time series data Interpret the terminology that you will encounter in technical papers Import libraries from other JVM languages such as Java and Scala Communicate your findings clearly and convincingly to nontechnical colleagues In Detail The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a *ing language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future. Style and approach This is a practical guide to data science that teaches theory by example through the libraries and frameworks accessible from the Clojure programming language.

Use PhoneGap to build cross-platform mobile applications quickly and efficiently About This Book Build native mobile phone applications with HTML5, JavaScript, and CSS Incorporate smartphone capabilities such as GPS, camera, accelerometer, and more into your apps for any mobile platform Use Cordova view to embed PhoneGap into native applications to either transit smoothly to PhoneGap or incorporate PhoneGap functionalities Who This Book Is For If you are a mobile application developer in iOS or Android, or a web application developer who wants to learn how to make cross-platform mobile applications using PhoneGap, this book is perfect for you. To make the most of this book, it will be helpful if you have prior knowledge of HTML5, CSS, and JavaScript. What You Will Learn Get to grips with the fundamentals of PhoneGap to get started Set up a development environment for Linux, Mac OS, and Windows Use Cordova CLI, workflows, and Plugman Plugin manager to create mobile applications efficiently Understand the development workflow to create native cross-platform mobile applications Embed plugin support to transition to PhoneGap or use it to enhance existing applications Improve your mobile development knowledge using object-oriented programming (OOP), reusable components, and AJAX closures Be empowered to build your own mobile apps quickly with ease Discover tips and tricks to make app development fun and easy In Detail PhoneGap is an open source framework that allows you to quickly build cross-platform mobile apps using HTML5, JavaScript, and CSS. PhoneGap Build is a cloud service that allows you to quickly develop and compile mobile applications without SDKs, compilers, and hardware. PhoneGap allows you to use its existing plugins or create new ones, as per your requirements, to enhance your mobile applications. Starting by installing PhoneGap, you’ll develop an app that uses various device capabilities through different plugins and learn how to build an app in the cloud with PhoneGap’s Build service. You’ll discover how to use PhoneGap to create an application view, along with how to use a camera, geolocation, and other device capabilities to create engaging apps. Next, you’ll augment applications with PhoneGap's plugins using minimalistic code. You’ll explore the app preparation process to deploy your app to the app store. By the end of the book, you’ll have also learned how to apply hybrid mobile UIs that will work across different platforms and different screen sizes for better user experience. Style and approach This is an example-based, fast-paced guide that covers the fundamentals of creating cross-platform mobile applications with PhoneGap.

Over 100 recipes to help you develop cross-platform, native applications in JavaScript About This Book Leverage your JavaScript skills to write mobile applications using Titanium Studio tools with the native advantage Deploy your application on the App Store and Google Play Add your own IOS native modules in objective-C, in an easy-to-follow step-by-step format Who This Book Is For This book is an essential for any developer learning or using JavaScript who wants to write native UI applications for iOS and Android. No knowledge of Objective-C, Swift and Java is required and you’ll quickly be developing native, cross-platform apps, in JavaScript! What You Will Learn Transfer data between applications with URL schemes, and make your application accessible to other mobile applications and services Connect with remote services using JSON Work with Google Maps and Apple Maps, GPS and annotate routes Create animations and special effects Integrate notifications and connect with social media services such as Facebook and Twitter Build applications with Alloy MVC – a rapid application development framework Design native APIs and use local databases In Detail The mobile web has paved the way but many users want to have “native” applications installed. Using Appcelerator as a platform it’s now possible to write iOS, Android, and Windows phone applications in JavaScript! It allows developers to develop fully native UI applications using Appcelerator studio tools without any knowledge of Objective-C, Swift or Java. This book will take you through the process of building cross-platform, native UI applications for the mobile from scratch. You will learn how to develop apps, how to use GPS, cameras and photos and how to build socially connected apps. You will also learn how to package them for submission to the App Store and Google Play. This cookbook takes a pragmatic approach to creating applications in JavaScript from putting together basic UIs, to handling events and implementation of third party services such as Twitter, Facebook and Push notifications. The book shows you how to integrate datasources and server APIs, and how to use local databases. The topics covered will guide you to use Appcelerator Studio tools for all the mobile features such as Geolocation, Accelerometer, animation and more. You’ll also learn about Alloy, the Appcelerator MVC framework for rapid app development, and how to transfer data between applications using URLSchemes, enabling other developers to access and launch specific parts of your app. Finally, you will learn how to register developer accounts and publish your very own applications on the App Store and Google Play. Style and approach This book offers a set of practical recipes with a step-by-step approach for building native applications for both the iOS and Android using JavaScript. This hands-on guide shows you exactly how to use the Appcelerator platform to rapidly develop cross-platform, native apps.

Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book Build smart extensions to pre-existing features at work that can help maximize their value Quantify your models to drive real improvement Take your knowledge of basic concepts, such as linear regression and Na?ve Bayes classification, to the next level and productionalize their models Play what-if games with your models and techniques by following the test-driven exploration process Who This Book Is For This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. You may be starting, or have already started, a machine learning project at work and are looking for a way to deliver results quickly to enable rapid iteration and improvement. Those looking for examples of how to isolate issues in models and improve them will find ideas in this book to move forward. What You Will Learn Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty Generate complex and simple random data to create a wide variety of test cases that can be codified into tests Develop models iteratively, even when using a third-party library Quantify model quality to enable collaboration and rapid iteration Adopt simpler approaches to common machine learning algorithms Take behaviour-driven development principles to articulate test intent In Detail Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine learning is applicable to a lot of what you do every day. As a result, you can’t take forever to deliver your first iteration of software. Learning to build machine learning algorithms within a controlled test framework will speed up your time to deliver, quantify quality expectations with your clients, and enable rapid iteration and collaboration. This book will show you how to quantifiably test machine learning algorithms. The very different, foundational approach of this book starts every example algorithm with the simplest thing that could possibly work. With this approach, seasoned veterans will find simpler approaches to beginning a machine learning algorithm. You will learn how to iterate on these algorithms to enable rapid delivery and improve performance expectations. The book begins with an introduction to test driving machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression. You will discover how to test different approaches to na?ve bayes and compare them quantitatively, along with how to apply OOP (Object-Oriented Programming) and OOP patterns to test-driven code, leveraging SciKit-Learn. Finally, you will walk through the development of an algorithm which maximizes the expected value of profit for a marketing campaign by combining one of the classifiers covered with the multiple regression example in the book. Style and approach An example-driven guide that builds a deeper knowledge and understanding of iterative machine learning development, test by test. Each topic develops solutions using failing tests to illustrate problems; these are followed by steps to pass the tests, simply and straightforwardly. Topics which use generated data explore how the data was generated, alongside explanations of the assumptions behind different machine learning techniques.

