IEEE Open Journal of the Computer Society最新文献_第6页

SSL-XIoMT: Secure, Scalable, and Lightweight Cross-Domain IoMT Sharing With SSI and ZKP Authentication SSL-XIoMT：安全，可扩展，轻量级跨域IoMT共享与SSI和ZKP认证

IEEE Open Journal of the Computer Society Pub Date : 2025-03-14 DOI: 10.1109/OJCS.2025.3570087

Lyhour Hak;Somchart Fugkeaw

{"title":"SSL-XIoMT: Secure, Scalable, and Lightweight Cross-Domain IoMT Sharing With SSI and ZKP Authentication","authors":"Lyhour Hak;Somchart Fugkeaw","doi":"10.1109/OJCS.2025.3570087","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3570087","url":null,"abstract":"The Internet of Medical Things (IoMT) is transforming healthcare by enabling devices to generate and share critical patient data. However, securely sharing this data across different healthcare entities remains a significant challenge due to concerns over privacy and security. Traditional solutions using Ciphertext Policy Attribute-Based Encryption (CP-ABE), Self-Sovereign Identity (SSI), and Zero-Knowledge Proofs (ZKPs) offer secure and anonymous data access, but they often fall short in scalability and integration, particularly in cross domain environments. To address these limitations, we introduce SSL-XIoMT, an optimized SSI and ZKP authentication framework within a consortium Hyperledger-based environment. This innovative system integrates SSI under advanced Zero-Knowledge Scalable Transparent Argument of Knowledge (ZK-STARK) and Plonk protocols within a consortium Hyperledger framework for privacy-preserving identity verification. We enhance identity privacy by integrating Multi-Party Computation (MPC), ensuring that identity credentials and ZKP proofs are securely shared and reconstructed without exposing sensitive information. Additionally, we optimize CP-ABE by offloading complex computations to fog nodes, which pre-compute attributes and logical operations. This approach significantly reduces computational overhead and enhances both privacy and efficiency. Our extensive analysis shows that SSL-XIoMT dramatically improves the performance of processing time for CP-ABE encryption and decryption compared to current methods. Moreover, our hybrid ZKPs based authentication approach outperforms the existing schemes regarding processing time and flexibility. The throughput test also demonstrates that SSL-XIoMT is practical for large scale cross-domain data sharing implementation.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"714-725"},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11003572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205884","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}

引用次数: 0

Editorial: Gratitude, Reflection, and Celebration: My Tenure as EiC Comes to a Close 社论：感恩，反思和庆祝：我作为EiC的任期即将结束

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3525990

Song Guo

引用次数: 0

TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation 基于变压器和注意力增强的脑肿瘤分割架构

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3569758

Mir Nafiul Nagib;Rahat Pervez;Afsana Alam Nova;Hadiur Rahman Nabil;Zeyar Aung;M. F. Mridha

{"title":"TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation","authors":"Mir Nafiul Nagib;Rahat Pervez;Afsana Alam Nova;Hadiur Rahman Nabil;Zeyar Aung;M. F. Mridha","doi":"10.1109/OJCS.2025.3569758","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3569758","url":null,"abstract":"Brain tumor segmentation is crucial in medical imaging, allowing informed diagnosis and treatment planning. In this study, we propose TuSegNet, a new transformer-based and attention-enhanced architecture for robust brain tumor segmentation. The model combines convolutional layers with transformer blocks for global context awareness, incorporates Atrous Spatial Pyramid Pooling (ASPP) for multi-scale feature extraction, and employs channel attention mechanisms to concentrate on tumor-relevant parts. Evaluated on three datasets—Dataset A, Dataset B, and a combined dataset—TuSegNet achieves state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 0.895, 0.910, and 0.930, respectively, and an Intersection over Union (IoU) of 0.820, 0.835, and 0.860. Ablation studies validate the importance of ASPP and attention mechanisms, while comparative analysis demonstrates outstanding performance over existing SOTA models such as Swin UNet and TransUNet. The proposed methodology improves segmentation accuracy and highlights the importance of hybrid architectures in handling complex medical imaging tasks. These developments underscore the potential of TuSegNet for real-world healthcare applications in brain tumor diagnosis.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"750-761"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11002687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232131","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}

引用次数: 0

WiFi-Based Location Tracking: A Still Open Door on Laptops 基于wifi的位置跟踪：笔记本电脑的一扇尚未打开的大门

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3569437

Mariana Cunha;Ricardo Mendes;Yves-Alexandre de Montjoye;João P. Vilela

引用次数: 0

Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification 基于对立的白鲨优化器优化改进的EfficientNetV2道路裂缝分类

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3569208

Mohammed Al-Shalabi;Mohammed A. Mahdi;Malik Braik;Mohammed Azmi Al-Betar;Shahanawaj Ahamad;Sawsan A. Saad

