Triton Github
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Open Source Systems: Enterprise Software and Solutions
This book constitutes the refereed proceedings of the 14th IFIP WG 2.13 International Conference on Open Source Systems, OSS 2018, held in Athens, Greece, in June 2018. The 14 revised full papers and 2 short papers presented were carefully reviewed and selected from 38 submissions. The papers cover a wide range of topics in the field of free/libre open source software (FLOSS) and are organized in the following thematic sections: organizational aspects of OSS projects, OSS projects validity, mining OSS data, OSS in public administration, OSS governance, and OSS reusability.
Designing Deep Learning Systems
A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting—and lucrative—career as a deep learning engineer. About the technology To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. About the book Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms. What's inside The deep learning development cycle Automate training in TensorFlow and PyTorch Dataset management, model serving, and hyperparameter tuning A hands-on deep learning lab About the reader For software developers and engineering-minded data scientists. Examples in Java and Python. About the author Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO. Table of Contents 1 An introduction to deep learning systems 2 Dataset management service 3 Model training service 4 Distributed training 5 Hyperparameter optimization service 6 Model serving design 7 Model serving in practice 8 Metadata and artifact store 9 Workflow orchestration 10 Path to production
Fuzzing Against the Machine
Author: Antonio Nappa
language: en
Publisher: Packt Publishing Ltd
Release Date: 2023-05-19
Find security flaws in any architecture effectively through emulation and fuzzing with QEMU and AFL Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand the vulnerability landscape and useful tools such as QEMU and AFL Explore use cases to find vulnerabilities and execute unknown firmware Create your own firmware emulation and fuzzing environment to discover vulnerabilities Book Description Emulation and fuzzing are among the many techniques that can be used to improve cybersecurity; however, utilizing these efficiently can be tricky. Fuzzing Against the Machine is your hands-on guide to understanding how these powerful tools and techniques work. Using a variety of real-world use cases and practical examples, this book helps you grasp the fundamental concepts of fuzzing and emulation along with advanced vulnerability research, providing you with the tools and skills needed to find security flaws in your software. The book begins by introducing you to two open source fuzzer engines: QEMU, which allows you to run software for whatever architecture you can think of, and American fuzzy lop (AFL) and its improved version AFL++. You'll learn to combine these powerful tools to create your own emulation and fuzzing environment and then use it to discover vulnerabilities in various systems, such as iOS, Android, and Samsung's Mobile Baseband software, Shannon. After reading the introductions and setting up your environment, you'll be able to dive into whichever chapter you want, although the topics gradually become more advanced as the book progresses. By the end of this book, you'll have gained the skills, knowledge, and practice required to find flaws in any firmware by emulating and fuzzing it with QEMU and several fuzzing engines. What you will learn Understand the difference between emulation and virtualization Discover the importance of emulation and fuzzing in cybersecurity Get to grips with fuzzing an entire operating system Discover how to inject a fuzzer into proprietary firmware Know the difference between static and dynamic fuzzing Look into combining QEMU with AFL and AFL++ Explore Fuzz peripherals such as modems Find out how to identify vulnerabilities in OpenWrt Who this book is for This book is for security researchers, security professionals, embedded firmware engineers, and embedded software professionals. Learners interested in emulation, as well as software engineers interested in vulnerability research and exploitation, software testing, and embedded software development will also find it useful. The book assumes basic knowledge of programming (C and Python); operating systems (Linux and macOS); and the use of Linux shell, compilation, and debugging.