New Titles

Building Secure Automotive IoT Applications:

Oka, Dennis Kengo,

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Automating Security Detection Engineering:

Chow, Dennis,

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Cloud Security Handbook:

Estrin, Eyal,

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Adversarial AI Attacks, mitigations, and defense strategies : A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps

Sotiropoulos, John,

Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features â¢ Understand the connection between AI and security by learning about adversarial AI attacks â¢ Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs â¢ Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems â¢ Purchase of the print or Kindle book includes a free PDF eBook Book Description Adversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you'll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you'll be able to develop, deploy, and secure AI systems effectively.What you will learn â¢ Understand poisoning, evasion, and privacy attacks and how to mitigate them â¢ Discover how GANs can be used for attacks and deepfakes â¢ Explore how LLMs change security, prompt injections, and data exposure â¢ Master techniques to poison LLMs with RAG, embeddings, and fine-tuning â¢ Explore supply-chain threats and the challenges of open-access LLMs â¢ Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you'll need a basic understanding of security, ML concepts, and Python.

Date Published:2024

Artificial Intelligence for Cybersecurity:

Kolosnjaji, Bojan,

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Agentic AI for Offensive Cybersecurity:

Yildirim, Orhan.

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C++ in Embedded Systems : A practical transition from C to modern C++

Mahmutbegović, Amar

This practical book provides a detailed explanation of the zero trust security model. Zero trust is a security paradigm shift that eliminates the concept of traditional perimeter-based security and requires you to "always assume breach" and "never trust but always verify." The updated edition offers more scenarios, real-world examples, and in-depth explanations of key concepts to help you fully comprehend the zero trust security architecture. Examine fundamental concepts of zero trust security model, including trust engine, policy engine, and context aware agents Understand how this model embeds security within the system's operation, with guided scenarios at the end of each chapter Migrate from a perimeter-based network to a zero trust network in production Explore case studies that provide insights into organizations' zero trust journeys Learn about the various zero trust architectures, standards, and frameworks developed by NIST, CISA, DoD, and others.

Date Published:2025

Building CLI Applications with C# and .NET : A step-by-step guide to developing cross-platform CLI apps—from coding and testing to deployment

Belmansour, Tidjani,

In the first edition of this critically acclaimed book, Andrew Hoffman defined the three pillars of application security: reconnaissance, offense, and defense. In this revised and updated second edition, he examines dozens of related topics, from the latest types of attacks and mitigations to threat modeling, the secure software development lifecycle (SSDL/SDLC), and more. Hoffman, senior staff security engineer at Ripple, also provides information regarding exploits and mitigations for several additional web application technologies such as GraphQL, cloud-based deployments, content delivery networks (CDN) and server-side rendering (SSR). Following the curriculum from the first book, this second edition is split into three distinct pillars comprising three separate skill sets: Pillar 1: Recon—Learn techniques for mapping and documenting web applications remotely, including procedures for working with web applications Pillar 2: Offense—Explore methods for attacking web applications using a number of highly effective exploits that have been proven by the best hackers in the world. These skills are valuable when used alongside the skills from Pillar 3. Pillar 3: Defense—Build on skills acquired in the first two parts to construct effective and long-lived mitigations for each of the attacks described in Pillar 2.

Date Published:2025

Gain the practical skills, knowledge, and confidence to ace the AWS (SAP-C02) exam on your first attempt : AWS Certified Solutions Architect Professional exam guide (SAP-C02)

Sard, Patrick,

Date Published:2007

.NET MAUI cross-platform application development : build high-performance apps for Android, iOS, macOS, and Windows using XAML and Blazor with .NET 8

Ye, Roger,

.NET MAUI cross-platform application development: build high-performance apps for Android, iOS, macOS, and Windows using XAML and Blazor with .NET 8, second edition -- Contributors -- Table of Contents -- Preface -- Part 1: Exploring .NET MAUI -- 1. Getting Started with .NET MAUI -- 2. Building Our First .NET MAUI App -- 3. User Interface Design with XAML -- 4. Exploring MVVM and Data Binding -- 5. Navigation Using .NET MAUI Shell and NavigationPage -- 6. Software Design with Dependency Injection -- 7. Using Platform-Specific Features -- Part 2: Implementing .NET MAUI Blazor -- 8. Introducing Blazor Hybrid App Development -- 9. Understanding Blazor Routing and Layout -- 10. Implementing Razor Components -- Part 3: Testing and Deployment -- 11. Developing Unit Tests -- 12. Deploying and Publishing in App Stores -- Other Books You May Enjoy -- Index. An evolution of Xamarin.Forms, .NET MAUI is a cross-platform framework for creating native mobile and desktop apps with C# and XAML. Using .NET MAUI, you can develop apps that'll run on Android, iOS, macOS, and Windows from a single shared codebase. In this revised edition of .NET MAUI Cross-Platform Application Development you will be introduced to .NET 8 and get up to speed with app development in no time. The book begins by showing you how to develop a cross-platform application using .NET MAUI, including guidance for migrating from Xamarin.Forms. You'll gain all the knowledge needed to create a cross-platform application for Android, iOS, macOS, and Windows following an example project step by step. As you advance, you'll integrate the latest frontend technology into your app using Blazor components, including the new Blazor Bindings feature. After this, you'll learn how to test and deploy your apps. With new coverage on creating mock .NET MAUI components, you can develop unit tests for your application. You will additionally learn how to perform Razor component testing using bUnit. By the end of this book, you'll have learned how to develop your own cross-platform applications using .NET MAUI.

Date Published:2024

Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models : Data labeling in machine learning with Python

Suda, Vijaya Kumar,

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Build streaming and batch pipelines that process massive amounts of data using Scala : Data enginerring with Scala and Spark

Tome, Eric,

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Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake : Data Engineering with Databricks Cookbook:

Chadha, Pulkit,

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Architect robust and cost-effective data solutions in the cloud era : Data Engineering Best Practices

Schiller, Richard J.,

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A practical guide to analyzing marketing data using Python : Data analytics for marketing

Diaz-Bérrio, Guilherme

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A comprehensive guide to mastering enterprise cyber defense tactics and techniques : Cybersecurity strategies and best practices

Aslaner, Milad,

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An end-to-end guide to implementing and maintaining robust security architecture : Cybersecurity architect's handbook

Nichols, Lester,

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Cybersecurity lessons learned from real-world breaches : Critical Infrastructure Security

Toledano, Soledad Antelada,

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Learn the ins and outs of motion graphics, post-production, rotoscoping, and VFX : Creative motion mastery with Adobe After Effects

Aggarwal, Vishu,

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An engineering leader's guide to security by design : Crafting secure software

Bulmash, Greg,

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