TELEMETRY Publication on Machine Learning-Based Trust Aggregation for IoT Systems

At the Workshop on Whole-Lifecycle Security for Smart Systems: Methods and Tools (LIFESEC), held in the framework of the 11th IEEE International Conference on Smart Computing (SmartComp 2025), TELEMETRY partners presented the paper Machine Learning Based Trust Aggregation for IoT Systems.

The paper was authored by Pasindu Manisha Kuruppuarachchi, Alan McGibney, Susan Rea and Bernd-Ludwig Wenning.

Abstract

IoT systems consist of multiple heterogeneous sensors, actuators, and control logic. These systems not only collect data from the physical world around us, they play a critical role in supporting decision-making processes. Trust is essential in this context, as decision-makers must rely on the system’s ability to perform its assigned tasks reliably. To ensure system-level trust, a trust analyser is implemented to assess the trustworthiness of IoT systems across seven distinct evaluation categories. Various tools and techniques can be applied within these categories, and the accuracy of each tool must be considered when aggregating trust evaluations to produce a representative trust score. To address this, several machine learning based aggregation methods are explored and compared, including Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN), and the Tsetlin Machine (TM). ANN and TM achieved 98% accuracy in correctly detecting trust attacks, while ANFIS achieved 76% accuracy. In addition, both ANFIS and TM offer interpretability, providing valuable insight into how they detect and flag attacks within the IoT system.

You can access the full paper here.

European Cyber Security Community Initiative (ECSCI)

The European Cyber Security Community Initiative (ECSCI) brings together EU-funded cybersecurity research and innovation projects to foster cross-sector collaboration and knowledge exchange. Its aim is to align technical and policy efforts across key areas such as AI, IoT, 5G, and cloud security. ECSCI organizes joint dissemination activities, public workshops, and strategic dialogue to amplify the impact of individual projects and build a more integrated European cybersecurity landscape.

Supported by the European Commission, ECSCI contributes to shaping a shared vision for cybersecurity in Europe by reinforcing connections between research, industry, and public stakeholders.

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European Cluster for Cybersecurity Certification

The European Cluster for Cybersecurity Certification is a collaborative initiative aimed at supporting the development and adoption of a unified cybersecurity certification framework across the European Union. Bringing together key stakeholders from industry, research, and national authorities, the cluster facilitates coordination, knowledge exchange, and alignment with the EU Cybersecurity Act.

Its mission is to contribute to a harmonized approach to certification that fosters trust, transparency, and cross-border acceptance of cybersecurity solutions. The cluster also works to build a strong stakeholder community that can inform and support the work of the European Union Agency for Cybersecurity (ENISA) and the future European cybersecurity certification schemes.

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CertifAI

CertifAI is an EU-funded project aimed at enabling organizations to achieve and maintain compliance with key cybersecurity standards and regulations, such as IEC 62443 and the EU Cyber Resilience Act (CRA), across the entire product development lifecycle. Rather than treating compliance as a one-time activity or post-development task, CertifAI integrates compliance checks and evidence collection as continuous, embedded practices within daily development and operational workflows.

The CertifAI framework provides structured, practical guidance for planning, executing, and monitoring compliance assessments. It supports organizations in conducting gap analyses, building compliance roadmaps, collecting evidence, and preparing for formal certification. The methodology leverages best practices from established cybersecurity frameworks and aligns with Agile and DevSecOps principles, enabling continuous and iterative compliance checks as products evolve.

A central feature of CertifAI is the use of automation and AI-driven tools—such as Retrieval-Augmented Generation (RAG) systems and Explainable AI—to support the interpretation of complex requirements, detect non-conformities, and generate Security Assurance Cases (SAC) with traceable evidence. The approach is organized into five main phases: preparation and planning, evidence collection and mapping, assessment execution, reporting, and ongoing compliance monitoring.

CertifAI’s methodology is designed to be rigorous yet adaptable, offering organizations a repeatable process to proactively identify, address, and document compliance gaps. This supports organizations not only in meeting certification requirements, but also in embedding a culture of security and compliance into daily practice.

Ultimately, CertifAI’s goal is to make compliance and security assurance continuous, transparent, and integrated, helping organizations efficiently prepare for certification while strengthening their overall cybersecurity posture.

