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We’re living in an era where cyber threat intelligence is becoming more important. Cyber threat intelligence routinely informs tactical and strategic decision-making throughout organizational operations. However, finding the right resources on the fundamentals of operationalizing a threat intelligence function can be challenging, and that’s where this book helps.In Operationalizing Threat Intelligence, you’ll explore cyber threat intelligence in five fundamental areas: defining threat intelligence, developing threat intelligence, collecting threat intelligence, enrichment and analysis, and finally production of threat intelligence. You’ll start by finding out what threat intelligence is and where it can be applied. Next, you’ll discover techniques for performing cyber threat intelligence collection and analysis using open source tools. The book also examines commonly used frameworks and policies as well as fundamental operational security concepts. Later, you’ll focus on enriching and analyzing threat intelligence through pivoting and threat hunting. Finally, you’ll examine detailed mechanisms for the production of intelligence.By the end of this book, you’ll be equipped with the right tools and understand what it takes to operationalize your own threat intelligence function, from collection to production.
Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding.If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get.By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.
H2O is an open source, fast, and scalable machine learning framework that allows you to build models using big data and then easily productionalize them in diverse enterprise environments.Machine Learning at Scale with H2O begins with an overview of the challenges faced in building machine learning models on large enterprise systems, and then addresses how H2O helps you to overcome them. You’ll start by exploring H2O’s in-memory distributed architecture and find out how it enables you to build highly accurate and explainable models on massive datasets using your favorite ML algorithms, language, and IDE. You’ll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. You’ll then learn how to easily deploy models with H2O MOJO. Next, the book shows you how H2O Enterprise Steam handles admin configurations and user management, and then helps you to identify different stakeholder perspectives that a data scientist must understand in order to succeed in an enterprise setting. Finally, you’ll be introduced to the H2O AI Cloud platform and explore the entire machine learning life cycle using multiple advanced AI capabilities.By the end of this book, you’ll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs.
Tim Bowler, Sensio (Fabien Potencier), Wojciech Bancer
With its flexible architecture, the Symfony framework allows you to build modern web applications and web services easily and rapidly. The MVC components separate the logic from the user interface and therefore make developing, changing, and testing your applications much faster. Using Symfony you can minimize repetitive coding tasks, optimize performance, and easily integrate with other libraries and frameworks.Although this framework contains with many powerful features, most developers do not exploit Symfony to its full potential.This book makes it easy to get started and produce a powerful and professional-looking web site utilizing the many features of Symfony. Taking you through a real-life application, it covers all major Symfony framework features without pushing you into too much theoretical detail, as well as throwing some light on the best practices for rapid application development.This book takes you through detailed examples as well as covering the foundations that you will need to get the most out of the Symfony framework. You will learn to shorten the development time of your complex applications and maintain them with ease. You will create several useful plug-ins and add them to your application and automate common tasks. The book also covers best practices and discussions on security and optimization. You will learn to utilize all major features of this framework by implementing them in your application.By the end, you should have a good understanding of the development features of Symfony (for Propel as well as Doctrine editions), and be able to deploy a high-performance web site quite easily.
Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Rithin Skaria, Jatinder Pal Singh
It’s easy to learn and deploy resources in Microsoft Azure, without worrying about resource optimization. However, for production or mission critical workloads, it’s crucial that you follow best practices for resource deployment to attain security, reliability, operational excellence and performance. Apart from these aspects, you need to account for cost considerations, as it’s the leading reason for almost every organization’s cloud transformation.In this book, you’ll learn to leverage Microsoft Well-Architected Framework to optimize your workloads in Azure. This Framework is a set of recommended practices developed by Microsoft based on five aligned pillars; cost optimization, performance, reliability, operational excellence, and security. You’ll explore each of these pillars and discover how to perform an assessment to determine the quality of your existing workloads. Through the book, you’ll uncover different design patterns and procedures related to each of the Well-Architected Framework pillars.By the end of this book, you’ll be well-equipped to collect and assess data from an Azure environment and perform the necessary upturn of your Azure workloads.