This paper presents a lightweight neural architecture for efficient direction-of-arrival (DoA) estimation, built upon a novel Ghost Squeeze-and-Excitation Residual Block (GSERB). The proposed block integrates Squeeze-and-Excitation (SE) attention directly into the Ghost feature generation process, enabling selective channel enhancement while minimizing redundant feature computation. Unlike conventional attentionaugmented convolutional networks, the GSERB amplifies informative ghost feature channels prior to fusion with intrinsic representations, thereby improving feature discrimination and representational efficiency. Experimental results showcase that this integration enhances the model's ability to capture spa-tial-spectral dependencies while maintaining a compact design. The resulting network, named GSERB-DoANet, achieves an effective balance between accuracy, scalability, and computational efficiency for real-time multi-source DoA estimation in wireless sensing applications.

This paper explores the application of Temporal Fusion Transformers (TFTs) to target recognition and multi-horizon tracking, with a particular emphasis on enhancing Direction of Arrival (DoA) estimation in complex signal environments. By leveraging the advanced temporal modeling capabilities and dynamic feature selection mechanisms inherent to the TFT architecture, we adapt and optimize the model for improved performance in antenna signal processing, leading to a more accurate and robust estimation framework. Our analysis underscores the architectural strengths of TFTs, especially their capacity to model long-range temporal dependencies and to dynamically prioritize sensor-derived features—capabilities that are essential for precise target tracking and recognition. We train and evaluate the model under diverse conditions, including multi-horizon forecasting and varying prediction latencies across models. The proposed method is benchmarked against state-of-the-art algorithms, including recurrent neural networks and standard transformer-based models. Extensive experimental results demonstrate that the TFT-based approach consistently outperforms existing techniques in forecasting accuracy, multi-target recognition, and computational complexity, highlighting its potential to advance the field of DoA estimation.

Malware detection is increasingly challenged by evolving techniques like obfuscation and polymorphism, limiting the effectiveness of traditional methods. Meanwhile, the widespread adoption of software containers has introduced new security challenges, including the growing threat of malicious software injection, where a container, once compromised, can serve as entry point for further cyberattacks. In this work, we address these security issues by introducing a method to identify compromised containers through machine learning analysis of their file systems. We cast the entire software containers into large RGB images via their tarball representations, and propose to use established Convolutional Neural Network architectures on a streaming, patchbased manner. To support our experiments, we release the COSOCO dataset-the first of its kind-containing 3364 largescale RGB images of benign and compromised software containers at https://huggingface.co/datasets/k3ylabs/cosoco-imagedataset. Our method detects more malware and achieves higher F1 and Recall scores than all individual and ensembles of VirusTotal engines, demonstrating its effectiveness and setting a new standard for identifying malware-compromised software containers.

Autonomous Vehicles (AVs) rely on real-time processing of natural images and videos for scene understanding and safety assurance through proactive object detection. Traditional methods have primarily focused on 2D object detection, limiting their spatial understanding. This study introduces a novel approach by leveraging 3D object detection in conjunction with augmented reality (AR) ecosystems for enhanced real-time scene analysis. Our approach pioneers the integration of a synthetic dataset, designed to simulate various environmental, lighting, and spatiotemporal conditions, to train and evaluate an AI model capable of deducing 3D bounding boxes. This dataset, with its diverse weather conditions and varying camera settings, allows us to explore detection performance in highly challenging scenarios. The proposed method also significantly improves processing times while maintaining accuracy, offering competitive results in conditions previously considered difficult for object recognition. The combination of 3D detection within the AR framework and the use of synthetic data to tackle environmental complexity marks a notable contribution to the field of AV scene analysis. • A multimodal architecture for real-time 3D object detection in AV systems. • Efficient 3D bounding box prediction extrapolated from 2D images. • Novel synthetic dataset simulates diverse environmental conditions for AVs. • Comparative evaluation against state-of-the-art techniques for object detection.

