Trust-as-a-Service: A reputation-enabled trust framework for 5G network resource provisioning

Trust, security, and privacy are three of the major pillars to assemble the fifth-generation network and beyond. Despite such pillars are principally interconnected, a multitude of challenges arise that need to be addressed separately. 5G networks ought to offer flexible and pervasive computing capabilities across multiple domains according to user demands and assure trustworthy network providers. To this end, distributed marketplaces expect to boost the trading of heterogeneous resources so as to enable the establishment of pervasive service chains between cross-domains. Yet, the need for selecting reliable parties as “marketplace operators” plays a pivotal role in achieving a trustworthy ecosystem. Two of the principal blockages in managing foreseeable networks are the need to consider trust as a property in the resource provisioning process and adapt previous trust models to accomplish the new network and business requirements. In this regard, this article is centered on the trust management of 5G multi-party network resource provisioning. As a result, a reputation-based trust framework is proposed as a Trust-as-a-Service (TaaS) solution for a distributed multi-stakeholder environment where requirements such as zero trust and zero-touch principles should be met. Besides, a literature review is also conducted to recognize the network and business requirements currently envisaged. Finally, the validation of the proposed trust framework was performed in a real research environment, the 5GBarcelona testbed, leveraging 12% of a 2.1 GHz CPU with 20 cores and 2% of the 30 GiB memory. These outcomes reveal the TaaS solution’s feasibility and conservative approach in the context of determining reliable network operators

LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers

In today's computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and the Edge computing paradigm, benchmarking resource-constrained devices is a critical task to evaluate their suitability and performance. The literature has extensively explored the performance of IoT devices when running high-level benchmarks specialized in particular application scenarios, such as AI or medical applications. However, lower-level benchmarking applications and datasets that analyze the hardware components of each device are needed. This low-level device understanding enables new AI solutions for network, system and service management based on device performance, such as individual device identification, so it is an area worth exploring more in detail. In this paper, we present LwHBench, a low-level hardware benchmarking application for Single-Board Computers that measures the performance of CPU, GPU, Memory and Storage taking into account the component constraints in these types of devices. LwHBench has been implemented for Raspberry Pi devices and run for 100 days on a set of 45 devices to generate an extensive dataset that allows the usage of AI techniques in different application scenarios. Finally, to demonstrate the inter-scenario capability of the created dataset, a series of AI-enabled use cases about device identification and context impact on performance are presented as examples and exploration of the published data

Can Evil IoT Twins Be Identified? Now Yes, a Hardware Behavioral Fingerprinting Methodology

The connectivity and resource-constrained nature of IoT, and in particular single-board devices, opens up to cybersecurity concerns affecting the Industrial Internet of Things (IIoT). One of the most important is the presence of evil IoT twins. Evil IoT twins are malicious devices, with identical hardware and software configurations to authorized ones, that can provoke sensitive information leakages, data poisoning, or privilege escalation in industrial scenarios. Combining behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques is a promising solution to identify evil IoT twins by detecting minor performance differences generated by imperfections in manufacturing. However, existing solutions are not suitable for single-board devices because they do not consider their hardware and software limitations, underestimate critical aspects during the identification performance evaluation, and do not explore the potential of ML/DL techniques. Moreover, there is a dramatic lack of work explaining essential aspects to considering during the identification of identical devices. This work proposes an ML/DL-oriented methodology that uses behavioral fingerprinting to identify identical single-board devices. The methodology leverages the different built-in components of the system, comparing their internal behavior with each other to detect variations that occurred in manufacturing processes. The validation has been performed in a real environment composed of identical Raspberry Pi 4 Model B devices, achieving the identification for all devices by setting a 50% threshold in the evaluation process. Finally, a discussion compares the proposed solution with related work and provides important lessons learned and limitations

A methodology to identify identical single-board computers based on hardware behavior fingerprinting

