Journal of Network and Computer Applications最新文献

{"title":"QuIDS: A Quantum Support Vector machine-based Intrusion Detection System for IoT networks","authors":"Rakesh Kumar,&nbsp;Mayank Swarnkar","doi":"10.1016/j.jnca.2024.104072","DOIUrl":"10.1016/j.jnca.2024.104072","url":null,"abstract":"<div><div>With the increasing popularity of IoT, there has been a noticeable surge in security breaches associated with vulnerable IoT devices. To identify and counter such attacks. Intrusion Detection Systems (IDS) are deployed. However, these IoT devices use device-specific application layer protocols like MQTT and CoAP, which pose an additional burden to the traditional IDS. Several Machine Learning (ML) and Deep Learning (DL) based IDS are developed to detect malicious IoT network traffic. However, in recent times, a variety of IoT devices have been available on the market, resulting in the frequent installation and uninstallation of IoT devices based on users’ needs. Moreover, ML and DL-based IDS must train with sufficient device-specific attack training data for each IoT device, consuming a noticeable amount of training time. To solve these problems, we propose QuIDS, which utilizes a Quantum Support Vector Classifier to classify attacks in an IoT network. QuIDS requires very little training data compared to ML or DL to train and accurately identify attacks in the IoT network. QuIDS extracts eight flow-level features from IoT network traffic and utilizes them over four quantum bits for training. We experimented with QuIDS on two publicly available datasets and found the average recall rate, precision, and f1-score of the QuIDS as 91.1%, 84.3%, and 86.4%, respectively. Moreover, comparing QuIDS with the ML and DL methods, we found that QuIDS outperformed by 37.7%, 24.4.6%, and 36.9% more average recall and precision rates than the ML and DL methods, respectively.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104072"},"PeriodicalIF":7.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complex networks for Smart environments management","authors":"Annamaria Ficara,&nbsp;Hocine Cherifi,&nbsp;Xiaoyang Liu,&nbsp;Luiz Fernando Bittencourt,&nbsp;Maria Fazio","doi":"10.1016/j.jnca.2024.104088","DOIUrl":"10.1016/j.jnca.2024.104088","url":null,"abstract":"","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104088"},"PeriodicalIF":7.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous graph representation learning via mutual information estimation for fraud detection","authors":"Zheng Zhang ,&nbsp;Xiangyu Su ,&nbsp;Ji Wu ,&nbsp;Claudio J. Tessone ,&nbsp;Hao Liao","doi":"10.1016/j.jnca.2024.104046","DOIUrl":"10.1016/j.jnca.2024.104046","url":null,"abstract":"<div><div>In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labeled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and semantic information and embed it into the low-dimensional node representation. Recently, maximizing the mutual information between the local node embedding and the summary representation has achieved the promising results on node classification tasks. However, existing deep graph infomax methods still have the following limitations. Firstly, attribute information of nodes in the graph is not fully utilized for capturing the semantic relationships between nodes. Secondly, the local and global supervision signal are not simultaneously exploited for the node embedding learning. Thirdly, the multiplex heterogeneous relations among nodes are ignored. To address these issues, a heterogeneous graph representation learning model by mutual information estimation (MIE-HetGRL) is proposed in this paper to identify the fraudsters in the fraud review graph. Concretely, a high-order mutual information estimation is proposed to integrate the local and global mutual information as the supervision signal. Then we devise a semantic attention fusion module to aggregate the relation-aware node embeddings into a compact node representation. Finally, a joint contrastive learning is designed for facilitating the training and optimization of model. The experimental results show that our proposed model significantly outperforms state-of-the-art baselines for fraud detection.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104046"},"PeriodicalIF":7.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A holistic survey of UAV-assisted wireless communications in the transition from 5G to 6G: State-of-the-art intertwined innovations, challenges, and opportunities","authors":"Mobasshir Mahbub ,&nbsp;Mir Md. Saym ,&nbsp;Sarwar Jahan ,&nbsp;Anup Kumar Paul ,&nbsp;Alireza Vahid ,&nbsp;Seyyedali Hosseinalipour ,&nbsp;Bobby Barua ,&nbsp;Hen-Geul Yeh ,&nbsp;Raed M. Shubair ,&nbsp;Tarik Taleb","doi":"10.1016/j.jnca.2025.104131","DOIUrl":"10.1016/j.jnca.2025.104131","url":null,"abstract":"<div><div>Due to the rapid progress in communication technologies, unmanned aerial vehicles (UAVs) have become increasingly capable of providing reliable and cost-effective wireless communication from aerial vantage points. Unlike conventional stationary infrastructure, UAVs exhibit attractive features such as high scalability and improved line-of-sight (LoS) connectivity. Consequently, UAV-assisted wireless communications have become a promising paradigm to enhance coverage and connectivity in terrestrial and non-terrestrial networks. Nevertheless, the efficient deployment of UAVs in continuously evolving wireless network scenarios has remained to be a challenging task. These challenges have attracted a large body of research literature and subsequently several survey papers on UAV-assisted wireless communications. One of the distinctive features of research in UAV-assisted wireless networks is its broad array of experimental and analytical tools and techniques. A thorough review of these methodologies can swiftly familiarize researchers with