IEEE Open Journal of the Computer Society最新文献

{"title":"Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection","authors":"Safa Ben Atitallah;Maha Driss;Wadii Boulila;Anis Koubaa","doi":"10.1109/OJCS.2025.3587486","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587486","url":null,"abstract":"The Industrial Internet of Things (IIoT) faces significant cybersecurity threats due to its ever-changing network structures, diverse data sources, and inherent uncertainties, making robust intrusion detection crucial. Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. To address these limitations, we propose the Fuzzy Graph Attention Network (FGATN), a novel intrusion detection framework that fuses fuzzy logic, graph attention mechanisms, and GNNs to deliver high accuracy and robustness in IIoT environments. FGATN introduces three core innovations: (1) fuzzy membership functions to explicitly model uncertainty and imprecision in traffic features; (2) fuzzy similarity-based graph construction with adaptive edge pruning to build meaningful graph topologies that reflect real-world communication patterns; and (3) an attention-guided fuzzy graph convolution mechanism that dynamically prioritizes reliable and task-relevant neighbors during message passing. We evaluate FGATN on three public intrusion datasets, Edge-IIoTSet, WSN-DS, and CIC-Malmem-2022, achieving accuracies of 99.07%, 99.20%, and 99.05%, respectively. It consistently outperforms state-of-the-art GNN (GCN, GraphSAGE, FGCN) and deep learning models (DNN, GRU, RobustCBL). Ablation studies confirm the essential roles of both fuzzy logic and attention mechanisms in boosting detection accuracy. Furthermore, FGATN demonstrates strong scalability, maintaining high performance across a range of varying graph sizes. These results highlight FGATN as a robust and scalable solution for next-generation IIoT intrusion detection systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1065-1076"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning With Dynamic Feature Reweighting","authors":"Jialei Cao;Wenxia Zheng;Yao Ge;Jiyuan Wang","doi":"10.1109/OJCS.2025.3587001","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587001","url":null,"abstract":"Financial fraud detection systems confront the persistent challenge of concept drift, where fraudulent patterns evolve continuously to evade detection mechanisms. Traditional rule-based methods and static machine learning models require frequent manual updates, failing to autonomously adapt to emerging fraud strategies. This article presents DriftShield, a novel adaptive fraud detection framework that addresses these limitations through four key technical innovations: (1) the first application of Soft Actor-Critic (SAC) reinforcement learning with continuous action spaces to fraud detection, enabling simultaneous fine-grained optimization of detection thresholds and feature importance weights; (2) a dynamic feature reweighting mechanism that automatically adapts to evolving fraud patterns while providing interpretable insights into changing fraud strategies; (3) an adaptive experience replay buffer combining sliding windows with prioritized sampling to balance catastrophic forgetting prevention with rapid concept drift adaptation; and (4) an entropy-driven exploration framework with automatic temperature tuning that intelligently balances exploitation of known fraud patterns with discovery of emerging threats. Experimental evaluation demonstrates that DriftShield achieves 18% higher fraud detection rates while maintaining lower false positive rates compared to static models. The system demonstrates 57% faster adaptation times, recovering optimal performance within 280 transactions after significant concept drift compared to 650 transactions for the next-best reinforcement learning approach. DriftShield attains a cumulative detection rate of 0.849, representing a 7.7% improvement over existing methods and establishing the efficacy of continuous-action reinforcement learning for autonomous adaptation in dynamic adversarial environments.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1166-1177"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep TPS-PSO: Hybrid Deep Feature Extraction and Global Optimization for Precise 3D MRI Registration","authors":"Gayathri Ramasamy;Tripty Singh;Xiaohui Yuan;Ganesh R Naik","doi":"10.1109/OJCS.2025.3586956","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3586956","url":null,"abstract":"This article presents TPS-PSO, a hybrid deformable image registration framework integrating deep learning, non-linear transformation modeling, and global optimization for accurate inter-subject, intra-modality 3D brain MRI alignment. The method combines a 3D ResNet encoder to extract volumetric features, a Thin Plate Spline (TPS) model to capture smooth anatomical deformations, and Particle Swarm Optimization (PSO) to estimate transformation parameters efficiently without relying on gradients. Evaluated on the BraTS 2022 dataset, TPS-PSO achieved state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 85.7%, Mutual Information (MI) of 1.23, Target Registration Error (TRE) of 3.8 mm, HD95 of 6.7 mm, and SSIM of 0.92. Comparative experiments against five recent baselines confirmed consistent improvements. Ablation studies and convergence analysis further validated the contribution of each module and the optimization strategy. The proposed framework generates topologically plausible deformation fields and shows strong potential for clinical and research applications in neuroimaging.