IEEE Open Journal of the Computer Society最新文献

{"title":"Addressing Security Orchestration Challenges in Next-Generation Networks: A Comprehensive Overview","authors":"Sadeep Batewela;Pasika Ranaweera;Madhusanka Liyanage;Engin Zeydan;Mika Ylianttila","doi":"10.1109/OJCS.2025.3564788","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3564788","url":null,"abstract":"Security Orchestration (SO) plays a pivotal role in ensuring robust, scalable, and efficient management of security mechanisms in next-generation 5G and beyond 5G (B5G) networks. This paper presents a comprehensive analysis of the technical challenges related to Security Orchestration (SO) in these advanced network technologies, focusing on key areas such as network security monitoring, interface standardization, privacy, scalability, multi-domain orchestration, and policy implementation. Additionally, we discuss lessons learned from existing works, identify remaining research gaps, and propose future directions for enhancing SO in 5G and B5G environments. Emerging technologies such as artificial intelligence (AI), blockchain, quantum computing and trusted execution environments (TEE) are also examined for their potential to address these challenges. The paper provides a taxonomy of SO-related issues and offers a roadmap for researchers and practitioners to navigate the evolving landscape of security in 5G and B5G networks.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"669-687"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108318","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":"Optimized Multi-Modal Conformer-Based Framework for Continuous Sign Language Recognition","authors":"Neena Aloysius;Geetha M;Prema Nedungadi","doi":"10.1109/OJCS.2025.3564828","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3564828","url":null,"abstract":"This study introduces Efficient ConSignformer, a novel framework advancing Continuous Sign Language Recognition (CSLR) by optimizing the Conformer-based CSLR model, ConSignformer. Central to this advancement is the Sign Query Attention (SQA) module, a computationally efficient self-attention mechanism that enhances both performance and scalability, resulting in the Efficient Conformer. Efficient ConSignformer integrates video embeddings from dual-modal CNN pipelines that process heatmaps and RGB videos, along with temporal learning layers tailored for each modality. These embeddings are further refined using the Efficient Conformer for the fused data from two modalities. To improve recognition accuracy, we employ an innovative task-adaptive supervised pretraining strategy for Efficient Conformer on a curated dataset of continuous Indian Sign Language (ISL). This strategy enables the model to effectively capture intricate data relationships during end-to-end training. Experimental results highlight the significant contributions of the SQA module and the pretraining strategy, with our model achieving competitive performance on benchmark datasets, PHOENIX-2014 and PHOENIX-2014 T. Notably, Efficient ConSignformer excels in recognizing longer sign sequences, leveraging a computationally lightweight Conformer backbone.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"739-749"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10978102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205883","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":"Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models","authors":"Saad Hameed;Basheer Qolomany;Samir Brahim Belhaouari;Mohamed Abdallah;Junaid Qadir;Ala Al-Fuqaha","doi":"10.1109/OJCS.2025.3564493","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3564493","url":null,"abstract":"Determining the ideal architecture for deep learning models, such as the number of layers and neurons, is a difficult and resource-intensive process that frequently relies on human tuning or computationally costly optimization approaches. While Particle Swarm Optimization (PSO) and Large Language Models (LLMs) have been individually applied in optimization and deep learning, their combined use for enhancing convergence in numerical optimization tasks remains underexplored. Our work addresses this gap by integrating LLMs into PSO to reduce model evaluations and improve convergence for deep learning hyperparameter tuning. The proposed LLM-enhanced PSO method addresses the difficulties of efficiency and convergence by using LLMs (particularly ChatGPT-3.5 and Llama3) to improve PSO performance, allowing for faster achievement of target objectives. Our method speeds up search space exploration by substituting underperforming particle placements with best suggestions offered by LLMs. Comprehensive experiments across three scenarios—(1) optimizing the Rastrigin function, (2) using Long Short-Term Memory (LSTM) networks for time series regression, and (3) using Convolutional Neural Networks (CNNs) for material classification—show that the method significantly improves convergence rates and lowers computational costs. Depending on the application, computational complexity is lowered by 20% to 60% compared to traditional PSO methods. Llama3 achieved a 20% to 40% reduction in model calls for regression tasks, whereas ChatGPT-3.5 reduced model calls by 60% for both regression and classification tasks, all while preserving accuracy and error rates. This groundbreaking methodology offers a very efficient and effective solution for optimizing deep learning models, leading to substantial computational performance improvements across a wide range of applications.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"574-585"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929883","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":"UNSW-MG24: A Heterogeneous Dataset for Cybersecurity Analysis in Realistic Microgrid Systems","authors":"Zhibo Zhang;Benjamin Turnbull;Shabnam Kasra Kermanshahi;Hemanshu Pota;Jiankun Hu","doi":"10.1109/OJCS.2025.3564266","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3564266","url":null,"abstract":"One of the major challenges of microgrid systems is the lack of comprehensive Intrusion Detection System (IDS) datasets specifically for realistic microgrid systems' communication. To address the unavailability of comprehensive IDS datasets for realistic microgrid systems, this article presents a UNSW-MG24 dataset based on realistic microgrid testbeds. This dataset contains synthesized benign network traffic from different campus departments, network flow of attack activities, system call traces, and microgrid-specific data from an integrated Festo LabVolt microgrid system. Additionally, pivoting attacks and mimicry attacks are implemented to increase this dataset's heterogeneity for intrusion detection. Comprehensive features such as network flow attributes, system call parameters, and power measurement metrics are extracted from the generated dataset. Finally, a premiminary analysis is presented to elaborate the UNSW-MG24 dataset.This dataset is publicly available for research purposes at UNSW-MG24 dataset.