273 research outputs found

    Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

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    Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

    Towards trustworthy computing on untrustworthy hardware

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    Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Security and Privacy of Resource Constrained Devices

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    The thesis aims to present a comprehensive and holistic overview on cybersecurity and privacy & data protection aspects related to IoT resource-constrained devices. Chapter 1 introduces the current technical landscape by providing a working definition and architecture taxonomy of ‘Internet of Things’ and ‘resource-constrained devices’, coupled with a threat landscape where each specific attack is linked to a layer of the taxonomy. Chapter 2 lays down the theoretical foundations for an interdisciplinary approach and a unified, holistic vision of cybersecurity, safety and privacy justified by the ‘IoT revolution’ through the so-called infraethical perspective. Chapter 3 investigates whether and to what extent the fast-evolving European cybersecurity regulatory framework addresses the security challenges brought about by the IoT by allocating legal responsibilities to the right parties. Chapters 4 and 5 focus, on the other hand, on ‘privacy’ understood by proxy as to include EU data protection. In particular, Chapter 4 addresses three legal challenges brought about by the ubiquitous IoT data and metadata processing to EU privacy and data protection legal frameworks i.e., the ePrivacy Directive and the GDPR. Chapter 5 casts light on the risk management tool enshrined in EU data protection law, that is, Data Protection Impact Assessment (DPIA) and proposes an original DPIA methodology for connected devices, building on the CNIL (French data protection authority) model

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Themelio: a new blockchain paradigm

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    Public blockchains hold great promise in building protocols that uphold security properties like transparency and consistency based on internal, incentivized cryptoeconomic mechanisms rather than preexisting trust in participants. Yet user-facing blockchain applications beyond "internal" immediate derivatives of blockchain incentive models, like cryptocurrency and decentralized finance, have not achieved widespread development or adoption. We propose that this is not primarily due to "engineering" problems in aspects such as scaling, but due to an overall lack of transferable endogenous trust—the twofold ability to uphold strong, internally-generated security guarantees and to translate them into application-level security. Yet we argue that blockchains, due to their foundation on game-theoretic incentive models rather than trusted authorities, are uniquely suited for building transferable endogenous trust, despite their current deficiencies. We then engage in a survey of existing public blockchains and the difficulties and crises that they have faced, noting that in almost every case, problems such as governance disputes and ecosystem inflexibility stem from a lack of transferable endogenous trust. Next, we introduce Themelio, a decentralized, public blockchain designed to support a new blockchain paradigm focused on transferable endogenous trust. Here, the blockchain is used as a low-level, stable, and simple root of trust, capable of sharing this trust with applications through scalable light clients. This contrasts with current blockchains, which are either applications or application execution platforms. We present evidence that this new paradigm is crucial to achieving flexible deployment of blockchain-based trust. We then describe the Themelio blockchain in detail, focusing on three areas key to its overall theme of transferable, strong endogenous trust: a traditional yet enhanced UTXO model with features that allow powerful programmability and light-client composability, a novel proof-of-stake system with unique cryptoeconomic guarantees against collusion, and Themelio's unique cryptocurrency "mel", which achieves stablecoin-like low volatility without sacrificing decentralization and security. Finally, we explore the wide variety of novel, partly off-chain applications enabled by Themelio's decoupled blockchain paradigm. This includes Astrape, a privacy-protecting off-chain micropayment network, Bitforest, a blockchain-based PKI that combines blockchain-backed security guarantees with the performance and administration benefits of traditional systems, as well as sketches of further applications

    Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

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    Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

    Expanding the UK Secure by Design proposal for a usable consumer-focused IoT security label

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    No person whom has any knowledge of security in the Internet of Things (IoT) would claim the current landscape is desirable, as exceedingly poor security of devices is routinely exhibited in an ecosystem experiencing exponential growth of devices. If these devices follow past trends in Cyber Security, it is not unreasonable to assume that without intervention another decade of exponentially growing costs attributed to Cyber Crime may lay ahead. After the failure of the voluntary approach to IoT Security, works are now being taken to legislate a minimum security standard.Building from existing proposals, this paper outlines real improvements that could be made to current ongoing works, with the intention of providing incentive for manufacturers to improve device security in the IoT sector and reduce the timeline for routine deployment of secured devices.Incorporating strategies developed in other industries, as well as security requirements from across international borders, a point-of-sale user focused label is proposed, which can be easily interpreted by non-technical users. Intending to provoke curiosity and fully reassure the end-user, a two-layer system is chosen which allows the conveyance of more detailed information than could fit on a physical label

    A comprehensive survey of V2X cybersecurity mechanisms and future research paths

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    Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version
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