{"title":"Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification","authors":"Mohammed Al-Shalabi;Mohammed A. Mahdi;Malik Braik;Mohammed Azmi Al-Betar;Shahanawaj Ahamad;Sawsan A. Saad","doi":"10.1109/OJCS.2025.3569208","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3569208","url":null,"abstract":"Maintaining reliable and long-lasting road infrastructure requires accurate identification and management of pavement cracks, as these cracks can significantly weaken asphalt and concrete surfaces over time. Although Convolutional Neural Networks (CNNs) and meta-heuristic algorithms have proven effective in solving real-world problems, their use in low-contrast pavement crack images is worth investigating. This study proposes an automated crack detection framework that integrates three key components: (1) a new variant of a pre-trained CNN architecture, referred to as Modified EfficientNetV2 (MEfficientNetV2) for pavement crack classification; (2) a combination of opposition-based learning with White Shark Optimizer (WSO), known as Opposition WSO (OWSO), to improve the balance between exploration and exploitation; and (3) Principal Component Analysis (PCA) for efficient dimensionality reduction and feature selection. This method is validated on various publicly available asphalt crack datasets that contain low-contrast natural images. Preprocessing techniques are first applied to eliminate noise and enhance image quality. The OWSO algorithm is then integrated to optimize the classification performance of MEfficientNetV2, while PCA accelerates the learning process by retaining critical features in the thresholds of the varying components. Comparative evaluations with state-of-the-art methods demonstrate that the proposed model excels in terms of precision, robustness, and generalizability. The outcome emphasizes its ability to identify the most effective solution for crack detection in practical scenarios, where PCA-based feature selection improves computational efficiency without compromising performance. This study focuses on the potential of hybrid deep learning and bio-inspired optimization strategies to improve automated pavement maintenance systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"762-775"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10999102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206021","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}

引用次数: 0

MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer MEViT：基于元学习effentnet视觉转换器的深度假检测推广

IEEE Open Journal of the Computer Society Pub Date : 2025-03-07 DOI: 10.1109/OJCS.2025.3568044

Van-Nhan Tran;Hoanh-Su Le;Piljoo Choi;Suk-Hwan Lee;Ki-Ryong Kwon

{"title":"MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer","authors":"Van-Nhan Tran;Hoanh-Su Le;Piljoo Choi;Suk-Hwan Lee;Ki-Ryong Kwon","doi":"10.1109/OJCS.2025.3568044","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3568044","url":null,"abstract":"Deepfakes are digitally manipulated videos that appear realistic but are actually fake. With the rapid advances in deep generative models, the accessibility and sophistication of such manipulation technologies are increasing, making it more challenging to detect fake content. Different facial forgery techniques result in complex data distributions, and most existing deepfake detection approaches rely on convolutional neural networks (CNNs) that treat the task as a binary classification problem. While these methods achieve high accuracy on specific datasets, their generalization performance across datasets is often poor due to overfitting to manipulation techniques seen during training. In this study, we propose a model called MEViT, which integrates the EfficientNet Vision Transformer with a meta-learning framework to enhance generalization in deepfake detection. Furthermore, we introduce a pair-discrimination loss to push the feature representations of fake samples away from those of real samples, and a domain adjustment loss to reduce domain shifts across different manipulation methods. The MEViT model is trained on a specific manipulation method in the FaceForensics++ dataset and evaluated on other unseen methods from the same dataset. Additionally, we conduct extensive experiments on multiple deepfake benchmarks, including FaceForensics++ and CelebDF-v2, and compare our method with various state-of-the-art approaches to demonstrate its effectiveness.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"789-800"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10992261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232130","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}

引用次数: 0

FOCC: A Synthetically Balanced Federated One-Class-Classification for Cyber Threat Intelligence in Software Defined Networking 软件定义网络中网络威胁情报的综合平衡联邦一类分类

IEEE Open Journal of the Computer Society Pub Date : 2025-03-06 DOI: 10.1109/OJCS.2025.3567386

Syed Hussain Ali Kazmi;Faizan Qamar;Rosilah Hassan;Kashif Nisar

{"title":"FOCC: A Synthetically Balanced Federated One-Class-Classification for Cyber Threat Intelligence in Software Defined Networking","authors":"Syed Hussain Ali Kazmi;Faizan Qamar;Rosilah Hassan;Kashif Nisar","doi":"10.1109/OJCS.2025.3567386","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3567386","url":null,"abstract":"Federated Learning offers a promising approach for building Cyber Threat Intelligence (CTI) by utilizing cross-domain data in Software Defined Networking (SDN) while addressing privacy concerns. However, as sixth-generation (6G) systems evolve with heterogeneous characteristics, the training data across individual SDN domains is likely to be highly Non-Independent and Identically Distributed (Non-IID), which significantly impairs the performance of Artificial Intelligence (AI) based Intrusion Detection Systems (IDSs). Therefore, this study proposes a novel framework called Federated One Class Classification (FOCC), which contains parallel inference with threat-specific independent autoencoders as local model at each domain and empowered with Variational Auto Encoders (VAEs). Firstly, the relation between weight divergence and multi-classification in Non-IID data is derived using mathematical analysis. Secondly, the threat specific data is generated by VAEs at each domain with latent space aggregation, which achieved the reduced validation loss in Federated Learning by synthetically balancing threat-specific data. Finally, the proposed FOCC framework depicts substantial improvement in threat specific multiclassification on InSDN dataset as compared to the existing state-of-the-art solutions for performance parameters; including accuracy, precision, recall and F1 score. Moreover, the integration of parallel processing in the proposed FOCC framework significantly minimizes computational delays.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"701-713"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10989587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178955","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}

