Wydawca: K-i-s-publishing
Threat Modeling Gameplay with EoP. A reference manual for spotting threats in software architecture
Brett Crawley, Adam Shostack
Are you looking to navigate security risks, but want to make your learning experience fun? Here's a comprehensive guide that introduces the concept of play to protect, helping you discover the threats that could affect your software design via gameplay.Each chapter in this book covers a suit in the Elevation of Privilege (EoP) card deck (a threat category), providing example threats, references, and suggested mitigations for each card. You’ll explore the methodology for threat modeling—Spoofing, Tampering, Repudiation, Information Disclosure, and Elevation of Privilege (S.T.R.I.D.E.) with Privacy deck and the T.R.I.M. extension pack. T.R.I.M. is a framework for privacy that stands for Transfer, Retention/Removal, Inference, and Minimization. Throughout the book, you’ll learn the meanings of these terms and how they should be applied. From spotting vulnerabilities to implementing practical solutions, the chapters provide actionable strategies for fortifying the security of software systems.By the end of this book, you will be able to recognize threats, understand privacy regulations, access references for further exploration, and get familiarized with techniques to protect against these threats and minimize risks.
Gema Socorro Rodríguez
With resources on Android and Kotlin abound, it’s difficult to find content that focuses on resolving common challenges faced by app developers. This book by Gema Socorro Rodríguez – a Google Developer Expert for Android with over 15 years of experience and a proven track record as an effective instructor – is designed to bridge the gap between theory and real-world application. It equips you with the skills to tackle everyday problems in Android development through hands-on projects.Under Gema's expert guidance, you’ll build three sophisticated Android projects. You'll start your development journey by building a WhatsApp-like application, learning how to process asynchronous messages reactively, render them using Jetpack Compose, and advance to creating and uploading a backup of these messages. Next, you’ll channel your creativity into Packtagram, an Instagram-inspired app that offers advanced photo-editing capabilities using the latest CameraX libraries. Your final project will be a Netflix-style app, integrating video playback functionality with ExoPlayer for both foreground and background operations, and implementing device casting features.By the end of this book, you'll have crafted three fully functional, multi-platform projects and gained the confidence to solve the most common challenges in Android development.
Andrew Berridge, Michael Phillips
The need for agile business intelligence (BI) is growing daily, and TIBCO Spotfire® combines self-service features with essential enterprise governance and scaling capabilities to provide best-practice analytics solutions. Spotfire is easy and intuitive to use and is a rewarding environment for all BI users and analytics developers.Starting with data and visualization concepts, this book takes you on a journey through increasingly advanced topics to help you work toward becoming a professional analytics solution provider. Examples of analyzing real-world data are used to illustrate how to work with Spotfire. Once you've covered the AI-driven recommendations engine, you'll move on to understanding Spotfire's rich suite of visualizations and when, why and how you should use each of them. In later chapters, you'll work with location analytics, advanced analytics using TIBCO Enterprise Runtime for R®, how to decide whether to use in-database or in-memory analytics, and how to work with streaming (live) data in Spotfire. You'll also explore key product integrations that significantly enhance Spotfire's capabilities.This book will enable you to exploit the advantages of the Spotfire serve topology and learn how to make practical use of scheduling and routing rules.By the end of this book, you will have learned how to build and use powerful analytics dashboards and applications, perform spatial analytics, and be able to administer your Spotfire environment efficiently
Michaël Hoarau
Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes.The book begins with Amazon Forecast, where you’ll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You’ll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you’ll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data.By the end of this AWS book, you’ll have understood how to use the three AWS AI services effectively to perform time series analysis.
Tarek A. Atwan
Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch.Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.
Tarek A. Atwan
To use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.