The connectivity and resource-constrained nature of single-board devices open the door to cybersecurity concerns affecting Internet of Things (IoT) scenarios. One of the most important issues is the presence of unauthorized IoT devices that want to impersonate legitimate ones by using identical hardware and software specifications. This situation can provoke sensitive information leakages, data poisoning, or privilege escalation in IoT scenarios. Combining behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques is a promising approach to identify these malicious spoofing devices by detecting minor performance differences generated by imperfections in manufacturing. However, existing solutions are not suitable for single-board devices since they do not consider their hardware and software limitations, underestimate critical aspects such as fingerprint stability or context changes, and do not explore the potential of ML/DL techniques. To improve it, this work first identifies the essential properties for single-board device identification: uniqueness, stability, diversity, scalability, efficiency, robustness, and security. Then, a novel methodology relies on behavioral fingerprinting to identify identical single-board devices and meet the previous properties. The methodology leverages the different built-in components of the system and ML/DL techniques, comparing the device internal behavior with each other to detect variations that occurred in manufacturing processes. The methodology validation has been performed in a real environment composed of 15 identical Raspberry Pi 4 Model B and 10 Raspberry Pi 3 Model B＋ devices, obtaining a 91.9% average TPR with an XGBoost model and achieving the identification for all devices by setting a 50% threshold in the evaluation process. Finally, a discussion compares the proposed solution with related work, highlighting the fingerprint properties not met, and provides important lessons learned and limitations

LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers

In today’s computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and Edge computing paradigms, benchmarking resource-constrained devices is a critical task to evaluate their suitability and performance. Between the employed devices, Single-Board Computers arise as multi-purpose and affordable systems. The literature has explored Single-Board Computers performance when running high-level benchmarks specialized in particular application scenarios, such as AI or medical applications. However, lower-level benchmarking applications and datasets are needed to enable new Edge-based AI solutions for network, system and service management based on device and component performance, such as individual device identification. Thus, this paper presents LwHBench, a low-level hardware benchmarking application for Single-Board Computers that measures the performance of CPU, GPU, Memory and Storage taking into account the component constraints in these types of devices. LwHBench has been implemented for Raspberry Pi devices and run for 100 days on a set of 45 devices to generate an extensive dataset that allows the usage of AI techniques in scenarios where performance data can help in the device management process. Besides, to demonstrate the inter-scenario capability of the dataset, a series of AI-enabled use cases about device identification and context impact on performance are presented as exploration of the published data. Finally, the benchmark application has been adapted and applied to an agriculture-focused scenario where three RockPro64 devices are present

