Computer Networks最新文献_第7页

FDSS: Flight data sharing scheme based on blockchain with dynamic, secure and efficient consensus algorithm FDSS：基于区块链的飞行数据共享方案，采用动态、安全和高效的共识算法

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-16 DOI: 10.1016/j.comnet.2025.111275

Feiyi Xu , Shihao Hu , Ying Sun , Xiaoxuan Hu , Jin Qi , Yanfei Sun , Zhenjiang Dong

{"title":"FDSS: Flight data sharing scheme based on blockchain with dynamic, secure and efficient consensus algorithm","authors":"Feiyi Xu , Shihao Hu , Ying Sun , Xiaoxuan Hu , Jin Qi , Yanfei Sun , Zhenjiang Dong","doi":"10.1016/j.comnet.2025.111275","DOIUrl":"10.1016/j.comnet.2025.111275","url":null,"abstract":"<div><div>As the number of data providers and the volume of data in flight data sharing networks continue to grow, achieving secure and efficient sharing of flight data across multiple institutions faces significant challenges. The phenomenon of data silos is prevalent, severely hindering the flow and utilization of information. To address these issues, this paper proposes a flight data sharing scheme (FDSS) that integrates cloud computing and blockchain. In this scheme, we propose a separation mechanism of metadata on-chain storage and entity data off-chain storage, which effectively improves the data storage efficiency. At the same time, we also offload the complex computation tasks during data access to the edge servers for processing, which realizes the refinement of data access and reduces the computation burden on the user side. Given that all information exchanges between data requesters and data owners are conducted via the blockchain, the block generation speed becomes critical to ensure the efficient operation of the scheme. To this end, we also make significant improvements to traditional consensus algorithms, aiming to comprehensively enhance the overall system efficiency. In the end, through in-depth theoretical analysis and experimental verification, the FDSS scheme demonstrates excellent performance and high feasibility, fully proving its great potential and value in the field of flight data sharing.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"265 ","pages":"Article 111275"},"PeriodicalIF":4.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845203","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}

引用次数: 0

Exploring the unseen: A transformer-based unknown traffic detection scheme with contextual feature representation 探索未知：基于变换器的未知流量检测方案与上下文特征表征

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-15 DOI: 10.1016/j.comnet.2025.111286

Yongseok Kwon , Seyoung Ahn , Minho Cho , Yushin Kim , Soohyeong Kim , Sunghyun Cho

{"title":"Exploring the unseen: A transformer-based unknown traffic detection scheme with contextual feature representation","authors":"Yongseok Kwon , Seyoung Ahn , Minho Cho , Yushin Kim , Soohyeong Kim , Sunghyun Cho","doi":"10.1016/j.comnet.2025.111286","DOIUrl":"10.1016/j.comnet.2025.111286","url":null,"abstract":"<div><div>Network traffic classification is vital for ensuring security, guaranteeing quality of service (QoS), and optimizing performance. Accurate classification of network traffic, particularly the detection of unknown traffic, becomes increasingly challenging in modern environments characterized by encrypted and dynamic traffic patterns. In this study, we propose a novel framework designed to address these challenges. The proposed method employs a bidirectional encoder representations from transformers (BERT)-based feature extraction model to capture contextual and discriminative features from packet bytes in traffic, followed by a feature verification model that computes similarity scores between packet classes to enable precise traffic classification. Even in dynamic situations where the unknown traffic ratio varies, our proposed adaptive algorithm can effectively detect unknown traffic by leveraging these similarity scores. We conduct extensive experiments on two benchmark datasets across various unknown traffic ratios and demonstrate that the proposed method outperforms state-of-the-art methods by a minimum of 4.55%p and a maximum of 32.04%p improvement in overall accuracy.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"265 ","pages":"Article 111286"},"PeriodicalIF":4.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845205","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}

引用次数: 0

Snowflakes: Efficient schemes for source location privacy in Wireless Sensor Networks 雪花：无线传感器网络中有效的源位置隐私方案

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-15 DOI: 10.1016/j.comnet.2025.111277