The Internet of Things (IoT) mainly consists of a large number of Internet-connected devices. The proliferation of untrusted third-party IoT applications has led to an increase in IoT-based malware attacks. In addition, it is infeasible for the IoT devices to support the sophisticated detection systems due to the restricted resources. Edge computing is considered to be promising. It provides solutions to the data security and privacy leakage brought by untrusted third-party IoT applications. In this article, an intelligent trusted and secure edge computing (ITEC) system is proposed for IoT malware detection. In this system, a signature-based preidentification mechanism is built for matching and identifying the malicious behaviors of untrusted third-party IoT applications. A delay strategy is then embedded into the risk detection engine in order to “buy time” for threat analysis and rate-limit the impact of suspicious third-party IoT applications in the system. We conduct extensive experiments to verify the effectiveness of the ITEC system and show that we can achieve accuracies of up to 98.52%.

In the digital age of the hyper-connected Critical Infrastructures (CIs), the role of the smart electrical grid is crucial, providing several benefits, such as improved grid resilience, efficient energy distribution and smart load and response management. However, despite the several advantages, the rapid evolution of the heterogeneous technologies involved in the smart electrical grid increases the attack surface. In this paper, we focus first our attention on how Artificial Intelligence (AI) can be used to protect the smart electrical grid in terms of detecting efficiently potential cyberattacks and anomalies. Secondly, we investigate how AI can be used to trick AI-enabled detection services, thus resulting in false alarms. In particular, we emphasise on cyberattacks against IEC 60870-5-104, an industrial communication protocol which is widely used in the energy domain. Therefore, a relevant AI-powered Intrusion Detection System (IDS) is provided, utilising strong Machine Learning (ML)/Deep Learning (DL) methods, such as Decision Tree, Random Forest, XGBOOST and deep MultiLayer Perceptron (MLP). On the other hand, we investigate how adversarial attacks can affect the detection performance of the previous IDS. For this purpose, the Fast Gradient Signed Method (FGSM) is examined, and a Conditional Tabular Generative Adversarial Network (CTGAN) adversarial attack generator is implemented. The evaluation results demonstrate the efficiency of the proposed IDS and the aforementioned adversarial attacks.

The revolution of Artificial Intelligence (AI) has brought about a significant evolution in the landscape of cyberattacks. In particular, with the increasing power and capabilities of AI, cyberattackers can automate tasks, analyze vast amounts of data, and identify vulnerabilities with greater precision. On the other hand, despite the multiple benefits of the Internet of Things (IoT), it raises severe security issues. Therefore, it is evident that the presence of efficient intrusion detection mechanisms is critical. Although Machine Learning (ML) and Deep Learning (DL)-based IDS have already demonstrated their detection efficiency, they still suffer from false alarms and explainability issues that do not allow security administrators to trust them completely compared to conventional signature/specification-based IDS. In light of the aforementioned remarks, in this paper, we introduce an AI-powered IDS with explainability functions for the IoT. The proposed IDS relies on ML and DL methods, while the SHapley Additive exPlanations (SHAP) method is used to explain decision-making. The evaluation results demonstrate the efficiency of the proposed IDS in terms of detection performance and explainable AI (XAI).

The evolution of cyberattacks has been significantly impacted by the rise of Artificial Intelligence (AI). In particular, AI-driven attacks leverage Machine Learning (ML) and Deep Learning (DL) methods to automate tasks like identifying vulnerabilities, crafting convincing phishing emails, and evading conventional security measures. These cyberattacks can adapt in real time, making them more elusive and challenging to detect. Furthermore, AI has enabled the development of AI-powered malware that can learn and evolve, making it even more dangerous. As AI continues to evolve, both attackers and defenders are engaged in a relentless arms race, with cybersecurity professionals striving to harness AI for threat detection and response while cybercriminals seek to exploit AI’s capabilities for their malicious purposes. This ongoing battle underscores the need for proactive and adaptive cybersecurity strategies to mitigate the evolving threats posed by AI-driven cyberattacks. Based on the aforementioned remarks, it is evident that efficient and adaptable countermeasures are necessary. In this paper, we focus our attention on Cyber Threat Intelligence (CTI) mechanisms. CTI is the process of collecting, analysing, and sharing information about potential cybersecurity threats to help organisations proactively defend against cyberattacks. In particular, after providing an overview of the CTI use cases, a brief analysis of existing solutions follows, highlighting the current trends and directions for future work in this research field.