Gestión de la confianza basada en la reputación para escenarios más allá de 5G

La rápida expansión de los servicios y dispositivos interconectados ha provocado un aumento en la variedad y el número de relaciones entre entidades. Con la llegada de las redes 5G, es cada vez más común que entidades de diferentes dominios administrativos establezcan conexiones. Este crecimiento de la interconectividad a menudo ejerce presión sobre los servicios y recursos, lo que empuja a los usuarios finales a buscar asistencia de la infraestructura de red o de los proveedores de servicios para mejorar sus capacidades. Además, la gran cantidad de opciones disponibles para implementar servicios y recursos a veces genera una confianza inherente. Dependiendo de sus necesidades, los usuarios finales pueden optar por soluciones como Infrastructure-as-a-Service, que les permite personalizar los recursos informáticos virtualizados. Alternativamente, podrían gravitar hacia los mercados, que ofrecen la conveniencia de utilizar recursos de terceros sin la necesidad de configurarlos y mantenerlos. Los mercados, en particular, están a su vez ganando terreno debido a su adaptabilidad en entornos dinámicos. Sin embargo, la confianza sigue siendo una piedra angular en ambos escenarios. Es fundamental a la hora de decidir una asociación comercial o elegir un servicio. Si bien los mercados a menudo carecen de opciones de filtrado basadas en la confianza, sí ofrecen otros filtros avanzados basados en el rendimiento, el hardware o la ubicación. Es fundamental señalar que los modelos de confianza tradicionales podrían no ser adecuados para las redes 5G modernas, dados los desafíos en constante evolución. Por lo tanto, existe la necesidad de modelos de confianza que consideren los atributos únicos y los KPI de vanguardia de estas redes. Bajo la modalidad de compendio, a continuación, se detallan los tres capítulos que componen esta tesis doctoral y sus principales objetivos. En primer lugar, se realizó una revisión exhaustiva de la literatura sobre modelos de confianza y reputación en entornos 5G; el primer capítulo informa sobre las propiedades y características esenciales de los modelos de confianza, ocho habilitadores claves junto con una revisión y comparación de los artículos más recientes relacionados con la confianza, y las tendencias y desafíos que marcan la dirección de esta tesis. En segundo lugar, se desarrolló una estrategia previa a la estandarización de modelos de confianza basados en la reputación más allá del 5G; el segundo capítulo informa sobre el análisis de documentos de estandarización, iniciativas de investigación y organismos reguladores, brinda una lista de requisitos y KPI vitales y ofrece recomendaciones preliminares para abordar la falta de modelos de confianza estandarizados en las redes post-5G. En tercer lugar, se examinó un análisis del marco de confianza basado en la reputación cuando se sufren diferentes ataques relacionados con la confianza; el tercer capítulo desarrolla el marco propuesto, compuesto por cuatro módulos, y proporciona una solución dinámica y automatizada, desarrollada en el marco del proyecto europeo 5GZORRO, para servicios bajo demanda y aprovisionamiento de recursos en mercados descentralizados. En este artículo, se creó un modelo PeerTrust adaptado para calcular puntuaciones de confianza basadas en información estadística inferida de ofertas de productos, proveedores de redes y recomendadores. Además, se diseñó un mecanismo de recompensa y castigo basado en SLA para adaptar continuamente los puntajes de confianza de una relación de confianza en curso cuando la violación del SLA, la predicción de incumplimiento o la detección de incumplimiento aparecían en tiempo real. Los experimentos demostraron que nuestro marco de reputación era resistente a los ataques bad-mouthing y on-off. En resumen, los capítulos que componen esta tesis doctoral promueven una investigación coherente que explora, analiza y, en última instancia, aborda soluciones de confianza basadas en la reputación para escenarios más allá del 5G. Sin embargo, algunas cuestiones planteadas por esta investigación siguen sin resolverse, por lo que aún requieren más esfuerzos. El principal de ellos es si será viable llevar a cabo una estandarización oficial de los modelos de confianza basados en la reputación para homogeneizar las soluciones y remar en la misma dirección.The rapid expansion of interconnected services and devices has led to a surge in the variety and number of relationships between entities. With the advent of 5G networks, it iss increasingly common for entities from different administrative domains to establish connections. This growth in interconnectivity often places a strain on resources, pushing end-users to seek assistance from network infrastructure or service providers to enhance their capabilities. Moreover, the plethora of options available for deploying services and resources sometimes leads to an inherent trust. Depending on their needs, end-users might opt for solutions like Infrastructure-as-a-Service, which allows them to tailor virtualized computing resources. Alternatively, they might gravitate towards marketplaces, which offer the convenience of using third-party resources without the hassle of setting up and maintaining them. Marketplaces, in particular, are gaining traction due to their adaptability in dynamic environments. However, trust remains a cornerstone in both scenarios. It's essential when deciding on a business partnership or choosing a service. While marketplaces often lack trust-based filtering options, they do offer advanced filters based on performance, hardware, or location. It is crucial to note that traditional trust models might not be suitable for modern 5G networks, given the ever-evolving challenges. Hence, there is a need for trust models that consider the unique attributes and cutting-edge KPIs of these networks. Under the compendium modality, the three chapters composing this PhD dissertation and their principal objectives are outlined as follows. Firstly, a comprehensive literature review on trust and reputation models in 5G settings was conducted; the first chapter reports on the essential properties and features of trust models, eight key enablers along with a review and comparison of the most recent trust-related papers, and trends and challenges that set the direction for this thesis. Secondly, a pre-standardization strategy for reputation-based trust models beyond 5G was developed; the second chapter reports on examining standardization papers, research initiatives, and regulatory bodies, providing a list of requirements and vital KPIs, and offering preliminary recommendations to address the lack of standardized trust models in post-5G networks. Thirdly, an analysis of the reputation-enabled trust framework when suffering different trust-related attack bursts was examined; the third chapter develops the proposed framework, composed of four modules, and provides a dynamic and automated solution, developed under the 5GZORRO European project, for on-demand service and resource provisioning in decentralized marketplaces. In this article, an adapted PeerTrust model was created to compute trust scores based on statistical information inferred from product offers, network providers, and recommenders. Additionally, an SLA-driven reward and punishment mechanism was designed to continuously adapt trust scores of an ongoing trust relationship when SLA violation, breach prediction, or breach detection appeared in real time. Experiments demonstrated that our reputation framework was resilient to bad-mouthing and on-off attacks. In summary, the chapters composing this PhD dissertation promote cohesive research exploring, analysing and, ultimately, addressing reputation-based trust solutions for beyond 5G scenarios. Nevertheless, some questions mooted by this research remain unsolved, so they still require more effort. Prime among them is whether it will be feasible to conduct an official standardization of reputation-based trust models to homogenise solutions and rowing in the same directio