Sain Saginbekov , Dossay Oryspayev

引用次数: 0

PDMA: Efficient and privacy-preserving dynamic task assignment with multi-attribute search in crowdsourcing 基于多属性搜索的众包动态任务分配算法

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-15 DOI: 10.1016/j.comnet.2025.111279

Haiyong Bao , Ronghai Xie , Zhehong Wang , Lu Xing , Hong-Ning Dai

{"title":"PDMA: Efficient and privacy-preserving dynamic task assignment with multi-attribute search in crowdsourcing","authors":"Haiyong Bao , Ronghai Xie , Zhehong Wang , Lu Xing , Hong-Ning Dai","doi":"10.1016/j.comnet.2025.111279","DOIUrl":"10.1016/j.comnet.2025.111279","url":null,"abstract":"<div><div>Crowdsourcing leverages distributed mobile devices for task allocation, significantly reducing service costs. However, existing schemes face three major challenges, i.e., data privacy leakage, focusing just on single-attribute tasks, and the inability to accommodate dynamic task updates. To address these issues, we propose a privacy-preserving dynamic multi-attribute task assignment scheme (PDMA). PDMA supports multi-attribute range searches by incorporating spatial, temporal, and keyword constraints. It introduces a hilbert attribute tree (HRAT) for efficient query of multi-attribute tasks and utilizes hilbert R-trees and counting bloom filters (CBF) to facilitate dynamic task updates. To preserve the privacy of spatial and temporal attributes, PDMA integrates the improved symmetric homomorphic encryption (iSHE) scheme, while hash functions preserve the CBF for keyword privacy. Additionally, we propose a secure ternary match protocol (CTP) and a secure subset query scheme (Ssub), which combine iSHE-based ciphertext comparison protocols with simulated ternary content addressable memory (TCAM) to accelerate keyword subset matching. Security and performance analysis demonstrate that PDMA achieves the chosen-query attack security (CQA2-security) and is both practical and efficient.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"265 ","pages":"Article 111279"},"PeriodicalIF":4.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850782","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}

引用次数: 0

MFSI: Multi-flow based service identification for encrypted network traffic MFSI：用于加密网络流量的基于多流的服务标识

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-15 DOI: 10.1016/j.comnet.2025.111283

Biying Wang, Baosheng Wang, Ziling Wei, Shuang Zhao, Shuhui Chen, Zhengpeng Li, Minxin Wang

{"title":"MFSI: Multi-flow based service identification for encrypted network traffic","authors":"Biying Wang, Baosheng Wang, Ziling Wei, Shuang Zhao, Shuhui Chen, Zhengpeng Li, Minxin Wang","doi":"10.1016/j.comnet.2025.111283","DOIUrl":"10.1016/j.comnet.2025.111283","url":null,"abstract":"<div><div>Encrypted traffic identification plays a crucial role in improving service quality, optimizing network management, and maintaining network security. Various machine learning and deep learning based methods have been proposed to address the challenge of identifying encrypted traffic. However, existing methods face two main challenges. First, they are easily affected by interfering traffic, which reduces the accuracy of identifying target traffic. Second, they rely on expert annotations to identify unknown applications. In this paper, we propose a multi-flow based method, namely MFSI, for identifying the service of encrypted network traffic. MFSI treats multiple flows as the classification unit to reduce the impact of interfering flows and constructs a robust graph structure Multi-Flow Multi-Relational Graph (MMRG), based on three types of relationships. Then, it introduces Relational Graph Convolutional Networks to update vertex features in MMRG and generates global graph-level representations for multi-flow classification. We conduct experiments on raw network traffic. The results show that MFSI can achieve a classification accuracy of 98.58% without filtering or deleting any traffic, surpassing state-of-the-art schemes. It also performs well in identifying the type of services of unknown encrypted traffic.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"265 ","pages":"Article 111283"},"PeriodicalIF":4.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852369","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}

引用次数: 0

A survey of attacks on blockchain systems using a layer-based approach 使用基于层的方法对区块链系统进行攻击的调查

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-13 DOI: 10.1016/j.comnet.2025.111274