Modern Healthcare cyberphysical systems have begun to rely more and more on distributed AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning (ML) and Deep Learning (DL) models for the wide variety of medical fields, while at the same time fortifying the privacy of the sensitive information that are present in the medical sector, makes the FL technology a necessary tool in modern health and medical systems. Unfortunately, due to the polymorphy of distributed data and the shortcomings of distributed learning, the local training of Federated models sometimes proves inadequate and thus negatively imposes the federated learning optimization process and in extend in the subsequent performance of the rest Federated models. Badly trained models can cause dire implications in the healthcare field due to their critical nature. This work strives to solve this problem by applying a post-processing pipeline to models used by FL. In particular, the proposed work ranks the model by finding how fair they are by discovering and inspecting micro-Manifolds that cluster each neural model's latent knowledge. The produced work applies a completely unsupervised both model and data agnostic methodology that can be leveraged for general model fairness discovery. The proposed methodology is tested against a variety of benchmark DL architectures and in the FL environment, showing an average 8.75% increase in Federated model accuracy in comparison with similar work.

Automatic generation of medical reports can provide diagnostic assistance to doctors and reduce their workload. To improve the quality of the generated medical reports, injecting auxiliary information through knowledge graphs or templates into the model is widely adopted in previous methods. However, they suffer from two problems: 1) The injected external information is limited in amount and difficult to adequately meet the information needs of medical report generation in content. 2) The injected external information increases the complexity of model and is hard to be reasonably integrated into the generation process of medical reports. Therefore, we propose an Information Calibrated Transformer (ICT) to address the above issues. First, we design a Precursor-information Enhancement Module (PEM), which can effectively extract numerous inter-intra report features from the datasets as the auxiliary information without external injection. And the auxiliary information can be dynamically updated with the training process. Secondly, a combination mode, which consists of PEM and our proposed Information Calibration Attention Module (ICA), is designed and embedded into ICT. In this method, the auxiliary information extracted from PEM is flexibly injected into ICT and the increment of model parameters is small. The comprehensive evaluations validate that the ICT is not only superior to previous methods in the X-Ray datasets, IU-X-Ray and MIMIC-CXR, but also successfully be extended to a CT COVID-19 dataset COV-CTR.

A Visible Light Positioning (VLP) network planner holds tremendous economic potential in that it permits designing a roll-out within given cost, illuminance and accuracy bounds. In this manuscript, the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm is applied to simultaneously optimise a roll-out's maintained illuminance and positioning error, by varying the placement of the VLP-enabled LED transmitters. With simulations that differ in positioning and/or environment parameters, the important illuminance-positioning trade-off is revealed. The corresponding Pareto fronts and LED arrangements are studied. Guidelines regarding where to place the LEDs and which LEDs to select for positioning are provided.

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.

Critical energy infrastructure (CEI) systems are vital to underpin the national economy and social development, but vulnerable to cyber attack and data privacy leakage when distributed machine learning technologies are deployed on them. Although federated learning (FL) has promoted distributed collaborative learning while keeping natural compliance with the privacy protection, it is tremendously difficult to schedule edge nodes of CEI collaboratively when asynchronous FL tasks are applied in CEI system, since the CEI system must make an irrevocable immediate decision on whether to hire a participant who arrives and departs dynamically without knowing future information. In this article, we tackle this issue by designing fairness-aware and time-sensitive task allocation mechanisms in asynchronous FL for CEI. First, we design an optimal multidimensional contract to guarantee the reliability, honesty, and fairness, and maximize the learning accuracy for the fixed deadline scenario. Second, we design a multimetric participant recruitment mechanism to control time consumption for the limited budget scenario, prove that the problem of optimizing this mechanism is NP-hard, and propose an$e$-approximation algorithm accordingly. Finally, extensive experiments using both real-world data and simulated data further demonstrate the effectiveness and efficiency of our proposed mechanisms compared to the state-of-the-art approaches.