Harness the power of Redis to integrate and manage your projects efficiently About This Book Learn how to use Redis's data types efficiently to manage large data sets Scale Redis to multiple servers with Twemproxy, Redis Sentinel, and Redis Cluster A fast-paced guide, full of real-world examples to help you get the best out of the features offered by Redis Who This Book Is For If you are a competent developer with experience of working with data structure servers and want to boost your project's performance by learning about features of Redis, then this book is for you. What You Will Learn Build analytics applications using Bitmaps and Hyperloglogs Enhance scalability with Twemproxy, Redis Sentinel, and Redis Cluster Build a Time Series implementation in Node.js and Redis Create your own Redis commands by extending Redis with Lua Get to know security techniques to protect your data (SSL encryption, firewall rules, basic authorization) Persist data to disk and learn the trade-offs of AOF and RDB Understand how to use Node.js, PHP, Python, and Ruby clients for Redis Avoid common pitfalls when designing your next solution In Detail Redis is the most popular in-memory key-value data store. It’s very lightweight and its data types give it an edge over the other competitors. If you need an in-memory database or a high-performance cache system that is simple to use and highly scalable, Redis is what you need. Redis Essentials is a fast-paced guide that teaches the fundamentals on data types, explains how to manage data through commands, and shares experiences from big players in the industry. We start off by explaining the basics of Redis followed by the various data types such as Strings, hashes, lists, and more. Next, Common pitfalls for various scenarios are described, followed by solutions to ensure you do not fall into common traps. After this, major differences between client implementations in PHP, Python, and Ruby are presented. Next, you will learn how to extend Redis with Lua, get to know security techniques such as basic authorization, firewall rules, and SSL encryption, and discover how to use Twemproxy, Redis Sentinel, and Redis Cluster to scale infrastructures horizontally. At the end of this book, you will be able to utilize all the essential features of Redis to optimize your project's performance. Style and approach A practical guide that offers the foundation upon which you can begin to understand the capabilities of Redis using a step-by-step approach. This book is full of real-world problems and in-depth knowledge of the concepts and features of Redis, with plenty of examples.

Build, process and analyze large-scale graph data effectively with Spark About This Book Find solutions for every stage of data processing from loading and transforming graph data to Improve the scalability of your graphs with a variety of real-world applications with complete Scala code. A concise guide to processing large-scale networks with Apache Spark. Who This Book Is For This book is for data scientists and big data developers who want to learn the processing and analyzing graph datasets at scale. Basic programming experience with Scala is assumed. Basic knowledge of Spark is assumed. What You Will Learn Write, build and deploy Spark applications with the Scala Build Tool. Build and analyze large-scale network datasets Analyze and transform graphs using RDD and graph-specific operations Implement new custom graph operations tailored to specific needs. Develop iterative and efficient graph algorithms using message aggregation and Pregel abstraction Extract subgraphs and use it to discover common clusters Analyze graph data and solve various data science problems using real-world datasets. In Detail Apache Spark is the next standard of open-source cluster-computing engine for processing big data. Many practical computing problems concern large graphs, like the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. Apache Spark GraphX API combines the advantages of both data-parallel and graph-parallel systems by efficiently expressing graph computation within the Spark data-parallel framework. This book will teach the user to do graphical programming in Apache Spark, apart from an explanation of the entire process of graphical data analysis. You will journey through the creation of graphs, its uses, its exploration and analysis and finally will also cover the conversion of graph elements into graph structures. This book begins with an introduction of the Spark system, its libraries and the Scala Build Tool. Using a hands-on approach, this book will quickly teach you how to install and leverage Spark interactively on the command line and in a standalone Scala program. Then, it presents all the methods for building Spark graphs using illustrative network datasets. Next, it will walk you through the process of exploring, visualizing and analyzing different network characteristics. This book will also teach you how to transform raw datasets into a usable form. In addition, you will learn powerful operations that can be used to transform graph elements and graph structures. Furthermore, this book also teaches how to create custom graph operations that are tailored for specific needs with efficiency in mind. The later chapters of this book cover more advanced topics such as clustering graphs, implementing graph-parallel iterative algorithms and learning methods from graph data. Style and approach A step-by-step guide that will walk you through the key ideas and techniques for processing big graph data at scale, with practical examples that will ensure an overall understanding of the concepts of Spark.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python’s most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.