Joydip Das , Syed Ashraf Al Tasin , Md. Forhad Rabbi , Md. Sadek Ferdous

{"title":"A survey of attacks on blockchain systems using a layer-based approach","authors":"Joydip Das , Syed Ashraf Al Tasin , Md. Forhad Rabbi , Md. Sadek Ferdous","doi":"10.1016/j.comnet.2025.111274","DOIUrl":"10.1016/j.comnet.2025.111274","url":null,"abstract":"<div><div>Blockchain technology is very popular nowadays as it ensures decentralization, transparency, and immutability. However, despite its inherent security features, blockchain-based systems have been frequently targeted by adversaries, raising concerns about their trustworthiness. This paper presents a comprehensive study of 24 major attacks against blockchain systems, categorized in a structured, layer-based approach. The study systematically examines the feasibility, underlying incentives, and underlying vulnerabilities exploited in these attacks. We categorize attacks across four blockchain layers, namely the Network, Consensus, Application, and Meta-Application layers, illustrating the diverse nature of security threats. Furthermore, we propose a systematic analysis that enables an in-depth evaluation of attacks, their interconnections, and cascading effects across different layers. This research intends to contribute to the development of robust security strategies that can mitigate vulnerabilities and increase confidence in decentralized systems by providing a structured technique for analyzing blockchain security concerns. Ultimately, this research underscores the critical need for continuous advancements in blockchain security mechanisms to ensure the resilience of blockchain based systems against emerging and evolving threats.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"265 ","pages":"Article 111274"},"PeriodicalIF":4.4,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845204","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}

引用次数: 0

Delay minimization in NTNs: Deployment and caching optimization for satellite- and cache-aided UAV NTNs中的延迟最小化：卫星和缓存辅助无人机的部署和缓存优化

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-13 DOI: 10.1016/j.comnet.2025.111257

Zihao Han , Ting Zhou , Tianheng Xu , Yuling Ouyang , Xianfu Chen , Tao Chen , Honglin Hu

引用次数: 0

Performance enhancement of UAV-enabled MEC systems through intelligent task offloading and resource allocation 通过智能任务卸载和资源分配提高无人机 MEC 系统的性能

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-12 DOI: 10.1016/j.comnet.2025.111280

Mohsen Darchini-Tabrizi , Amirali Pakdaman-Donyavi , Reza Entezari-Maleki , Leonel Sousa

{"title":"Performance enhancement of UAV-enabled MEC systems through intelligent task offloading and resource allocation","authors":"Mohsen Darchini-Tabrizi , Amirali Pakdaman-Donyavi , Reza Entezari-Maleki , Leonel Sousa","doi":"10.1016/j.comnet.2025.111280","DOIUrl":"10.1016/j.comnet.2025.111280","url":null,"abstract":"<div><div>The rapid and continuous growth of the Internet of Things has led to a rising demand for processing and storage solutions that can overcome the limitations of user devices. While cloud computing provides scalability, it often introduces considerable delay. To address this challenge, a computing paradigm that brings resources closer to user devices is preferable. Unmanned Aerial Vehicles (UAVs) have emerged as an effective solution to enhance communication quality and coverage in wireless systems, particularly in specific conditions. This paper presents a UAV-enabled Mobile Edge Computing (MEC) system, where UAVs equipped with computational capabilities provide task offloading services to users. In this system, users process part of their computing tasks locally, while offloading the remaining tasks to UAVs for processing. The primary goal of the proposed algorithm is to minimize processing delays, taking into account the environmental and energy constraints of the UAVs, such as movement boundaries, link blockages, transmission delays, and battery consumption for computation and flight. To achieve this, we propose a Deep Reinforcement Learning algorithm based on the Rainbow Deep Q-Network. This algorithm explores the dynamic and stochastic environment of MEC to deploy an appropriate computation offloading policy. The extensive experiments conducted in this study demonstrate the superior performance of the proposed method. The results show fast convergence to an optimal value and an average improvement of 12.63 percent in delay compared to state-of-the-art methods.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"264 ","pages":"Article 111280"},"PeriodicalIF":4.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829125","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}