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"543-553"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929690","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":"Security Orchestration in 5G and Beyond Smart Network Technologies","authors":"Sadeep Batewela;Madhusanka Liyanage;Engin Zeydan;Mika Ylianttila;Pasika Ranaweera","doi":"10.1109/OJCS.2025.3563619","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3563619","url":null,"abstract":"Security Orchestration (SCO) represents a pivotal element in achieving intelligent and automated security monitoring and management within intricate and dynamic network environments, notably the 5th Generation (5G) and Beyond 5G (B5G) networks. The seamless integration of SCO into emerging 5G/B5G technologies, such as Open Radio Access Networks (ORAN), Zero-touch network and Service Management (ZSM), Software-Defined Networking (SDN), Network Function Virtualization (NFV), Multi-access Edge Computing (MEC), cloud computing, and Network Slicing (NS), is of paramount importance for realizing the vision of zero-touch, self-repair, self-healing, and secure networks. Furthermore, these advanced technologies offer opportunities to enhance SCO's functionalities. This article offers an in-depth exploration of SCO's evolution, significance, functionalities, and critical components, considering its potential within the realm of 5G and B5G network technologies. The primary objective of this specify is to provide an informative analysis of the integration of SCO into 5G and B5G network technologies and how these implementations can be harmoniously combined within a unified framework. Lastly, we present valuable insights, remaining research problems, and future directions toward achieving the goal of zero-touch security and networks.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"554-573"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929882","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":"Emerging Computing Tools for Emergency Management: Applications, Limitations and Future Prospects","authors":"Ali Daud;Khameis Mohamed Al Abdouli;Afzal Badshah","doi":"10.1109/OJCS.2025.3563759","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3563759","url":null,"abstract":"Emerging Technologies (ET) as tools are reshaping every aspect of daily life and revolutionizing the global landscape. Emergency Management (EM), in particular, is a critical domain significantly impacted by these technologies as emergency tools. Organizations and governments actively adopt tools powered by Emerging Technologies (ET) to improve Emergency Management (EM) strategies in a technology driven world. This research aims to comprehensively evaluate the role of ET as tools in EM, focusing on their utilization, effectiveness across different phases, and the challenges associated with their deployment. By exploring key domains influenced by these tools, this research identifies their primary use cases and examines how their integration has transformed the operational framework of EM. This research contributes valuable insights for academia, market entities, and government agencies to integrate ET tools effectively into EM practices, ultimately leading to improved efficiency and a higher probability of saving lives and protecting assets.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"627-644"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073184","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":"An Efficient Neural Cell Architecture for Spiking Neural Networks","authors":"Kasem Khalil;Ashok Kumar;Magdy Bayoumi","doi":"10.1109/OJCS.2025.3563423","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3563423","url":null,"abstract":"Neurons in a Spiking Neural Network (SNN) communicate using electrical pulses or spikes. They fire or trigger conditionally, and learning is sensitive to such triggers' timing and duration. The Leaky Integrate and Fire (LIF) model is the most widely used SNN neuron model. Most existing LIF-based neurons use a fixed spike frequency, which prevents them from attaining near-optimal accuracy. A research challenge is to design energy and area-efficient SNN neural cells that provide high learning accuracy and are scalable. Recently, the idea of tuning the spiking pulses in SNN was proposed and found promising. This work builds on the pulse-tuning idea by proposing an area and energy-efficient, stable, and reconfigurable SNN cell that generates spikes and reconfigures its pulse width to achieve near-optimal learning. It auto-adapts spike rate and duration to attain near-optimal accuracies for various SNN applications. The proposed cell is designed in mixed-signal, known to be beneficial to SNN, implemented using 45-nm technology, occupies an area of 27 <inline-formula><tex-math>$mu {rm m}^{2}$</tex-math></inline-formula>, incurs 1.86 <inline-formula><tex-math>$mu {rm W}$</tex-math></inline-formula>, and yields a high learning performance of 99.12%, 96.37%, and 78.64% in N-MNIST, MNIST, and N-Caltech101 datasets, respectively. The proposed cell attains higher accuracy, scalability, energy, and area economy than the state-of-the-art SNN neurons. Its energy efficiency and compact design make it highly suitable for sensor network applications and embedded systems requiring real-time, low-power neuromorphic computing.