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DOSS

The Horizon Europe DOSS – Design and Operation of Secure Supply Chain – project aims to improve the security and reliability of IoT operations by introducing an integrated monitoring and validation framework to IoT Supply Chains.

DOSS elaborates a “Supply Trust Chain” by integrating key stages of the IoT supply chain into a digital communication loop to facilitate security-related information exchange. The technology includes security verification of all hardware and software components of the modelled architecture. A new “Device Security Passport” contains security-relevant information for hardware devices and their components. 3rd party software, open-source applications, as well as in-house developments are tested and assessed. The centrepiece of the proposed solution is a flexibly configurable Digital Cybersecurity Twin, able to simulate diverse IoT architectures. It employs AI for modelling complex attack scenarios, discovering attack surfaces, and elaborating the necessary protective measures. The digital twin provides input for a configurable, automated Architecture Security Validator module which assesses and provides pre-certification for the modelled IoT architecture with respect of relevant, selectable security standards and KPIs. To also ensure adequate coverage for the back end of the supply chain the operation of the architecture is also be protected by secure device onboarding, diverse security and monitoring technologies and a feedback loop to the digital twin and actors of the supply chain, sharing security-relevant information.

The procedures and technology will be validated in three IoT domains: automotive, energy and smart home.

The 12-member strong DOSS consortium comprises all stakeholders of the IoT ecosystem: service operators, OEMs, technology providers, developers, security experts, as well as research and academic partners.

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EMERALD: Evidence Management for Continuous Compliance as a Service in the Cloud

The EMERALD project aims to revolutionize the certification of cloud-based services in Europe by addressing key challenges such as market fragmentation, lack of cloud-specific certifications, and the increasing complexity introduced by AI technologies. At the heart of EMERALD lies the concept of Compliance-as-a-Service (CaaS) — an agile and scalable approach aimed at enabling continuous certification processes in alignment with harmonized European cybersecurity schemes, such as the EU Cybersecurity Certification Scheme for Cloud Services (EUCS).

By focusing on evidence management and leveraging results from the H2020 MEDINA project, EMERALD will build on existing technological readiness (starting at TRL 5) and push forward to TRL 7. The project’s core innovation is the development of tools that enable lean re-certification, helping service providers, customers, and auditors to maintain compliance across dynamic and heterogeneous environments —including Cloud, Edge, and IoT infrastructures.

EMERALD directly addresses the critical gap in achieving the ‘high’ assurance level of EUCS by offering a technical pathway based on automation, traceability, and interoperability. This is especially relevant in light of the emerging need for continuous and AI-integrated certification processes, as AI becomes increasingly embedded in cloud services.

The project also fosters strategic alignment with European initiatives on digital sovereignty, supporting transparency and trust in digital services. By doing so, EMERALD promotes the adoption of secure cloud services across both large enterprises and SMEs, ensuring that security certification becomes a practical enabler rather than a barrier.

Ultimately, EMERALD’s vision is to provide a robust, flexible, and forward-looking certification ecosystem, paving the way for more resilient, trustworthy, and user-centric digital infrastructures in Europe.

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SEC4AI4SEC

Sec4AI4Sec is a project funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101120393.

This project aims to create a range of cutting-edge technologies, open-source tools, and new methodologies for designing and certifying secure AI-enhanced systems and AI-enhanced systems for security. Additionally, it will provide reference benchmarks that can be utilized to standardize the evaluation of research outcomes within the secure software research community.

The project is divided into two main phases, each with its own name.

·       AI4Sec – stands for using artificial intelligence in security. Democratize security expertise with an AI-enhanced system that reduces development costs and improves software quality. This part of the project improves via AIs the secure coding and testing.

·       Sec4AI –  involves AI-enhanced systems. These systems also have risks that make them vulnerable to new security threats unique to AI-based software, especially when fairness and explainability are essential.

The project considers the economic and technological impacts of combining AI and security.

The economic phase of the project focuses on leveraging AI to drive growth, productivity, and competitiveness across industries. It includes developing new business models, identifying new market opportunities, and driving innovation across various sectors. 

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