Blockchain - Pioneering the Web3 Infrastructure for an Intelligent Future
Cloud Native Data Security with Oauth
With the growth of cloud native applications, developers increasingly rely on APIs to make everything work. But security often lags behind, making APIs an attractive target for bad actors looking to access valuable business data. OAuth is a popular way to address this issue, but this open standard doesn't provide sufficient guidelines for using API tokens to protect business data. That alone can lead to vulnerabilities and invite data breaches. By using cloud native components in Kubernetes or similar platforms, organizations can implement a scalable, future-proof security architecture for their systems that follows a zero-trust approach to protect business data. You'll access tokens, claims, and token design with an emphasis on an API-first approach. This book takes readers through an end-to-end security architecture that scales to many components in a cloud native environment, while only requiring simple security code in applications and APIs. You'll learn: Why user identity must be part of your cloud native security stack How to integrate user identity into APIs How to externalize security, secure data access, and authenticate clients using OAuth Methods for running security components in a Kubernetes cluster How to use claims to protect business data in APIs How to follow security best practices for client applications and APIs
Machine Learning and AI with Simple Python and Matlab Scripts
A practical guide to AI applications for Simple Python and Matlab scripts Machine Learning and AI with Simple Python and Matlab Scripts introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular topics in engineering and science. Step-by-step instructions for simple Python and Matlab scripts mimicking real-life applications will enter the readers into the magical world of AI, without requiring them to have advanced math and computational skills. The book is supported by instructor only lecture slides and sample exams with multiple-choice questions. Machine Learning and AI with Simple Python and Matlab Scripts includes information on: Artificial neural networks applied to real-world problems such as algorithmic trading of financial assets, Alzheimer's disease prognosis Convolution neural networks for speech recognition and optical character recognition Recurrent neural networks for chatbots and natural language translators Typical AI tasks including flight control for autonomous drones, dietary menu planning, and route planning Advanced AI tasks including particle swarm optimization and differential and grammatical evolution as well as the current state of the art in AI tools Machine Learning and AI with Simple Python and Matlab Scripts is an accessible, thorough, and practical learning resource for undergraduate and graduate students in engineering and science programs along with professionals in related industries seeking to expand their skill sets.
Intelligent Cybersecurity and Resilience for Critical Industries
Intelligent Cybersecurity and Resilience for Critical Industries: Challenges and Applications thoroughly explores cybersecurity principles, strategies, and technologies crucial for protecting digital assets and combating evolving cyber threats in critical industries. This book provides indispensable guidance in fortifying cyber defenses for critical infrastructures. Each chapter offers invaluable insights into proactive defense measures, from AI-driven threat management in healthcare systems to practical applications of AI for cyber risk management in critical infrastructures. Unraveling the complexities of contemporary cyber threats, this book empowers readers with the knowledge and tools needed to navigate the intricate landscape of cybersecurity effectively. Through a multidisciplinary approach spanning AI, machine learning, and advanced technologies, it addresses the urgent challenges organizations encounter in securing their digital infrastructure and safeguarding sensitive data from malicious cyber-attacks.Technical topics discussed in the book include: AI-driven strategies for advanced malware detection and prevention Hybrid deep learning techniques for precise malware classification Machine learning applications tailored to IoT security challenges Comprehensive exploration of blockchain techniques enhancing IoT security and privacy Practical integration of security analysis modules for proactive threat intelligence. Designed as an essential reference, this book caters to students, researchers, cybersecurity professionals, and individuals keen on comprehending and tackling contemporary cyber defense and risk assessment challenges. It serves as a valuable resource for enhancing cybersecurity awareness, knowledge, and practical skills in critical industries.
Optimal Spending on Cybersecurity Measures
The aim of this book is to demonstrate the use of business-driven risk assessments within the privacy impact assessment process to meet privacy laws requirements. This book introduces the cyber risk investment model, and the cybersecurity risk management framework used within business-driven risk assessments to meet the intent of Privacy and Data Protection Laws. This can be used by various stakeholders who are involved in the implementation of cybersecurity measures to safeguard sensitive data. This framework facilitates an organization's risk management decision-making process to demonstrate the mechanisms in place to fund cybersecurity measures to meet Privacy Laws and demonstrates the application of the process showcasing six case studies. This book also discusses the elements used within the cybersecurity risk management process and defines a strategic approach to minimize cybersecurity risks.Features: - Aims to strengthen the reader's understanding of industry governance, risk and compliance practices.- Incorporates an innovative approach to assess business risk management.- Explores the strategic decisions made by organizations when implementing cybersecurity measures and leverages an integrated approach to include risk management elements.
Financial Cryptography and Data Security
The two-volume set LNCS 14744 + 14745 constitutes the proceedings of the 28th International Conference on Financial Cryptography and Data Security, FC 2024, which took place in Willemstad, Cura癟ao, during March 4-8, 2024. The number of 36 full and 6 short papers included in the proceedings were carefully reviewed and selected from 199 submissions. They were organized in topical sections as follows: Part I: Consensus; AMMs; fees and rewards; hardware attacks; Part II: Feeling Optimistic; randomness and time; signatures; applied cryptography; PETS; designing for the real world.
Large Language Models for Developers
This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture's attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance.FEATURES- Covers the full lifecycle of working with LLMs, from model selection to deployment- Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization- Teaches readers to enhance model efficiency with advanced optimization techniques- Includes companion files with code and images -- available from the publisher
AI Revealed
This book is a guide to navigating the evolving landscape of artificial intelligence. Designed for both novices and seasoned professionals it covers a broad range of topics from fundamental ideas to innovative advancements. Readers will investigate the principles of machine learning, explore the intricacies of deep learning architectures, and discover the applications of natural language processing and computer vision. With its concise explanations and useful examples, it gives readers the knowledge and abilities they need to confidently traverse the changing field of artificial intelligence. This text also looks at real-world case studies and important ethical issues, providing insightful information about the ethical ramifications and societal effects of technology. Features: Practical applications and case studies with a section on use cases across various industries, including healthcare, finance, transportation, and retail. Actionable steps for getting started with AI include how to set up an AI development environment, learning Python for AI applications, and utilizing popular AI libraries. Resources for further study including, AI online courses, AI communities and forums, and recommended books essentially, a roadmap for continuous learning.
Large Language Models
This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher.
Financial Cryptography and Data Security
The two-volume set LNCS 14744 + 14745 constitutes the proceedings of the 28th International Conference on Financial Cryptography and Data Security, FC 2024, which took place in Willemstad, Cura癟ao, during March 4-8, 2024. The number of 36 full and 6 short papers included in the proceedings were carefully reviewed and selected from 199 submissions. They were organized in topical sections as follows: Part I: Consensus; AMMs; fees and rewards; hardware attacks; Part II: Feeling Optimistic; randomness and time; signatures; applied cryptography; PETS; designing for the real world.