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1090-1099"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking Variants of the Adam Optimizer for Quantum Machine Learning Applications","authors":"Tuan Hai Vu;Vu Trung Duong Le;Hoai Luan Pham;Yasuhiko Nakashima","doi":"10.1109/OJCS.2025.3586953","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3586953","url":null,"abstract":"Quantum Machine Learning is gaining traction by leveraging quantum advantage to outperform classical Machine Learning. Many classical and quantum optimizers have been proposed to train Parameterized Quantum Circuits in the simulation environment, achieving high accuracy and fast convergence speed. However, to the best of our knowledge, currently there is no related work investigating these optimizers on multiple algorithms, which may lead to the selection of suboptimal optimizers. In this article, we first benchmark the most popular classical and quantum optimizers, such as Gradient Descent (GD), Adaptive Moment Estimation (Adam), and Quantum Natural Gradient Descent (QNG), through the Quantum Compilation algorithm. Evaluated metrics include the lowest cost value and the wall time. The results indicate that Adam outperforms other optimizers in terms of convergence speed, cost value, and stability. Furthermore, we conduct additional experiments on multiple algorithms with Adam variants, demonstrating that the choice of hyperparameters significantly impacts the optimizer’s performance.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1146-1154"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Spectrum Coexistence of NR-V2X and Wi-Fi 6E Using Deep Reinforcement Learning","authors":"Kashish D. Shah;Dhaval K. Patel;Brijesh Soni;Siddhartan Govindasamy;Mehul S. Raval;Mukesh Zaveri","doi":"10.1109/OJCS.2025.3586664","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3586664","url":null,"abstract":"The deployment of 5G NR-based Cellular-V2X, i.e., the NR-V2X standard, is a promising solution to meet the increasing demand for vehicular data transmission in the low-frequency spectrum. The high throughput requirement of NR-V2X users can be overcome by extending it to utilize the sub-6GHz unlicensed spectrum, coexisting with Wi-Fi 6E, thus increasing the overall spectrum availability. Most existing works on coexistence rely on rule-based approaches or classical machine learning algorithms. These approaches may fall short in real-time environments where adaptive decision-making is required. In this context, we introduce a novel Deep Reinforcement learning (DRL) based framework for 5G NR-V2X (mode-1 and mode-2) and Wi-Fi 6E coexistence. We propose an algorithm to dynamically adjust the transmission time of the 5G NR-V2X (for mode-1) or Wi-Fi 6E (for mode-2), based on the Wi-Fi and V2X traffic, to maximize the overall throughput of both systems. The proposed algorithm is implemented through extensive simulations using the Network Simulator-3 (ns-3), integrated with a custom Deep Reinforcement Learning (DRL) framework developed using OpenAIGym. This closed-loop integration enables realistic, dynamic interaction between the learning agent and high-fidelity network environments, representing a novel simulation setup for studying NR-V2X and Wi-Fi coexistence. The results show that when employing DRL on NR-V2X and Wi-Fi coexistence, the average data rates for Vehicular User Equipments (VUEs) and Wi-Fi User Equipments (WUEs) improve by <inline-formula><tex-math>$sim$</tex-math></inline-formula>24% and 23%, respectively, as compared to the static method; and even higher improvement when compared to the existing RL-based LTE-V2X and Wi-Fi coexistence approach. Additionally, we analyzed the impact of NR-V2X coexistence on the Wi-Fi subsystem under mode-1 and mode-2 communications. Our findings indicate that mode-1 communication demands more spectrum resources than mode-2, leading to a performance compromise for Wi-Fi.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1133-1145"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DCT-Based Channel Attention for Multivariate Time Series Classification","authors":"Amine Haboub;Hamza Baali;Abdesselam Bouzerdoum","doi":"10.1109/OJCS.2025.3586682","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3586682","url":null,"abstract":"This article introduces a novel DCT-based channel attention (DCA) mechanism for time series classification (TSC) using convolutional neural networks (CNNs). Traditional squeeze-and-excitation (SE) mechanisms rely on global average pooling to model channel-wise interdependencies, which may oversimplify complex temporal dynamics. The proposed DCA model leverages discrete cosine transform (DCT) coefficients to incorporate frequency-domain information, capturing a broader spectrum of temporal features. Two selection criteria are employed to identify the most informative DCT coefficients for constructing the attention map. The first criterion utilizes the lowest frequency coefficients, whereas the second criterion selects the coefficients exhibiting the highest energy to construct the attention map. Comprehensive experiments on twelve diverse TSC datasets demonstrate that DCA consistently outperforms state-of-the-art attention mechanisms, achieving an average improvement of <inline-formula><tex-math>$text{2.2}{%}$</tex-math></inline-formula> in classification accuracy.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1110-1120"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs","authors":"Samir Abdaljalil;Hasan Kurban;Rachad Atat;Erchin Serpedin;Khalid Qaraqe","doi":"10.1109/OJCS.2025.3584942","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3584942","url":null,"abstract":"Detecting anomalies in dynamic graphs is a complex yet essential task, as existing methods often fail to capture long-term dependencies required for identifying irregularities in evolving networks. We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</small>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. Anomalies are detected using reconstruction errors generated by an AutoEncoder, enabling the framework to robustly uncover deviations across time. Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. Comprehensive evaluations on four real-world datasets demonstrate that <sc>T-StructGAD</small> consistently outperforms 12 state-of-the-art unsupervised anomaly detection models, showcasing its superior ability to detect complex anomalies in evolving graphs. This work advances anomaly detection in dynamic graphs by integrating deep learning techniques to address structural and temporal irregularities in a more effective manner.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1100-1109"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11068181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Survey on the Usage of Machine Learning to Detect False Data Injection Attacks in Smart Grids","authors":"Kiara Nand;Zhibo Zhang;Jiankun Hu","doi":"10.1109/OJCS.2025.3585248","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3585248","url":null,"abstract":"This article provides a comprehensive survey on the application of machine learning techniques for detecting False Data Injection Attacks (FDIA) in smart grids. It introduces a novel taxonomy categorizing detection methods based on key criteria such as AC and DC systems, performance metrics, bus size, algorithm selection, and specific subcategories of detection problems. The proposed taxonomy highlights the utility of Graph Neural Networks, autoencoders, and federated learning in addressing sub-problems like privacy preservation, generalized detection, locational detection, and attack classification. The survey underscores the importance of realistic, publicly accessible datasets and enhanced attack simulation techniques. Future research directions are suggested to further the development of robust FDIA detection methods in smart grids.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1121-1132"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11063250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Squeeze-Excitation Transformer With Residual Bi-GRU Model for Distributed UWB Based Continuous Gesture Recognition and its Application to Human-UAV Interactions","authors":"Chih-Lyang Hwang;Felix Gunawan;Chih-Han Chen","doi":"10.1109/OJCS.2025.3584205","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3584205","url":null,"abstract":"Attributable to the random features of wireless signal, different environments, user areas, and variabilities in user gestures, wireless gesture recognition becomes more formidable. In this work, a continuous wireless gesture recognition developed by integrating distributed ultrawideband network (DUWBN) and squeeze-excitation transformer with residual bi-gate recurrent unit (SE-T-RB-GRU) model can tackle the above difficulties. It presents distinguished improvements in processing continuous data streams for real-time applications. The details of model training, optimization strategies, and data preprocessing techniques are presented to improve the performance. From the viewpoint of accuracy and training time, the best sequence length from 3 anchors with different heights is achieved. Furthermore, only one subarea including wireless localization is needed for the modeling and the other extended subareas is achieved by coordinate transformationation. A mode filter trigger is also designed to prevent noisy commands. Finally, extensively experimental comparisons with the state-of-the-art methods have average accuracy of 96.31% and an application to human-UAV interactions is implemented. The proposed approach becomes a plug-in module for similar tasks, e.g., a warehouse management system, home appliances.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1077-1089"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11059322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GEN Self-Labeling Object Detector for PCB Recycling Evaluation","authors":"Leandro Honorato de S. Silva;Agostinho Freire;George O. A. Azevedo;Sérgio Campello Oliveira;Carlo M. R. da Silva;Bruno J. T. Fernandes","doi":"10.1109/OJCS.2025.3584297","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3584297","url":null,"abstract":"Waste Printed Circuit Boards (WPCBs) contain many valuable and rare metals found in electronic waste, and recycling these boards can help recover these metals and prevent hazardous elements from harming the environment. However, the diverse composition of PCBs makes it challenging to automate the recycling process, which should ideally be tailored to each PCB’s composition. Computer vision is a possible solution to evaluate WPCBs, but most state-of-the-art models depend on labeled datasets unavailable in the WPCB domain. Building a large and fully labeled WPCB dataset is expensive and time-consuming. In addition, the presence of long-tailed class imbalance, where specific electronic components are significantly more prevalent than others, further complicates the development of accurate detection and classification models. To address this, we propose a new method called GEN Self-Labeling Electronic Component Detector, which utilizes a domain adaptation strategy to train semi-supervised teacher-student models that can handle the lack of fully labeled datasets while mitigating the effects of class imbalance. We also introduce a new version of the Waste Printed Circuit Board Economic Feasibility Assessment (WPCB-EFAv2), which characterizes the PCB’s composition by identifying hazardous components, calculating the density of each component type, and estimating the metals that could be recovered from recycling electrolytic capacitors and integrated circuits. Finally, we present a case study involving six PCBs with different characteristics, from which we estimated that 121 g of metals could be recovered. The most recovered metal (108 g) was aluminum from electrolytic capacitors. This information can help reduce the PCB’s composition uncertainty, leading to more efficient dismantling and cost-effective recycling processes.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1041-1052"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11058390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}