Optimal Selection Techniques for Cloud Service Providers

Nowadays Cloud computing permeates almost every domain in Information and Communications Technology (ICT) and, increasingly, most of the action is shifting from large, dominant players toward independent, heterogeneous, private/hybrid deployments, in line with an ever wider range of business models and stakeholders. The rapid growth in the numbers and diversity of small and medium Cloud providers is bringing new challenges in the as-a-Services space. Indeed, significant hurdles for smaller Cloud service providers in being competitive with the incumbent market leaders induce some innovative players to "federate" deployments in order to pool a larger, virtually limitless, set of resources across the federation, and stand to gain in terms of economies of scale and resource usage efficiency. Several are the challenges that need to be addressed in building and managing a federated environment, that may go under the "Security", "Interoperability", "Versatility", "Automatic Selection" and "Scalability" labels. The aim of this paper is to present a survey about the approaches and challenges belonging to the "Automatic Selection" category. This work provides a literature review of different approaches adopted in the "Automatic and Optimal Cloud Service Provider Selection", also covering "Federated and Multi-Cloud" environments

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Health Information Systems in the Digital Health Ecosystem—Problems and Solutions for Ethics, Trust and Privacy

Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject's perspective, there is frequently no guarantee and therefore no trust that PHI is processed ethically in Digital Health Ecosystems. This results in new ethical, privacy and trust challenges to be solved. The authors' objective is to find a combination of ethical principles, privacy and trust models, together enabling design, implementation of DHIS acting ethically, being trustworthy, and supporting the user's privacy needs. Research published in journals, conference proceedings, and standards documents is analyzed from the viewpoint of ethics, privacy and trust. In that context, systems theory and systems engineering approaches together with heuristic analysis are deployed. The ethical model proposed is a combination of consequentialism, professional medical ethics and utilitarianism. Privacy enforcement can be facilitated by defining it as health information specific contextual intellectual property right, where a service user can express their own privacy needs using computer-understandable policies. Thereby, privacy as a dynamic, indeterminate concept, and computational trust, deploys linguistic values and fuzzy mathematics. The proposed solution, combining ethical principles, privacy as intellectual property and computational trust models, shows a new way to achieve ethically acceptable, trustworthy and privacy-enabling DHIS and Digital Health Ecosystems

Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics

Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics

Developing an Effective Detection Framework for Targeted Ransomware Attacks in Brownfield Industrial Internet of Things

The Industrial Internet of Things (IIoT) is being interconnected with many critical industrial activities, creating major cyber security concerns. The key concern is with edge systems of Brownfield IIoT, where new devices and technologies are deployed to interoperate with legacy industrial control systems and leverage the benefits of IoT. These edge devices, such as edge gateways, have opened the way to advanced attacks such as targeted ransomware. Various pre-existing security solutions can detect and mitigate such attacks but are often ineffective due to the heterogeneous nature of the IIoT devices and protocols and their interoperability demands. Consequently, developing new detection solutions is essential. The key challenges in developing detection solutions for targeted ransomware attacks in IIoT systems include 1) understanding attacks and their behaviour, 2) designing accurate IIoT system models to test attacks, 3) obtaining realistic data representing IIoT systems' activities and connectivities, and 4) identifying attacks. This thesis provides important contributions to the research focusing on investigating targeted ransomware attacks against IIoT edge systems and developing a new detection framework. The first contribution is developing the world's first example of ransomware, specifically targeting IIoT edge gateways. The experiments' results demonstrate that such an attack is now possible on edge gateways. Also, the kernel-related activity parameters appear to be significant indicators of the crypto-ransomware attacks' behaviour, much more so than for similar attacks in workstations. The second contribution is developing a new holistic end-to-end IIoT security testbed (i.e., Brown-IIoTbed) that can be easily reproduced and reconfigured to support new processes and security scenarios. The results prove that Brown-IIoTbed operates efficiently in terms of its functions and security testing. The third contribution is generating a first-of-its-kind dataset tailored for IIoT systems covering targeted ransomware attacks and their activities, called X-IIoTID. The dataset includes connectivity- and device-agnostic features collected from various data sources. The final contribution is developing a new asynchronous peer-to-peer federated deep learning framework tailored for IIoT edge gateways for detecting targeted ransomware attacks. The framework's effectiveness has been evaluated against pre-existing datasets and the newly developed X-IIoTID dataset

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.Comment: 33 pages, 13 figure