the most recent efforts within this expansive field. However, most of the existing review/survey papers in this domain lack a comprehensive discussion about the advanced technologies used in UAV-assisted wireless networks, such as rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), digital twin (DT), cognitive radio (CR), space-air-ground integrated network (SAGIN), cell-free massive multiple-input multiple-output (CF mMIMO), integrated sensing and communication (ISAC), quantum technology, holographic MIMO (HMIMO). Motivated by this limitation and considering the novel UAV-assisted communication scenarios that can benefit from the adoption of such technologies; this work provides a thorough analysis of state-of-the-art intertwined technologies relative to UAV-assisted communications along with a discussion of their effectiveness and limitations. Furthermore, this study provides a brief overview of the comprehensive challenges of UAV-assisted networks, along with their security challenges, and opens future direction in this domain. This work finally explores the unique challenges in each of the existing technologies developed for UAV-assisted wireless networks that have been limitedly explored in prior literature aimed at providing a set of directions for future works.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"237 ","pages":"Article 104131"},"PeriodicalIF":7.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MW3F: Improved multi-tab website fingerprinting attacks with Transformer-based feature fusion","authors":"Yali Yuan,&nbsp;Weiyi Zou,&nbsp;Guang Cheng","doi":"10.1016/j.jnca.2025.104125","DOIUrl":"10.1016/j.jnca.2025.104125","url":null,"abstract":"<div><div>Website Fingerprinting (WF) attacks compromise the anonymity of Tor by analyzing traffic patterns. Multi-tab WF attacks, which aim to identify multiple categories of websites from obfuscated traffic, have achieved significant progress. However, existing methods often fail to fully exploit the relationships between traffic features. On the one hand, splitting-based methods have complex processes that result in the loss of local traffic features. On the other hand, end-to-end methods process complete traffic but perform poorly when relying on a single feature. To address these challenges, this paper proposes an effective Multi-tab Website Fingerprinting attack with Transformer-based Feature Fusion named MW3F. Specifically, MW3F first extracts high-level traffic features, including direction and inter-packet time. Subsequently, These new representations are fused using the multi-head self-attention, which captures both local dependencies and global interactions. Finally, to identify website categories adaptively, MW3F incorporates learnable label embeddings to probe and pool class-related features. Each website category prediction is associated with a corresponding label embedding. We evalute MW3F against state-of-the-art multi-tab WF attacks in both multi-tab and defense scenarios. In the closed-world scenario, MW3F achieves a mean average precision (mAP) of over 90% across all tab settings, outperforming the strongest baseline, ARES, by 7% in the 5-tab setting. In the defense scenario, MW3F achieves approximately 90% mAP against WTF-PAD and Front defenses, demonstrating superior performance and exceptional robustness.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104125"},"PeriodicalIF":7.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143354819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of graph-powered data quality applications for IoT monitoring sensor networks","authors":"Pau Ferrer-Cid,&nbsp;Jose M. Barcelo-Ordinas,&nbsp;Jorge Garcia-Vidal","doi":"10.1016/j.jnca.2025.104116","DOIUrl":"10.1016/j.jnca.2025.104116","url":null,"abstract":"<div><div>The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect of the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning (ML) and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. This survey focuses on graph-based models for data quality control in monitoring sensor networks. In addition, it introduces the technical details that are commonly used to provide powerful graph-based solutions for data quality tasks in sensor networks, such as missing value imputation, outlier detection, or virtual sensing. To conclude, different challenges and emerging trends have been identified, e.g., graph-based models for digital twins or model transferability and generalization.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104116"},"PeriodicalIF":7.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on the state-of-the-art CDN architectures and future directions","authors":"Waris Ali ,&nbsp;Chao Fang ,&nbsp;Akmal Khan","doi":"10.1016/j.jnca.2025.104106","DOIUrl":"10.1016/j.jnca.2025.104106","url":null,"abstract":"<div><div>A Content Delivery Network (CDN) consists of a distributed infrastructure of proxy servers designed to deliver digital content to end users effectively. CDNs have gained popularity due to increasing Internet users and their growing demand for low-latency content delivery. However, several unexplored aspects within CDN technology, including management, standardization, and architecture of CDNs, are crucial to staying aligned with industry trends and advancements. Previous survey papers focus on CDN aspects and have not yet categorized state-of-the-art CDN architectures. In this survey, we categorize and analyze seven state-of-the-art CDN architectures, providing a detailed analysis of their components, benefits, and limitations. We highlight advancements such as the convergence of CDN and Content-Centric Networking (CCN) paradigms for improved data retrieval, fog-based CDN collaboration with MEC for edge processing optimization, and blockchain technology for secure content delivery. Additionally, we also identify research challenges within CDN architectures and discuss the effectiveness of proposed solutions. Finally, we propose future research directions, including the collaboration of reinforcement learning for adaptive edge responses in CDN-P2P, machine learning for CDN selection in multi-CDN setups, and federated learning for improved caching in software-based and fog-based CDNs.