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Cyber Space and Outer Space Security
This book delves into cutting-edge research in cyberspace and outer space security, encompassing both theoretical and experimental aspects. It provides mitigation measures and strategies to address the identified challenges within. It covers a spectrum of topics including techniques and strategies for enhancing cyberspace security, combating ransomware attacks, and securing autonomous vehicles. Additionally, it explores security and surveillance systems involving autonomous vehicles, resilience schemes against security attacks using blockchain for autonomous vehicles, security analysis of autonomous drones (UAVs), the cybersecurity kill chain, the internet of drones (IoD), and cyberspace sol...
Federated Learning
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-th...
AI, Machine Learning and Deep Learning
Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on ...
Four Internets
Four Internets offers a revelatory new approach for conceptualizing the Internet and understanding the sometimes rival values that drive its governance and stability. It unravels how tensions between the models play out across politics, economics, and technology, ultimately debating whether these models can continue to co-exist--or what might happen if any fall away.
Federated Learning for Internet of Medical Things
This book intends to present emerging Federated Learning (FL)-based architectures, frameworks, and models in Internet of Medical Things (IoMT) applications. It intends to build on the basics of the healthcare industry, the current data sharing requirements, and security and privacy issues in medical data sharing. Once IoMT is presented, the book shifts towards the proposal of privacy-preservation in IoMT, and explains how FL presents a viable solution to these challenges. The claims are supported through lucid illustrations, tables, and examples that present effective and secured FL schemes, simulations, and practical discussion on use-case scenarios in a simple manner. The book intends to c...
Adversarial AI Attacks, Mitigations, and Defense Strategies
Learn how to defend AI and LLM systems against manipulation and intrusion through adversarial attacks such as poisoning, trojan horses, and model extraction, leveraging DevSecOps, MLOps and other methods to secure systems Key Features Understand the unique security challenges presented by predictive and generative AI Explore common adversarial attack strategies as well as emerging threats such as prompt injection Mitigate the risks of attack on your AI system with threat modeling and secure-by-design methods Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI ...
Digital Contact Tracing for Pandemic Response
As nations race to hone contact-tracing efforts, the world's experts consider strategies for maximum transparency and impact. As public health professionals around the world work tirelessly to respond to the COVID-19 pandemic, it is clear that traditional methods of contact tracing need to be augmented in order to help address a public health crisis of unprecedented scope. Innovators worldwide are racing to develop and implement novel public-facing technology solutions, including digital contact tracing technology. These technological products may aid public health surveillance and containment strategies for this pandemic and become part of the larger toolbox for future infectious outbreak p...
Computer Vision – ECCV 2022
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
