The H2020 European research project GHOST — Safe-Guarding Home IoT Environments with Personalised Real-time Risk Control — aims to deploy a highly effective security framework for IoT smart home residents through a novel reference architecture for user-centric cyber security in smart homes providing an unobtrusive and user-comprehensible solution. The aforementioned security framework leads to a transparent cyber security environment by increasing the effectiveness of the existing cyber security services and enhancing system's self-defence through disruptive software-enabled network security solutions. In this paper, GHOST security framework for IoT-based smart homes is presented. It is aiming to address the security challenges posed by several types of attacks, such as network, device and software. The effective design of the overall multi-layered architecture is analysed, with particular emphasis given to the integration aspects through dynamic and re-configurable solutions and the features provided by each one of the architectural layers. Additionally, real-life trials and the associated use cases are described showcasing the competences and potential of the proposed framework.

This paper investigates the contribution of the various cybersecurity domains to the United Nations’ Sustainable Development Goals (SDGs), emphasizing the critical role of cybersecurity in advancing sustainable economic growth and resilient IoT infrastructure. The paper also examines specific use cases on how cybersecurity measures and practices can contribute to achieving SDG 8 and SDG 9 focused on decent work and economic growth and industry, innovations, and infrastructure. In the context of SDG 8 the use case of a smart agriculture network was examined, whereas for SDG 9, the use case focuses on a smart factory processing raw materials. An analysis of the prioritization of the several cybersecurity domains following the MoSCoW method is also presented. This paper offers valuable insights and guidance for enhancing corporate resilience and economic benefits in the Internet of Things (IoT) aligning with the SDGs and contributing to a more sustainable and resilient future for the IoT.

Automated X-ray inspection is crucial for efficient and unobtrusive security screening in various public settings. However, challenges such as object occlusion/overlap, variations in the physical properties of the items of interest, diversity in the types of X-ray scanning devices used, and limited training data hinder accurate and reliable detection of illicit items. Despite the large body of research works in the field, the reported experimental evaluation is often incomplete, while the derived outcomes are frequently conflicting. In order to shed light on the research landscape of this field and to facilitate further research, a systematic, detailed, and thorough comparative evaluation study of recent Deep Learning (DL)-based methods for X-ray object detection is conducted in this work. For achieving this, a comprehensive evaluation framework is developed, composed of the following building blocks: a) Six of the most recent, large-scale and widely used public datasets for X-ray illicit item detection (namely, OPIXray, CLCXray, SIXray, EDS, HiXray, and PIDray), b) Ten different state-of-the-art object detection schemes, covering all main categories present in the literature, including generic Convolutional Neural Network (CNN), custom (X-ray-specific) CNN, generic transformer and generic hybrid CNN-transformer architectures, and c) Various detection (mAP⁵⁰ and mAP^50:95 mean Average Precision (mAP)) and time/computational-complexity (inference time (ms), parameter size (M), and computational load (GFLOPS)) performance metrics. A thorough analysis of the computed experimental results leads to the extraction of critical observations and detailed insights, emphasizing on the following key aspects: a) Overall behavior of the various object detection schemes, b) Object-level detection performance investigation, c) Dataset-specific observations, d) Statistical variance and empirical reliability examination, and e) Time efficiency and computational complexity analysis. In order to support reproducibility of the reported experimental results and to promote research in the field, the evaluation framework code and model weights are publicly available at https://github.com/jgenc/xray-comparative-evaluation, while a broad set of precomputed results (for all employed detectors and datasets) can be found at https://jgenc.github.io/ceasefire_demo/#/.

In the face of increasingly sophisticated and dynamic cyber threats, policy-based cybersecurity deployment has emerged as a critical strategy for securing modern, complex network infrastructures. This approach enables organizations to enforce adaptable, scalable and context-aware security policies that can dynamically respond to evolving threats and operational demands. However, the growing complexity of distributed systems and the interdependencies between diverse security components present significant challenges in ensuring consistent and efficient policy enforcement. Static and centralized security models are no longer sufficient to address these challenges, underscoring the need for intelligent, distributed solutions that can optimize policy deployment in real-time. This paper introduces a novel framework based on Message Passing algorithms that enables decentralized, adapted and optimized policy deployment across complex cybersecurity infrastructures. More specifically, we extend the traditional Min-Sum algorithm by incorporating dynamic trust weights that influence policy enforcement decisions, allowing more reliable nodes to have a greater impact while mitigating the risks posed by untrusted entities. The proposed framework is designed to handle large-scale network environments with minimal computational overhead, relying on localized message exchanges instead of centralized computations. We benchmark our framework against state-of-the-art approaches, demonstrating that the proposed framework achieves lower total enforcement cost while significantly improving convergence speed, particularly in heterogeneous trust environments. Finally, we provide an in-depth evaluation of how trust heterogeneity influences security policy enforcement. Extensive simulations were conducted to evaluate the framework’s effectiveness in optimizing policy deployment for varying network topologies, demonstrating significant improvements in threat detection accuracy, policy consistency and resource efficiency across diverse and dynamic network environments.

Consider a random network of static primary wireless sources and a co-located network of secondary wireless devices. The channel coefficients between the two networks are assumed to be known to the secondary users (SUs), e.g., using radio environment maps (REM). However, the operational state of the sources is unknown due to intermittency. In this paper, we study the performance of primary source detection by SUs using a message-passing algorithm. Additionally, we employ methods from statistical mechanics, in particular, the Replica approach, to obtain analytic results for the performance of such networks in the large system-size limit. We test the results through a large-scale simulation analysis, obtaining good agreement. The proposed method provides a simple way to evaluate the performance of the system and assess how it depends on the macroscopic parameters that characterize it, such as the average density of SUs and sources and the signal-to-noise ratio. The main contribution of this paper is the application of an algorithm that quantitatively predicts the parameter value region for which accurate and reliable detection of the operational state of the primary sources can be achieved in a fast and decentralized manner.

Without a doubt, the security of Internet of Things (IoT) systems is of crucial importance. The use of such systems has significantly increased in the recent year, where in every aspect of our daily life we interact with IoT environments and devices of any type. Therefore, it comes with no surprise that the relevant security concerns have attracted the focus of the security community and there is a rising need for security solutions in the IoT domain. GHOST is an EU Horizon 2020 Research and Innovation funded project, aiming at developing a reference architecture for securing smart-home IoT ecosystems. One of the approaches employed in GHOST project is to model the behavior of the IoT devices with regard to the network activity with the aim to detect and following mitigating cyber-security events. This functionality is mainly provided by two GHOST system modules, that is the Network and Data Flow Analysis (NDFA) and the Profile Builder (PB), described in detail in this paper.