The rise of the Internet of Medical Things introduces the healthcare ecosystem in a new digital era with multiple benefits, such as remote medical assistance, realtime monitoring, and pervasive control.However, despite the valuable healthcare services, this progression raises significant cybersecurity and privacy concerns.In this article, we focus our attention on the IEC 60 870-5-104 protocol, which is widely adopted in industrial healthcare systems.First, we investigate and assess the severity of the IEC 60 870-5-104 cyberattacks by providing a quantitative threat model, which relies on Attack Defence Trees and Common Vulnerability Scoring System v3.1.Next, we introduce an intrusion detection and prevention system (IDPS), which is capable of discriminating and mitigating automatically the IEC 60 870-5-104 cyberattacks.The proposed IDPS takes full advantage of the machine learning (ML) and software defined networking (SDN) technologies.ML is used to detect the IEC 60 870-5-104 cyberattacks, utilizing 1) Transmission Control Protocol/Internet Protocol network flow statistics and 2) IEC 60 870-5-104 payload flow statistics.

The integration of advanced sensing, signal processing, deep learning, and edge computing into vehicles is enabling intelligent automated vehicles that can navigate autonomously in various environments. There are several exciting developments in new technologies that may contribute to the improvement of the robustness of autonomous vehicles and thus making them safer on the road. However, the development of suitable context prediction methodologies in order to provide proactive behavior for intelligent transportations remains a challenge. The reason is that future context information, hidden in the raw context traces left by users in the real world, is not immediately accessible to applications. Therefore, sophisticated context prediction approaches are required that could discover and mine patterns (e.g., of a driver’s behavior) from observed context history. The major challenge of a context prediction approach is in the prediction accuracy and prediction expressiveness. Neural networks along with deep-learning methods have shown noticeably better performance in comparison with previous methods regarding the accuracy of the outcomes. However, deep learning also issues more complexity and interpretability problems and, hence, arises serious challenges regarding the verifiability of these approaches. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for context prediction of autonomous vehicles.

The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. The objective of the optimization problem is to obtain a feasible solution that minimizes the total power consumption of the WSN, when the error probability at the fusion center meets certain criteria. This work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing to the power allocation of decentralized detection. More specifically, we apply and compare four algorithms designed to tackle Large scale global optimization (LSGO) problems. These are the memetic linear population size reduction and semi-parameter adaptation (MLSHADE-SPA), the contribution-based cooperative coevolution recursive differential grouping (CBCC-RDG3), the differential grouping with spectral clustering-differential evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive differential evolution (EADE). To the best of the authors knowledge, this is the first time that LSGO algorithms are applied to the optimal power allocation problem in IoT networks. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.

In recent years and with the advancement of IoT networks, malicious intrusions aiming at disrupting the services and getting access to confidential information in medical environments is ever progressing. To that end, this paper proposes a Federated Layered Architecture to be used in Medical Cyber-Physical Systems (MCPS) Networks that entails the creation of multiple aggregation layers to induce further security to the model training process. Moreover, two Deep Adversarial Neural Networks (GANs) are presented for use with data found in the MCPS environment. The evaluation of the presented work showed that the models trained in the Federated system have an increase in their ability to detect possible intrusions in the MCPS network than the commonly trained models.

In the next generation of wireless communication networks, Massive MIMO remains the appropriate technology to provide higher throughput to the users with incredible gains in terms of power consumption and energy efficiency. However, current research rarely considers the electromagnetic field exposure during the network design phase. In this paper, we propose a simulation-based method that enables an optimal design of the massive MIMO 5G networks with respect to both power consumption and electromagnetic field exposure. The results of the simulations show that the massive MIMO network achieves the same performance in terms of users coverage with 10 times less power consumption and an electric field strength 17 times weaker in comparison with the traditional 4G network.

Interoperability is one of the most challenging problems in modern cross-organizational information systems, which rely on heterogeneous information and process models. Interoperability becomes very important for e-Government information systems that support cross-organizational communication especially in a cross-border setting. The main goal in this context is to seamlessly provide integrated services to the user (citizen). In this paper we focus on Pan European e-Services and issues related with their integration. Our analysis uses basic concepts of the generic public service model of the Governance Enterprise Architecture (GEA) and of the Web Service Modeling Ontology (WSMO), to express the semantic description of the e-services. Based on the above, we present a mediation infrastructure capable of resolving semantic interoperability conflicts at a pan-European level. We provide several examples to illustrate both the need to solve such semantic conflicts and the actual solutions we propose.