This article aims to investigate the problem of security and privacy in the fintech business, since the use of digital technologies has increased in last few years. In this context, we conducted a survey of fintech companies to understand how they are addressing the security and privacy issue and what are the potential risks and benefits for consumers. According to the results, fintech companies are exploiting a range of technical, regulatory, and compliance measures to ensure the security and privacy of financial data, such as encryption, access controls, and data governance policies. However, we attempted to pinpoint open issues as well as prospective research directions. In such settings, there is a need to better understand the efficiency of different privacy and security measures, as well as the potential risks and advantages for customers while they are using fintech services. This article recommends that future research broaden its scope and delve further into the security and privacy issues of specific types of fintech services.

Reconfigurable intelligent surfaces (RISs) are a new and emerging technology that has the potential to revolutionize the way that wireless communication systems are designed and implemented. These surfaces are made up of a large number of small, individually controllable elements, each of which can be used to manipulate the phase and amplitude of the electromagnetic waves that pass through it. This allows them to perform a wide range of functions, including wireless power transfer, beamforming, and cloaking. In this work, we provide an overview of the principles and applications of reconfigurable intelligent surfaces, and we discuss the advantages and challenges of these surfaces in wireless communications. We also provide an overview of the current state of research in this area. Moreover, we outline the future directions for the development of these surfaces.

Unmanned Aerial Vehicles (UAVs) have become integral to modern consumer electronics (CE) ecosystem-based smart city infrastructures, providing dynamic connectivity, environmental monitoring, and situational awareness through distributed consumer Internet of Things (IoT) networks. However, their reliance on wireless communication links exposes them to a wide range of cyber and physical-layer attacks, demanding intelligent yet lightweight defense mechanisms. This paper presents a Multi-Agent Federated Edge Intrusion Detection System (UAV-IDS) designed to ensure privacy-preserving, real-time protection for UAV-enabled smart city IoT environments. The proposed framework integrates a hybrid Autoencoder–LSTM architecture deployed across edge nodes, where multiple intelligent agents collaboratively perform feature extraction, anomaly detection, and policy adaptation. A UAV-based coordinator manages federated learning rounds, aggregating model updates from distributed edge nodes without sharing raw data. Extensive experiments conducted on the T-ITS dataset demonstrate that the proposed system achieves 99.44% accuracy, 99.31% precision, and a 46.8 ms average detection latency, outperforming existing deep and federated IDS frameworks. Moreover, the lightweight deployment (8.42 MB) and low communication cost confirm the feasibility of the proposed design for embedded UAV operations. The results validate the framework’s potential as an adaptive, scalable, and privacy-conscious solution for next-generation smart city security infrastructures.

Unmanned Aerial Vehicles (UAV) represent a new generation of intelligent solutions that improve productivity and safety in agriculture, security, and healthcare services. However, they are more prone to cyber-attacks such as data manipulation, spoofing, vague attacks, and false data injection due to increasing integration of Cyber-Physical Systems. This research proposes an anomaly-based intrusion detection system (IDS) for UAVs with real-time cognition of cyberspace and physical presence using Machine Learning (ML) algorithms to achieve strong performance metrics. The feature set includes both cyber-object characteristics (such as network traffic and IP addresses) and physical-object characteristics (such as sensor data), collected under both normal and adversarial conditions. This data is used to train and evaluate the proposed approach. Prior to training, exploratory data analysis, normalization, and data balancing using the Synthetic Minority Oversampling Technique (SMOTE) were performed to maximize the efficiency of the feature space. A well-known cyber-physical dataset, T_ITS, was used in this process. The results show that high-quality preprocessing significantly improves key performance metrics such as accuracy, precision, recall, and F1-score. Among the classifiers, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were identified as the top performers achieving an accuracy of 99.18%. These results emphasize the importance of robust IDS frameworks in securing UAV operations against rising threat of cyber-attacks. Received: 9 April 2025 | Revised: 30 July 2025 | Accepted: 10 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://doi.org/10.1109/TITS.2023.3339728, reference number [1]. The data that support the findings of this study are openly available at https://dx.doi.org/10.21227/6f22-py65, reference number [43]. Author Contribution Statement Hafiz Muhammad Attaullah: Conceptualization, Methodology, Software, Resources, Data curation, Writing – original draft, Writing – review & editing. Muhammad Harris: Conceptualization, Methodology, Software, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Inam Ullah Khan: Methodology, Resources, Data curation, Writing – review & editing, Visualization. Muhammad Mansoor Alam: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Project administration. Mazliham Mohd Su'ud: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration.

Unmanned Aerial Vehicles are increasingly deployed in consumer applications such as logistics, disaster recovery, and surveillance, yet their wireless communication links remain highly vulnerable to cyber-attacks. Traditional intrusion detection systems (IDS) often struggle in UAV environments due to resource constraints, dynamic network conditions, and scarcity of labeled datasets. To address these challenges, we propose a Generative AI-enabled lightweight IDS framework tailored for UAV communication networks. The framework integrates hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) autoencoders with generative augmentation and knowledge distillation, achieving high accuracy while maintaining computational efficiency. Explainability is incorporated via SHapley Additive exPlanations (SHAP) analysis to ensure trustworthy and interpretable decision-making. Experimental evaluations on a multimodal UAV dataset demonstrate state-of-the-art performance with 99.49% accuracy, 99.48% recall, and AUROC of 0.9999, alongside a student model that reduces model size, inference latency, and memory footprint by nearly 50%. Comparative results confirm that the proposed framework outperforms recent IDS baselines in both detection capability and lightweight deployment, offering a practical and scalable solution for next-generation UAV communication networks.

The growing deployment of uncrewed aerial vehicles (UAV) in autonomous and networked missions has heightened their exposure to both cyber and cyber-physical attacks, underscoring the need for intelligent and lightweight intrusion detection systems (IDS) solutions. This study introduces RoboLSTM-IDS, a deep anomaly-based framework that combines robust feature engineering with temporal sequence modeling for UAV network security. Leveraging Robust Optimization-Based Tabular Feature Engineering (ROBOTa), a robust optimization-based feature selection technique—the system extracts stable, high-impact features from complex UAV telemetry and communication data. These are modeled using a Long Short-Term Memory network to capture sequential attack dynamics. Comprehensive experiments conducted on five benchmark datasets, including real-world UAV cyber-physical data (T-ITS), CICIDS-2017, UNSW-NB15, and their CTGAN-augmented variants, demonstrate that RoboLSTM-IDS consistently outperforms traditional machine learning and deep learning baselines. It achieves up to 99.62% accuracy and 0.997 AUC, while maintaining low false positive rates and real-time execution performance. Unlike conventional IDS models that are computationally heavy, proposed model achieves a 6× smaller model size, 3× lower memory footprint, and significantly reduced inference latency. These results confirm RoboLSTM-IDS as an effective and scalable IDS solution tailored for next-generation UAV ecosystems.