Over 90 hands-on recipes to design Internet scalable web and mobile applications with Amazon DynamoDB About This Book Construct top-notch mobile and web applications with the Internet scalable NoSQL database and host it on cloud Integrate your applications with other AWS services like AWS EMR, AWS S3, AWS Redshift, and AWS CloudSearch etc. in order to achieve a one-stop application stack Step-by-step implementation guide that provides real-world use with hands-on recipes Who This Book Is For This book is intended for those who have a basic understanding of AWS services and want to take their knowledge to the next level by getting their hands dirty with coding recipes in DynamoDB. What You Will Learn Design DynamoDB tables to achieve high read and write throughput Discover best practices like caching, exponential back-offs and auto-retries, storing large items in AWS S3, storing compressed data etc. Effectively use DynamoDB Local in order to make your development smooth and cost effective Implement cost effective best practices to reduce the burden of DynamoDB charges Create and maintain secondary indexes to support improved data access Integrate various other AWS services like AWS EMR, AWS CloudSearch, AWS Pipeline etc. with DynamoDB In Detail AWS DynamoDB is an excellent example of a production-ready NoSQL database. In recent years, DynamoDB has been able to attract many customers because of its features like high-availability, reliability and infinite scalability. DynamoDB can be easily integrated with massive data crunching tools like Hadoop /EMR, which is an essential part of this data-driven world and hence it is widely accepted. The cost and time-efficient design makes DynamoDB stand out amongst its peers. The design of DynamoDB is so neat and clean that it has inspired many NoSQL databases to simply follow it. This book will get your hands on some engineering best practices DynamoDB engineers use, which can be used in your day-to-day life to build robust and scalable applications. You will start by operating with DynamoDB tables and learn to manipulate items and manage indexes. You will also discover how to easily integrate applications with other AWS services like EMR, S3, CloudSearch, RedShift etc. A couple of chapters talk in detail about how to use DynamoDB as a backend database and hosting it on AWS ElasticBean. This book will also focus on security measures of DynamoDB as well by providing techniques on data encryption, masking etc. By the end of the book you’ll be adroit in designing web and mobile applications using DynamoDB and host it on cloud. Style and approach An easy-to-follow guide, full of real-world examples, which takes you through the world of DynamoDB following a step-by-step, problem-solution based approach.

If you are a hobbyist who wants to develop projects based on Arduino as the main microcontroller platform or an engineer interested in finding out what the Arduino platform offers, then this book is ideal for you. Some prior knowledge of the C programming language is required.

This book is aimed at those familiar with the basics of Blender, looking to delve into the depths of the Cycles rendering engine to create an array of breath-taking materials and textures.

This book is aimed at developers and technical testers who are looking for a quick way to take their SoapUI skills and understanding to the next level. Even if you are new to SoapUI but have basic Java skills and a reasonable grasp of RESTFul and Soap web services, then you should have no problem making use of this book.

If you have already undertaken some simple projects with the Raspberry Pi and are looking to enter the exciting work of hardware interaction, then this book is ideal for you.

If you are a developer who wants to migrate from Selenium RC or any other automation tool to Selenium WebDriver, then this book is for you. Knowledge of automation tools is necessary to follow the examples in this book.

If you are a security professional whose workload is increasing, or a Puppet professional looking to increase your knowledge of security, or even an experienced systems administrator, then this book is for you. This book will take you to the next level of security automation using Puppet. The book requires no prior knowledge of Puppet to get started.

This book is for anyone who wants to learn Intel Galileo for home automation and cross-platform software development. No knowledge of programming with Intel Galileo is assumed, but knowledge of the C programming language is essential.

If you are an Android developer looking to test your applications or optimize your application development process, then this book is for you. No previous experience in application testing is required.

This book is ideal for developers who have experience in developing websites or possess minor knowledge of how responsive websites work. No experience of high-level website development or performance tweaking is required.

Are you writing two websites: one for mobile and one for larger displaysOr perhaps you've already implemented your first RWD but are struggling to bring it all togetherIf so, this book gives you everything you need to take your websites to the next level. Some HTML and CSS knowledge will help; everything else you need is included in the book.

If you are a C# developer who wants to develop Android apps and enhance your existing skill set, then this book is ideal for you. Good working knowledge of C#, .NET, and object-oriented software development is assumed.