Toward pre-standardization of reputation-based trust models beyond 5G

In the last years, the number of connections in mobile telecommunication networks has increased rampantly, and in consequence, the number and type of relationships among entities. Should such interactions are to be profitable, entities will need to rely on each other. Hence, mobile telecommunication networks demand trust and reputation models that allow developing feasible communications in 5G and beyond networks, through which a group of entities can establish chains of services between cross-operators/domains, with security and trustworthiness. One of the key obstacles to achieving generalized connectivity beyond 5G networks is the lack of automatized, efficient, and scalable models for establishing security and trust. In this vein, this article proposes a pre-standardization approach for reputation-based trust models beyond 5G. To this end, we have realized a thorough review of the literature to match trust standardization approaches. An abstract set of requirements and key performance indicators has been extracted, and some pre-standardization recommendations proposed to fulfill essential conditions of future networks and to cover the lack of common trust and reputation models beyond 5G

A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets

In the current network-based computing world, where the number of interconnected devices grows exponentially, their diversity, malfunctions, and cybersecurity threats are increasing at the same rate. To guarantee the correct functioning and performance of novel environments such as Smart Cities, Industry 4.0, or crowdsensing, it is crucial to identify the capabilities of their devices (e.g., sensors, actuators) and detect potential misbehavior that may arise due to cyberattacks, system faults, or misconfigurations. With this goal in mind, a promising research field emerged focusing on creating and managing fingerprints that model the behavior of both the device actions and its components. The article at hand studies the recent growth of the device behavior fingerprinting field in terms of application scenarios, behavioral sources, and processing and evaluation techniques. First, it performs a comprehensive review of the device types, behavioral data, and processing and evaluation techniques used by the most recent and representative research works dealing with two major scenarios: device identification and device misbehavior detection. After that, each work is deeply analyzed and compared, emphasizing its characteristics, advantages, and limitations. This article also provides researchers with a review of the most relevant characteristics of existing datasets as most of the novel processing techniques are based on Machine Learning and Deep Learning. Finally, it studies the evolution of these two scenarios in recent years, providing lessons learned, current trends, and future research challenges to guide new solutions in the area

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users’ quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users’ behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users’ authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level

