Название: Reversing Data Structures and Algorithms in Malware
Автор: Jason Reaves
Издательство: Leanpub
Год: 2020-05-13
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB

Book walks through a number of real world examples for reverse engineering data structures and algorithms found in malware. Walk through a number of real world examples for reverse engineering data structures and algorithms found in malware in order to gradually introduce the reader to more advanced examples culminating in reverse engineering a C2 (Command and Control) protocol. There are many obstacles you encounter when doing malware analysis, from unpacking your first sample, mapping out your first routine or breaking into that malwares data encoding routine. One obstacle in particular I’ve seen give people problems more than others is being able to follow malware as it parses data, data that is sometimes seemingly random hex but instead used for configuration purposes that can depict how malware acts. One experience that seems to help people when reverse engineering these structures and the algorithms that process them is a past in low level development such as assembly or C programming, however this isn’t a luxury that everyone can come to malware analysis with such a background.

Название: Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions
Автор: Warren B. Powell
Издательство: Wiley
Год: 2022
Страниц: 1133
Язык: английский
Формат: pdf (true)
Размер: 31.4 MB

Clearing the jungle of stochastic optimization. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Each chapter illustrates how to model and solve a specific decision problem. These have been designed to bring out the features of different classes of policies. There are Python modules that go with most of these exercises that provide an opportunity to do computational work. These exercises will generally require that the reader use the Python module as a start, but where additional programming is required.

Название: Machine Learning for Business Analytics: Real-Time Data Analysis for Decision-Making
Автор: Hemachandran K., Sayantan Khanra, Raul V. Rodriguez, Juan R. Jaramillo
Издательство: Routledge
Год: 2023
Страниц: 191
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.2 MB

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The Machine Learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. Commonly used ML algorithms are given specific attention. The algorithms studied include linear regression, logistic regression, Naive Bayes, kNN, random forest, and others. The basic three types of ML algorithms are as follows: 1) Supervised Learning; 2) Unsupervised Learning; 3) Reinforcement Learning.

Название: Artificial Intelligence and Machine Learning in Smart City Planning
Автор: Vedik Basetti, Chandan Kumar Shiva
Издательство: Elsevier
Год: 2023
Страниц: 326
Язык: английский
Формат: pdf
Размер: 16.3 MB

Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques. Human beings are the smart and advanced species on this planet because they can think, evaluate, and solve complicated issues. On the other hand, Artificial Intelligence is in the initial stage when compared to human intelligence in many aspects. Then the purpose of Machine Learning is to take decisions based on the data available with efficiency. Research has been going on in technologies like Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning to solve real-world complex issues.

Название: The Decision Intelligence Handbook: Practical Steps for Evidence-Based Decisions in a Complex World (Second Early Release)
Автор: L.Y. Pratt, N.E. Malcolm
Издательство: O’Reilly Media, Inc.
Год: 2023-02-17
Страниц: 181
Язык: английский
Формат: epub (true), mobi
Размер: 24.0 MB

Decision Intelligence (DI) has been widely named as a top technology trend for several years, and the Gartner Group reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of Artificial Intelligence (AI). Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies. Until now, there has been little practical guidance for organizations to formalize decision-making and integrate their decisions with data. Decision Intelligence (DI) is a methodology and set of processes and technologies for making better, more evidence-based decisions by helping decision makers understand how the actions they take today can affect their desired outcomes in the future. The key concept of DI is the idea that you can design decisions. Importantly, a decision, as we treat it here, is a thought process about an action that, once made, leads to specific outcomes without any further intervention. Under the hood, DI is all about integration and connecting human decision-makers with data, AI, complex systems modeling, human behavior, and many other disciplines.

Название: Advanced Testing of Systems-of-Systems, Volume 1: Theoretical Aspects
Автор: Bernard Homes
Издательство: Wiley-ISTE
Год: 2022
Страниц: 308
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

The breadth of the subject justifies splitting this work in two books. Part I, this book, covers the general aspects applicable to systems-of-systems testing, among them the impact of development life cycle, test strategy and methodology, the added value of quality referential, test documentation and reporting. We identified the impact of various test levels and test techniques, whether static or dynamic. In the second book, we will focus on project management, identifying human interactions as primary elements to consider, and we will continue with practical aspects such as testing processes and their iterative and continuous improvement. We will also see additional but necessary processes, such as requirement management, defects management and configuration management. The main aspect put forward is the difference between the traditional vision of software testing – focused on one system and one version – and the necessary vision when multiple systems and multiple versions of software must be interconnected to provide a service that needs to be tested thoroughly.

Название: Advanced Testing of Systems-of-Systems, Volume 2: Practical Aspects
Автор: Bernard Homes
Издательство: Wiley-ISTE
Год: 2022
Страниц: 306
Язык: английский
Формат: pdf (true), epub (true)
Размер: 11.3 MB

In this second part of the book on systems-of-systems testing, we will focus on more practical aspects such as managing test projects, testing processes and how to improve them continuously. We will see the additional but necessary processes such as the management of requirements, defects and configurations, and we will also see a case study allowing us to ask ourselves several useful questions. We will end with a perilous prediction exercise by listing the challenges that tests will have to face in the years to come. In DevOps environments, it is recommended to enable automatic creation of environments to test builds as they are created by developers. As proposed by Kim et al., it is necessary to allow to recreate – automatically – the test environments rather than trying to repair them. This automatic creation solution ensures an identical test environment to the previous version, which will facilitate regression testing.

Название: Optimization and Machine Learning: Optimization for Machine Learning and Machine Learning for Optimization
Автор: Rachid Chelouah, Patrick Siarry
Издательство: Wiley
Год: 2022
Страниц: 255
Язык: английский
Формат: pdf (true), epub (true)
Размер: 18.1 MB

Machine Learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and Machine Learning, and to demonstrate how to apply them in the fields of engineering. The fields of Machine Learning and optimization are highly interwoven. Optimization problems form the core of Machine Learning methods and modern optimization algorithms are using Machine Learning more and more to improve their efficiency. Machine Learning has applications in all areas of science. There are many learning methods, each of which uses a different algorithmic structure to optimize predictions, based on the data received. Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on Machine Learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on Machine Learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.

Название: Uncertainty Quantification using R
Автор: Eduardo Souza de Cursi
Издательство: Springer
Год: 2023
Страниц: 767
Язык: английский
Формат: pdf
Размер: 33.9 MB

This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning. R programming is analogous to programming in usual programming languages and interpreters.

Название: Developing Web Components with Svelte: Building a Library of Reusable UI Components
Автор: Alex Libby
Издательство: Apress
Год: 2023
Страниц: 350
Язык: английский
Формат: pdf, epub
Размер: 10.2 MB

Leverage the power of Svelte to quickly produce the foundations of a framework-agnostic component library that can extend and develop over time. This is a project-oriented book that simplifies setting up the skeleton of the library and adding components, using little more than a text editor or free software. You'll gain a starting baseline that can be used to develop future projects or incorporated into an existing workflow. You'll take development to the next level and can use this knowledge to create components with any framework, such as React, Angular or Vue.js, not just Svelte. Developing Web Components with Svelte is an excellent resource for getting acquainted with creating and maintaining a component library using a phased approach. It takes the view that you don't have to create something complex and unwieldy. Instead, you can start building something quickly, then extend it as needs dictate over time, without sacrificing speed or features.