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104106"},"PeriodicalIF":7.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SDN-AAA: Towards the standard management of AAA infrastructures","authors":"Francisco Lopez-Gomez ,&nbsp;Rafa Marin-Lopez ,&nbsp;Oscar Canovas ,&nbsp;Gabriel Lopez-Millan ,&nbsp;Fernando Pereniguez-Garcia","doi":"10.1016/j.jnca.2025.104114","DOIUrl":"10.1016/j.jnca.2025.104114","url":null,"abstract":"<div><div>Software Defined Networking (SDN) is a widely adopted technology that enables agile and flexible management of networks and services. This paradigm is a strong candidate for addressing the dynamic and secure management of large and complex Authentication, Authorization and Accounting (AAA) infrastructures. In those infrastructures, multiple nodes must securely exchange information to interconnect different realms, and the manual configuration of these nodes represents a significant point of failure and a challenge for administrators. This paper presents a novel SDN-based framework, named SDN-AAA, that follows a data model-driven approach using the YANG standard. This framework enables the dynamic management of routing and security configurations in AAA scenarios. Additionally, empirical results demonstrate that the proposed framework can handle increasing numbers of nodes without significant performance degradation in mesh and star topologies, with configuration and routing times that linearly or exponentially scale depending on the topology used. This validates the feasibility of the solution in real-world scenarios.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104114"},"PeriodicalIF":7.7,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GridFL: A 3D-Grid-based Federated Learning framework","authors":"Jiagao Wu,&nbsp;Yudong Jiang,&nbsp;Zhouli Fan,&nbsp;Linfeng Liu","doi":"10.1016/j.jnca.2025.104115","DOIUrl":"10.1016/j.jnca.2025.104115","url":null,"abstract":"<div><div>Federated Learning (FL) is an emerging distributed machine learning framework that enables a large number of devices to train machine learning models collaboratively without sharing local data. Despite the extensive potential of FL, in practical scenarios, different characteristics of clients lead to the presence of different heterogeneity in resources, data distribution, and data quantity, which poses a challenge for the training of FL. To address this problem, in this paper, we first conduct an exhaustive experimental study on all three kinds of heterogeneity in FL and provide insights into the specific impact of heterogeneity on training performance. Subsequently, we propose GridFL, a 3D-grid-based FL framework, where the three kinds of heterogeneity are defined as three dimensions (i.e., dimensions of training speed, data distribution, and data quantity) independently, and all clients in FL training are assigned to corresponding cells of the 3D grid by a gridding algorithm based on K-means clustering. In addition, we propose a grid scheduling algorithm with a dynamic selection strategy, which can select an optimal subset of clients to participate in FL training per round by adopting different strategies for different dimensions and cells. The simulation experiments show that GridFL exhibits superior performance in heterogeneous environments and outperforms several related state-of-the-art FL algorithms. Thus, the effectiveness of the proposed algorithms and strategies in GridFL are verified.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104115"},"PeriodicalIF":7.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STARNeT: Multidimensional spatial–temporal attention recall network for accurate encrypted traffic classification","authors":"Xinjie Guan,&nbsp;Shuyan Zhu,&nbsp;Xili Wan,&nbsp;Yaping Wu","doi":"10.1016/j.jnca.2025.104109","DOIUrl":"10.1016/j.jnca.2025.104109","url":null,"abstract":"<div><div>Network traffic classification is crucial for monitoring network health, detecting malicious activities, and ensuring Quality-of-Service (QoS). The use of dynamic ports and encryption complicates the process, rendering traditional port-based or payload-based classification methods ineffective. Conventional machine learning and statistical approaches often depend on manual feature or pattern extraction by experts, leading to inefficiencies and potential inaccuracies. Deep learning offers a promising alternative, with its inherent capability to autonomously extract patterns and features from data. Nonetheless, the design of existing deep learning models often limits them to high-level semantic feature extraction, neglecting the rich multidimensional spatial and temporal information in network traffic. To address these limitations, this paper introduces STARNet, a deep learning-based model for encrypted traffic classification. STARNet incorporates a dual-stream pathway network architecture that optimizes feature extraction from each pathway. It also features a novel spatiotemporal multidimensional semantic feature recall mechanism, designed to enrich the model’s analytical depth by retaining important information that might be missed when focusing solely on high-level features. Evaluated on two public network traffic datasets, STARNet demonstrates superior accuracy in traffic classification tasks, highlighting its potential to enhance network monitoring and security.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104109"},"PeriodicalIF":7.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}