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"599-612"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10972324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073381","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 Detailed Comparative Analysis of Automatic Neural Metrics for Machine Translation: BLEURT & BERTScore","authors":"Aniruddha Mukherjee;Vikas Hassija;Vinay Chamola;Karunesh Kumar Gupta","doi":"10.1109/OJCS.2025.3560333","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3560333","url":null,"abstract":"<sc><b>Bleurt</b></small> is a recently introduced metric that employs <sc>Bert</small>, a potent pre-trained language model to assess how well candidate translations compare to a reference translation in the context of machine translation outputs. While traditional metrics like<sc>Bleu</small> rely on lexical similarities, <sc>Bleurt</small> leverages <sc>Bert</small>’s semantic and syntactic capabilities to provide more robust evaluation through complex text representations. However, studies have shown that <sc>Bert</small>, despite its impressive performance in natural language processing tasks can sometimes deviate from human judgment, particularly in specific syntactic and semantic scenarios. Through systematic experimental analysis at the word level, including categorization of errors such as lexical mismatches, untranslated terms, and structural inconsistencies, we investigate how <sc>Bleurt</small> handles various translation challenges. Our study addresses three central questions: What are the strengths and weaknesses of <sc>Bleurt</small>, how do they align with <sc>Bert</small>’s known limitations, and how does it compare with the similar automatic neural metric for machine translation, <sc>BERTScore</small>? Using manually annotated datasets that emphasize different error types and linguistic phenomena, we find that <sc>Bleurt</small> excels at identifying nuanced differences between sentences with high overlap, an area where <sc>BERTScore</small> shows limitations. Our systematic experiments, provide insights for their effective application in machine translation evaluation.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"658-668"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178946","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 B-Spline Function Based 3D Point Cloud Unwrapping Scheme for 3D Fingerprint Recognition and Identification","authors":"Mohammad Mogharen Askarin;Jiankun Hu;Min Wang;Xuefei Yin;Xiuping Jia","doi":"10.1109/OJCS.2025.3559975","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3559975","url":null,"abstract":"A three-dimensional (3D) fingerprint recognition and identification system offers several advantages: in addition to sharing the hygiene property of a 2D contactless fingerprint system in reducing the risk of contamination, it offers an exceptional anti-proofing attack capability over the traditional two-dimensional (2D) fingerprint, including 2D contactless fingerprint, recognition and identification systems. This is because capturing a 3D fingerprint sample will require a synchronized operation of multiple 3D-spaced cameras. It is infeasible to construct a quality 3D fingerprint sample based on a set of random 2D fingerprint images. In addition to capturing surface ridge and valley patterns similar to a 2D fingerprint system, 3D fingerprints capture depth, curvature, and shape information, enabling the development of more precise and robust authentication systems. Despite recent advancements, significant challenges remain. The topological height of fingerprint pixels complicates the extraction of ridge and valley patterns. Furthermore, registration issues limit the acquisition process, requiring consistent direction and orientation across all samples. To address these challenges, this article introduces a method that unwraps 3D fingerprints, represented as 3D point clouds, using B-spline curve fitting to mitigate height variation and reduce registration limitations. The unwrapped point cloud is then converted into a grayscale image by mapping the relative heights of the points. This grayscale image is subsequently used for recognition through conventional 2D fingerprint identification methods. The proposed approach demonstrated superior performance in 3D fingerprint recognition, achieving Equal Error Rates (EERs) of 0.2072%, 0.26%, and 0.22% across three experiments, outperforming existing methods. Additionally, the method surpassed 3D fingerprint flattening technique in both recognition and identification during cross-session experiments, achieving an EER of 1.50% when fingerprints with varying registrations were included.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"480-490"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962329","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871001","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":"Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning","authors":"Francisco-Jose Alvarado-Alcon;Rafael Asorey-Cacheda;Antonio-Javier Garcia-Sanchez;Joan Garcia-Haro","doi":"10.1109/OJCS.2025.3559331","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3559331","url":null,"abstract":"The Internet of Things (IoT) is gaining significant attention for its ability to digitally transform various sectors by enabling seamless connectivity and data exchange. However, deploying these networks is challenging due to the need to tailor configurations to diverse application requirements. To date, there has been limited focus on examining and enhancing the carbon footprint (CF) associated with these network deployments. In this study, we present an optimization framework leveraging machine learning techniques to minimize the CF associated with IoT multi-hop network deployments by varying the placement of the required gateways. Additionally, we establish a direct comparison between our proposed machine learning method and the integer linear program (ILP) approach. Our findings reveal that placing gateways using neural networks can achieve a 14% reduction in the CF for simple networks compared to those not using optimization for gateway placement. The ILP method could reduce the CF by 16.6% for identical networks, although it incurs a computational cost more than 250 times higher, which has its own environmental impact. Furthermore, we highlight the superior scalability of machine learning techniques, particularly advantageous for larger networks, as discussed in our concluding remarks.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"531-542"},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892357","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}