• Trust as a new intent for orchestrating the future 6G networks. • Continuous network assurance for cloud continuum services. • The Level of Trust framework monitors and double checks trust level agreements. • Trust-oriented ontology for Cloud Continuum and intent-based scenarios. Intent-Based Networking (IBN) has gained prominence from both industry and research communities for boosting network automation and reducing network complexity in multi-stakeholder scenarios. IBN helps service and network providers understand and translate high-level business goals by escaping from technical details. Nevertheless, the rapid evolution of service requirements in dynamic multi-stakeholder environments needs to be considered by suppliers in order to meet the continuous demands of their customers. In this regard, trustworthiness is recognized as a key feature to assess reliability, compliance, and user perception of 6G services. In that spirit, this article aims to pave the way for encompassing trustworthiness, or Level of Trust (LoT), as a new intent to properly deliver service provisioning in 6G solutions. In particular, the LoT framework introduces a set of functionalities to guarantee a proper definition of requirements (Trust Level Agreements, TLAs), their interpretation, translation, consistency, and assurance during an ongoing relationship. To this end, this article presents a trust-based intent management approach with an ad-hoc interpreter that is, in turn, powered by a trust-based ontology for Cloud Continuum scenarios. Besides, a Level of Trust Assessment Function (LoTAF) is designed to monitor and report updates and events regarding LoT continuously. Such a monitoring engine follows the IETF basics of service assurance for IBN architecture. Last but not least, experiments in a collaborative robot warehouse scenario showcase that LoTAF enables workload allocation to the most trustworthy compute nodes, improving confidence in service orchestration, while introducing negligible overheads (1.40 % RAM, 5.01 % CPU, and 0.1069s latency).

In today’s dynamic and complex warfare landscape, characterized by the convergence of traditional and emerging threats, the significance of cybersecurity in shaping modern conflicts cannot be overstated. Such trend presents a challenging paradigm shift in how military organizations approach mosaic warfare in the digital age since new attack vectors and targets appear in their landscapes. In this vein, it is pivotal for military teams to have a clear and concise roadmap for cybersecurity incidents linked to potential mosaic warfare. This manuscript introduces a novel approach to bolstering mosaic warfare strategies by integrating an advanced Knowledge Representation and Reasoning system and a tailored ontology. Motivated by the critical role of cybersecurity in contemporary warfare, the proposed system aims to enhance situational awareness, decision-making capabilities, and operational effectiveness in the face of evolving cyber threats. In this sense, this manuscript entails a new ontology that not only covers the cybersecurity realm but also introduces key concepts related to strategic and operational military levels at the same time. The ad-hoc ontology is also compared against other well-known ones, such as MITRE, NATO, or UCO approaches and manifests a significant performance by employing standardized quality metrics for ontologies. Lastly, a realistic mosaic warfare scenario is contextualized to demonstrate the deployment of the proposed system and how it can properly represent all information gathered from heterogeneous data sources.

The fifth generation (5G) of mobile telecommunications is characterized by massive growth in the number of stakeholders, interconnected devices, and available services distributed under different administrative domains. Distributed marketplaces aim at facilitating stakeholders in the quest and hiring of third party resources and services. Establishing trustworthiness in such an open ecosystem is a cornerstone for the final deployment of these marketplaces in 5G networks and beyond. Hence, building trust management systems that ensure the selection of reliable parties or assets in 5G distributed marketplaces is essential. Thus, a reputation-based trust management framework is proposed to analyze stakeholder behavior patterns and predict trust scores to establish trustworthy relationships across domains. Furthermore, an Service Level Agreement (SLA)-driven reward and punishment mechanism is designed and developed on top of the reputation-based trust framework. Such a mechanism enables continuously adapting trust scores by gathering breach predictions, breach detections, and SLA violations in real time. Furthermore, an edge-based use case is presented to contextualize our reputation-based framework in a tangible enforcement scenario. In conclusion, three experiments were conducted on real-life testbeds demonstrating that our framework fairly distinguishes bad-mouthing attacks with 67% accuracy, when 50% recommenders are corrupted, and is resilient to continuous misbehavior bursts.

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.

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.