引用次数: 0

Frequency-domain augmentation and multi-scale feature alignment for improving transferability of adversarial examples 频域增强和多尺度特征对齐提高对抗性示例的可转移性

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-11 DOI: 10.1016/j.comnet.2025.111261

Gui-Hong Li, Heng-Ru Zhang, Fan Min

{"title":"Frequency-domain augmentation and multi-scale feature alignment for improving transferability of adversarial examples","authors":"Gui-Hong Li, Heng-Ru Zhang, Fan Min","doi":"10.1016/j.comnet.2025.111261","DOIUrl":"10.1016/j.comnet.2025.111261","url":null,"abstract":"<div><div>Transfer-based adversarial attack implies that the same adversarial example can fool Deep Neural Networks (DNNs) with different architectures. Model-related approaches train a new surrogate model in local to generate adversarial examples. However, because DNNs with different architectures focus on diverse features within the same data, adversarial examples generated by surrogate models frequently exhibit poor transferability when the surrogate and target models have significant architectural differences. In this paper, we propose a Two-Stage Generation Framework (TSGF) through frequency-domain augmentation and multi-scale feature alignment to address this issue. In the stage of surrogate model training, we enable the surrogate model to capture various features of data through detail and diversity enhancement. Detail enhancement increases the weight of details in clean examples by a frequency-domain augmentation module. Diversity enhancement incorporates slight adversarial examples into the training process to increase the diversity of clean examples. In the stage of adversarial generation, we perturb the distinctive features that different models focus on to improve transferability by a multi-scale feature alignment attack technique. Specifically, we design a loss function using the intermediate multi-layer features of the surrogate model to maximize the difference between the features of clean and adversarial examples. We compare TSGF with a combination of three closely related surrogate model training schemes and the most relevant adversarial attack methods. Results show that TSGF improves transferability across significantly different architectures. The implementation of TSGF is available at <span><span>https://github.com/zhanghrswpu/TSGF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"264 ","pages":"Article 111261"},"PeriodicalIF":4.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823463","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}

引用次数: 0

Cache Periscope: Gain insights into the global epidemic of malicious domains through DNS Cache 缓存潜望镜：通过DNS缓存深入了解恶意域的全球流行

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-04-11 DOI: 10.1016/j.comnet.2025.111245

Yabo Wang , Jiakun Sun , Ruizhi Xiao , Weilong Li , Shuyuan Jin

{"title":"Cache Periscope: Gain insights into the global epidemic of malicious domains through DNS Cache","authors":"Yabo Wang , Jiakun Sun , Ruizhi Xiao , Weilong Li , Shuyuan Jin","doi":"10.1016/j.comnet.2025.111245","DOIUrl":"10.1016/j.comnet.2025.111245","url":null,"abstract":"<div><div>Domain names are often abused for various harmful and illegal activities. To mitigate these threats, security practitioners have proposed detecting malicious domains based on features such as domain name resolution data, semantic attributes, website appearance, and website correlations. Although these techniques have achieved notable results, they remain relatively passive in countering malicious activities. In this paper, we present a large-scale measurement study of the global epidemic of malicious domain names on open resolvers using cache probing techniques. We propose a modified probing method designed for large-scale domain access estimation. Leveraging this method, we examine the access patterns of malicious domains under open resolvers, which are widely deployed and utilized across the Internet, aiming to map the distribution of malicious activities based on the geographic location of open resolvers. Additionally, to the best of our knowledge, we are the first to propose using domain name top lists to estimate the volume of resolver users, which reflects the potential influence of a resolver. The weights of the estimated user volumes are further validated by real DNS traffic. By integrating these two methods, we evaluate the potential impact of malicious domains and conduct extensive measurements and analyses of malicious domain activities on open resolvers worldwide. Our findings reveal the regional distribution of malicious campaigns and provide insights into the global epidemic of malicious domains. Our measurement results demonstrate that practitioners can actively collect threat intelligence using the proposed techniques, gain insights into the current Internet threats, and implement more proactive measures to combat malicious campaigns.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"265 ","pages":"Article 111245"},"PeriodicalIF":4.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860456","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}

引用次数: 0