The explosive growth of the Internet of Things (IoT) demands security mechanisms that adapt to emerging threats. Telemetry meets this need by fusing federated learning, explainable AI, and privacy-preserving analytics into a multi-layer monitoring framework. Lightweight agents such as r-Monitoring impose only 0.27 % CPU overhead at device level, while the BACON federated anomaly detector safeguards system traffic. On a 21-sensor industrial robot, TELEMETRY's Nokia pipeline flagged subtle speed anomalies with 73% accuracy; within the NF-ToN-IoT corpus, BACON differentiated benign from malicious flows with 96% accuracy and markedly fewer false positives than signature baselines. The Misuse Detection Toolkit ensemble further achieved 97.7% validation accuracy across 50 training epochs, underscoring the framework's adaptability. Together, these layers cut detection latency and reduce on-device resource use, illustrating how ML-driven, federated monitoring can harden next-generation IoT deployments. Ongoing work explores graph-based analytics, adaptive models, and more scalable federation schemes.

The authors of the article examined the modern challenges involved in developing an access control system for smart factories, focusing on the creation of an integrated solution for monitoring and managing the information security of the Internet of Things (loT) through the integration of diverse tools and methods within a single system. In this paper, the information system is examined through the lens of system analysis, focusing on the interactions between the subjects and objects within the system. This perspective allows for an accurate assessment of the current state of obj ects, taking into account the system's architecture and its vulnerabilities, as well as the evolution of the system state over time. The approach proposed incorporates modern tools and software solutions, including intrusion detection systems (IDS), fuzzy testing, Software Bill of Materials (SBOM), and various machine learning (ML) techniques.

This paper describes challenges within IoT ecosystems from the perspective of cybersecurity testing along with a proposed approach to address them that will be investigated in a recently started Horizon Europe project named TELEMETRY. The key observations regarding the design of the framework are summarised as follows. There is a need to consider the full lifecycle of IoT components – at their design time, their integration into systems, and operation of those systems. Threats and risks can propagate when components are connected together in systems - vulnerabilities in one component can affect other components in a system. IoT devices present limitations to current testing and management due to geographical distribution, opacity and limited processing power. Risk assessment fulfils an important requirement because it enables assessment of what elements are important to the system’s stakeholders, how these elements may be compromised, and how the compromises may be controlled. Feedback from operational monitoring of IoT devices can inform firmware updates / patches to the devices but there is a significant challenge in rolling out these patches to multiple low-power devices geographically distributed

ERATOSTHENES is an EC, co-funded, research project strongly considering modern security challenges in the domain of Internet of Things in mind of their huge penetration into our day to day lives. There are a series of recent challenges that recently have been converted into obstacles or risk points that could block the secure operation of IoT networks in all day to day activities, from home to office, to leisure and security. These include examples such as the highly increased number of connected devices (at all network levels) that are on top forming inhomogeneous networks and systems of systems. Different vendor characteristics further increase the attack surface that is expected to further rise in the upcoming years. Such, highly critical, characteristics, dramatically increase the needs for confidentiality access control, user and things’ privacy, devices’ trustworthiness and compliance that require lifecycle considerations. The ERATOSTHENES project orchestrates a novel distributed, automated, auditable, yet privacy-respectful, Trust and Identity Management Framework and Reference Architecture with the ultimate scope to dynamically and holistically manage IoT devices in a lifecycle approach, strengthening trust, identities, and resilience in the entire IoT ecosystem while supporting the enforcement of the NIS directive, GDPR and Cybersecurity Act. This publication describes the ERATOSTHENES technical concept and reference architecture as well as design considerations, architecture characteristics, connectivity and interoperability.