引用次数: 0

Reference-Free 3D WiFi AP Localization by Outdoor-to-Indoor Bridging 通过室内外桥接实现无参考的3D WiFi AP定位

IEEE Open Journal of the Computer Society Pub Date : 2025-03-02 DOI: 10.1109/OJCS.2025.3566774

Tatsuya Amano;Hirozumi Yamaguchi;Teruo Higashino

{"title":"Reference-Free 3D WiFi AP Localization by Outdoor-to-Indoor Bridging","authors":"Tatsuya Amano;Hirozumi Yamaguchi;Teruo Higashino","doi":"10.1109/OJCS.2025.3566774","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3566774","url":null,"abstract":"WiFi access point (AP) localization is essential for wireless infrastructure management and location-based services. While deep learning approaches have shown promising accuracy improvements, they require extensive training data with precise coordinates, making large-scale deployment impractical. Traditional localization techniques also rely heavily on indoor reference points (RPs), resulting in costly and labor-intensive deployments. We present WiSight, a novel framework that eliminates the need for indoor RPs by leveraging GPS-tagged outdoor RSS measurements and 3D building geometry, anchoring the indoor reference frame to the global coordinate system using GPS-tagged exterior points. WiSight first identifies virtual anchor positions on building exteriors through outdoor signal propagation modeling, then reconstructs indoor AP configurations using unlabeled RSS measurement pairs and multidimensional scaling. Extensive evaluation across multiple buildings demonstrates that WiSight achieves an average 3D AP localization error of 7.1 m (median: 6.8 m), reducing error by 59% compared to an opportunistic GPS-based approach. In office environments, WiSight attains 9.6 m error (median: 8.5 m)—22% lower than the state-of-the-art deep learning-based method, while achieving 82% floor-level accuracy without requiring any indoor RPs or training data.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"688-700"},"PeriodicalIF":0.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125492","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}

引用次数: 0

Correlation-Based Knowledge Distillation in Exemplar-Free Class-Incremental Learning 无范例类增量学习中基于关联的知识提炼

IEEE Open Journal of the Computer Society Pub Date : 2025-02-28 DOI: 10.1109/OJCS.2025.3546754

Zijian Gao;Bo Liu;Kele Xu;Xinjun Mao;Huaimin Wang

{"title":"Correlation-Based Knowledge Distillation in Exemplar-Free Class-Incremental Learning","authors":"Zijian Gao;Bo Liu;Kele Xu;Xinjun Mao;Huaimin Wang","doi":"10.1109/OJCS.2025.3546754","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3546754","url":null,"abstract":"Class-incremental learning (CIL) aims to learn a family of classes incrementally with data available in order rather than training all data at once. One main drawback of CIL is that standard deep neural networks suffer from catastrophic forgetting (CF), especially when the model only has access to data from the current incremental step. Knowledge Distillation (KD) is a widely used technique that utilizes old models as the teacher model to alleviate CF. However, based on a case study, our investigation reveals that the vanilla KD is insufficient with a strict point-to-point restriction. Instead, a relaxed match between the teacher and student improves distillation performance and model stability. In this article, we propose a simple yet effective method to mitigate CF without any additional training costs or requiring any exemplars. Specifically, we apply the linear correlation between the features of the teacher and student to measure the distillation loss rather than vanilla point-to-point loss, which significantly improves the model stability. Then, we utilize label augmentation to improve feature generalization and save prototypes to alleviate classification bias further. The proposed method significantly outperforms state-of-the-art methods in the various settings of benchmarks, including CIFAR-100 and Tiny-ImageNet, demonstrating its effectiveness and robustness.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"449-459"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698225","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}

引用次数: 0

Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results 用非参数 Bootstrap Likelihood 估计法研究聚类结果的机会设置

IEEE Open Journal of the Computer Society Pub Date : 2025-02-24 DOI: 10.1109/OJCS.2025.3545261

Ammar Elnour;Wencheng Yang;Yan Li

{"title":"Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results","authors":"Ammar Elnour;Wencheng Yang;Yan Li","doi":"10.1109/OJCS.2025.3545261","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3545261","url":null,"abstract":"Clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzing result uncertainty. This issue applies to most outcomes obtained from other knowledge discovery techniques, such as machine learning and statistical learning. Traditional statistical methods assume data follows standard distributions, whereas resampling and bootstrapping methods offer more accurate and reliable alternatives. This article introduces a method that employs bootstrap likelihood estimation to infer the uncertainty of generated clustering structures. We first calculated the clustering error in the original dataset and then utilized the proposed method to estimate its nonparametric bootstrapped likelihood. By comparing these two values, we can establish a nonparametric significance testing framework that directly determines the validity of the result. To evaluate the effectiveness of our method, we conducted experiments using synthetic and real datasets. The results demonstrate that our method can successfully validate clustering results.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"438-448"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698199","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}

引用次数: 0