Satellite telecommunication networks are playing an increasingly pivotal role in modern communication infrastructures, owing to their expansive coverage, high reliability, and growing capabilities in computing, storage, and bandwidth. In response to evolving market demands, mobile network operators are progressively integrating satellite systems with edge-cloud computing platforms to deliver advanced networking functionalities within a unified architecture. This integration places strong demands on the non-functional assessment (e.g., reliability, availability, and resource efficiency) of satellite-based edge nodes, introducing unprecedented challenges due to their unique operational constraints. In this paper, we propose a lightweight certification framework tailored for satellite computing systems, designed to assess and validate the non-functional posture of satellite edge networks. Our approach explicitly addresses the distinctive characteristics of satellite environments, including intermittent connectivity and constrained resource availability. We validate the proposed scheme through a realistic testbed implementation, modeling a 5G-enabled satellite edge node based on the Tiansuan satellite constellation, an experimental platform jointly developed by Beijing University of Posts and Telecommunications, Spacety, and Peking University.

In the present paper, the effectiveness of the use of Machine Learning techniques, in particular Deep Learning algorithms, in the analysis of Ti-6Al-4V (Ti64) manufacture is studied; relationships between the values of physical parameters used during the production and mechanical characteristics are defined by means of the analysis of images taken from sections of the specimens, where defects and microstructural discontinuities can be observed. The Deep Learning approach, widely used for image classification and features extraction, also in this case shows promising possibilities, as proved by the implementation results reported.

The modern edge-cloud continuum data intensive workflows are increasingly based on 6G edge nodes in order to spread their diffusion relying on public network and enhanced by the use of machine learning (ML) models in order to extend their capabilities. Data intensive workflows are also glowingly used in critical scenarios such as health and IoT. In these scenarios, guarantees on the model prediction quality and on the model non-functional properties (e.g., model confidentiality), are nowadays requested in order to comply with regulations such as the EU AI Act. Although the traditional CIA (Confidentiality, Integrity, Availability) triad are largely considered as the minimal non-functional properties to be guaranteed for a given system, they cannot be applied as such in the context of ML models. In this paper we identify the shortcomings of the conventional definition of CIA, provides novel ML-specific definitions for the CIA non-functional properties and develops an assurance methodology to evaluate them on the target models and provide relevant guarantees. The paper presents an experimental evaluation based on a realistic MLOps pipeline aimed to demonstrate its feasibility and effectiveness and is based on the novel definition of ML model integrity Non-Functional Property.

In recent years, the use of caching techniques in web applications has increased significantly, in line with their expanding user base. The logic of web caches is closely tied to the application logic, and misconfigurations can lead to security risks, including the unauthorized access of private information and session hijacking. In this study, we examine Web Cache Deception as a technique for attacking web applications. We develop a solution for discovering vulnerabilities that expands upon and encompasses prior research in the field. We conducted an experimental evaluation of the attack’s efficacy against real-world targets, and present a new attack vector via web-client-based email services.

Nowadays, modern service compositions are increasingly adopted in critical scenarios where advanced Quality of Services (QoS) such as low latency, security, and privacy are fundamental. The landing platforms for the deployment of such compositions are progressively becoming capable to offer capabil-ities that support such advanced QoS requests (e.g., low latency via 5G network slice) spanning the Edge-Cloud Continuum. Actual deployment solutions focus mainly on resource allocation (i.e., CPU, memory, and storage), falling short of addressing advanced QoS and unleashing the true potential of the Edge-Cloud Continuum. In this paper, we present an automatic QoS-aware deployment solution for composed services in the Edge-Cloud Continuum. It compares QoS requests on the service composition with the capabilities of a given continuum in order to find, generate and execute suitable deployment recipes. Our preliminary experimental evaluation demonstrates the feasibility of our solution in a realistic scenario.

Modern (industrial) domains are based on large digital ecosystems where huge amounts of data and information need to be collected, shared, and analyzed by multiple actors working within and across organizational boundaries. This data-driven ecosystem poses strong requirements on data management and data analysis, as well as on data protection and system trustworthiness. However, although Big Data has reached its functional maturity and represents a key enabler for enterprises to compete in the global market, the assurance and trustworthiness of Big Data computations (e.g., security, privacy) are still in their infancy. While functionally appealing, Big Data does not provide a transparent environment with clear non-functional properties, impairing the users’ ability to evaluate its behavior and clashing with modern data-privacy regulations. In this paper, we present a novel assurance process for Big Data, which evaluates the Big Data pipelines, and the Big Data ecosystem underneath, to provide a comprehensive measure of their trustworthiness. To the best of our knowledge, this approach is the first attempt to address the general problem of Big Data trustworthiness in an holistic way. We experimentally evaluate our solution in a real Big Data Analytics-as-a-Service environment, first presenting a detailed walkthrough evaluation, and then showing its feasibility and negligible performance overhead (i.e., approx 1 min).

Today big data pipelines are increasingly adopted by service applications representing a key enabler for enterprises to compete in the global market. However, the management of non-functional aspects of the big data pipeline (e.g., security, privacy) is still in its infancy. As a consequence, while functionally appealing, the big data pipeline does not provide a transparent environment, impairing the users’ ability to evaluate its behavior. In this paper, we propose a security assurance methodology for big data pipelines grounded on the DevSecOps development paradigm to increase trustworthiness allowing reliable security and privacy by design. Our methodology models and annotates big data pipelines with non-functional requirements verified by assurance checks ensuring requirements to hold along with the pipeline lifecycle. The performance and quality of our methodology are evaluated in a real walkthrough analytics scenario.

Nowadays there is an increasing trend in the volume and velocity of data, typically consumed by data-intensive AI/ML-based services, requiring a larger diffusion of more effective Edge computing approaches. In addition, we are experiencing an increment of critical applications using an increasing volume of sensitive data and requiring advanced security and privacy protections. 5G Edge technology can foster a more diffused Edge computing adoption but several challenges in terms of interoperability. Handling data-intensive pipelines on the 5Genabled Edge continuum, considering specific QoS requirements including security and privacy, is still in its infancy. In this paper, we propose an initial solution for deploying a data-intensive pipeline in a 5G-enabled Edge continuum satisfying specific QoS requirements. Our approach is based on a QoS-aware meta orchestration modeling of a given pipeline and an orchestration builder generating deployable Edge-specific orchestrations. In this paper, we also present an initial walkthrough scenario in the context of a wet lab analysis pipeline to be deployed on the 5G-enabled Edge continuum.

Information-Centric Networking is an emerging alternative to host-centric networking designed for large-scale content distribution and stricter privacy requirements. Recent research on Information-Centric Networking focused on the protection of the network from attacks targeting the content delivery protocols, while assuming genuine content can always be retrieved from trustworthy nodes. In this paper, we depart from the assumption of the trustworthiness of network nodes and propose a novel certification methodology for information-centric networks that supports continuous security verification of non-functional properties. Our methodology provides a complete and detailed view of the network security status, increasing the trustworthiness of the network and its services. The proposed approach builds on an enhanced certification model capturing the evolution of the system over time. It also defines certification services that fully integrate with existing networks to collect evidence on the target of certification and carry out the certification process. It finally proposes two certification processes, centralized and decentralized, balancing the impact on the network and the system performance. Efficiency, performance, and soundness of our approach are experimentally evaluated in a simulated Named Data Networking (NDN) network targeting property availability.