5G-PPP Technology Board:AI and ML – Enablers for Beyond 5G Networks

This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from 5G PPP projects that research, implement and validate 5G and B5G network systems. The white paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for 5G and B5G networks. A family of neural networks is presented which are, generally speaking, non-linear statistical data modelling and decision-making tools. They are typically used to model complex relationships between input and output parameters of a system or to find patterns in data. Feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks belong to this family. Reinforcement learning is concerned about how intelligent agents must take actions in order to maximize a collective reward, e.g., to improve a property of the system. Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data. Hybrid solutions are presented such as combined analytical and machine learning modelling as well as expert knowledge aided machine learning. Finally, other specific methods are presented, such as generative adversarial networks and unsupervised learning and clustering. In the sequel the white paper elaborates on use case and optimisation problems that are being tackled with AI/ML, partitioned in three major areas namely, i) Network Planning, ii) Network Diagnostics/Insights, and iii) Network Optimisation and Control. In Network Planning, attention is given to AI/ML assisted approaches to guide planning solutions. As B5G networks become increasingly complex and multi-dimensional, parallel layers of connectivity are considered a trend towards disaggregated deployments in which a base station is distributed over a set of separate physical network elements which ends up in the growing number of services and network slices that need to be operated. This climbing complexity renders traditional approaches in network planning obsolete and calls for their replacement with automated methods that can use AI/ML to guide planning decisions. In this respect two solutions are discussed, first the network element placement problem is introduced which aims at improvements in the identification of optimum constellation of base stations each located to provide best network performance taking into account various parameters, e.g. coverage, user equipment (UE) density and mobility patterns (estimates), required hardware and cabling, and overall cost. The second problem considered in this regard is the dimensioning considerations for C-RAN clusters, in which employing ML-based algorithms to provide optimal allocation of baseband unit (BBU) functions (to the appropriate servers hosted by the central unit (CU)) to provide the expected gains is addressed. In Network Diagnostics, attention is given to the tools that can autonomously inspect the network state and trigger alarms when necessary. The contributions are divided into network characteristics forecasts solutions, precise user localizations methods, and security incident identification and forecast. The application of AI/ML methods in high-resolution synthesising and efficient forecasting of mobile traffic; QoE inference and QoS improvement by forecasting techniques; service level agreement (SLA) prediction in multi-tenant environments; and complex event recognition and forecasting are among network characteristics forecasts methods discussed. On high-precision user localization, AI-assisted sensor fusion and line-of-sight (LoS)/non-line-of-sight (NLoS) discrimination, and 5G localization based on soft information and sequential autoencoding are introduced. And finally, on forecasting security incidents, after a short introduction on modern attacks in mobile networks, ML-based network traffic inspection and real-time detection of distributed denial-of-service (DDoS) attacks are briefly examined. In regard to the Network Optimisation and Control, attention is given to the different network segments, including radio access, transport/fronthaul (FH)/backhaul (BH), virtualisation infrastructure, end-to-end 5G PPP Technology Board AI/ML for Networks 3 (E2E) network slicing, security, and application functions. Among application of AI/ML in radio access, the slicing in multi-tenant networks, radio resource provisioning and traffic steering, user association, demand-driven power allocation, joint MAC scheduling (across several gNBs), and propagation channel estimation and modelling are discussed. Moreover, these solutions are categorised (based on the application time-scale) into real-time, near-real-time, and non-real-time groups. On transport and FH/BH networks, AI/ML algorithms on triggering path computations, traffic management (using programmable switches), dynamic load balancing, efficient per-flow scheduling, and optimal FH/BH functional splitting are introduced. Moreover, federated learning across MEC and NFV orchestrators, resource allocation for service function chaining, and dynamic resource allocation in NFV infrastructure are among introduced AI/ML applications for virtualisation infrastructure. In the context of E2E slicing, several applications such as automated E2E service assurance, resource reservation (proactively in E2E slice) and resource allocation (jointly with slice-based demand prediction), slice isolation, and slice optimisation are presented. In regard to the network security, the application of AI/ML techniques in responding to the attack incidents are discussed for two cases, i.e. in moving target defence for network slice protection, and in self-protection against app-layer DDoS attacks. And finally, on the AI/ML applications in optimisation of application functions, the dash prefetching optimization and Q-learning applications in federated scenarios are presented.The white paper continues with the discussions on the application of AI/ML in the 5G and B5G network architectures. In this context the AI/ML based solutions pertaining to autonomous slice management, control and orchestration, cross-layer optimisation framework, anomaly detection, and management analytics, as well as aspects in AI/ML-as-a-service in network management and orchestration, and enablement of ML for the verticals' domain are presented. This is followed by topics on management of ML models and functions, namely the ML model lifecycle management, e.g., training, monitoring, evaluation, configuration and interface management of ML models. Furthermore, the white paper investigates the standardisation activities on the enablement of AI/ML in networks, including the definition of network data analytics function (NDAF) by 3GPP, the definition of an architecture that helps address challenges in network automation and optimization using AI and the categories of use cases where AI may benefit network operation and management by ETSI ENI, and finally the O-RAN definition of non-real-time and near-real-time RAN controllers to support ML-based management and intelligent RAN optimisation. Additionally, the white paper identifies the challenges in view of privacy and trust in AI/ML-based networks and potential solutions by introducing privacy preserving mechanisms and the zero-trust management approach are introduced. The availability of reliable data-sets as a crucial prerequisite to efficiency of AI/ML algorithms is discussed and the white paper concludes with a brief overview of AI/ML-based KPI validation and system troubleshooting. In summary the findings of this white paper conclude with the identification of several areas (research and development work) for further attention in order to enhance future network return-on-investment (ROI): (a) building standardized interfaces to access relevant and actionable data, (b) exploring ways of using AI to optimize customer experience, (c) running early trials with new customer segments to identify AI opportunities, (d) examining use of AI and automation for network operations, including planning and optimization, (e) ensuring early adoption of new solutions for AI and automation to facilitate introduction of new use cases, and (f) establish/launch an open repository for network data-sets that can be used for training and benchmarking algorithms by all