The increase in data paths and the resulting latency growth in Wireless Virtual Reality (WVR) can significantly affect user experience. Mobile Edge Computing emerges as an effective solution to address these issues. However, offloading methods based on Deep Reinforcement Learning (DRL) face hurdles like limited environmental exploration and prolonged user waiting time. To address the mentioned challenges in WVR edge computing, where computational offloading involves multiple devices and edge servers, we aim to minimize system latency and reduce energy consumption. Therefore, we introduce the Task Prediction and Multi-objective Optimization Algorithm (TPMOA). First, we reduce the time users wait for rendering results by predicting their viewpoints. Next, we apply an entropy-innovated DRL algorithm to the latent space for computation offloading. Through representation learning, we establish a reward function that includes latent objectives and optimizes the experience replay buffer. This approach allows us to train and select the optimal offloading strategy, thereby reducing rendering latency and system energy consumption. Our experiments show that our approach effectively tackles the challenges of limited environmental exploration ability and extended user waiting time. Specifically, our method outperforms the RNN-based AC method significantly, reducing latency by 11.39%.
{"title":"DRL-based latency-energy offloading optimization strategy in wireless VR networks with edge computing","authors":"Jieru Wang , Hui Xia , Lijuan Xu , Rui Zhang , Kunkun Jia","doi":"10.1016/j.comnet.2025.111034","DOIUrl":"10.1016/j.comnet.2025.111034","url":null,"abstract":"<div><div>The increase in data paths and the resulting latency growth in Wireless Virtual Reality (WVR) can significantly affect user experience. Mobile Edge Computing emerges as an effective solution to address these issues. However, offloading methods based on Deep Reinforcement Learning (DRL) face hurdles like limited environmental exploration and prolonged user waiting time. To address the mentioned challenges in WVR edge computing, where computational offloading involves multiple devices and edge servers, we aim to minimize system latency and reduce energy consumption. Therefore, we introduce the Task Prediction and Multi-objective Optimization Algorithm (TPMOA). First, we reduce the time users wait for rendering results by predicting their viewpoints. Next, we apply an entropy-innovated DRL algorithm to the latent space for computation offloading. Through representation learning, we establish a reward function that includes latent objectives and optimizes the experience replay buffer. This approach allows us to train and select the optimal offloading strategy, thereby reducing rendering latency and system energy consumption. Our experiments show that our approach effectively tackles the challenges of limited environmental exploration ability and extended user waiting time. Specifically, our method outperforms the RNN-based AC method significantly, reducing latency by 11.39%.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"258 ","pages":"Article 111034"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177368","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2025.111062
Zhen Qian, Guanghui Li, Tao Qi, Chenglong Dai
As the 5G technology and mobile smart devices evolve rapidly, the federated learning-based edge video caching has become a key technology to mitigate the explosive growth of traffic. However, due to energy-limited edge mobile devices, it is unrealistic to keep the maximum computational power of all smart devices in each round of communication in federated learning. Moreover, users’ implicit feedback behavior poses challenges to predicting popular content. To tackle these challenges, we propose a Federated deep Reinforcement learning-based Proactive Video Caching scheme (FRPVC), which not only improves the cache hit rate while addressing user privacy and security, but also minimizes the total system cost in energy-constrained mobile edge computing networks. FRPVC utilizes the user’s local implicit feedback data for training denoised auto-encoder models based on federated learning. We further formulate the user computational resource allocation problem as a Markov Decision Process (MDP) to minimize the expected long-term system cost and propose a DDQN-based resource allocation method to solve the optimal resource allocation policy, which can efficiently allocate the computational resources of each federated training client to minimize the total cost of the federated learning process. By validating under three real datasets, the experiments show that the proposed scheme outperforms the baseline algorithm in terms of cache hit rate and is close to the optimal algorithm. In addition, the experiments also show that FRPVC is able to effectively reduce the system cost under local resource constraints.
{"title":"Federated deep reinforcement learning-based cost-efficient proactive video caching in energy-constrained mobile edge networks","authors":"Zhen Qian, Guanghui Li, Tao Qi, Chenglong Dai","doi":"10.1016/j.comnet.2025.111062","DOIUrl":"10.1016/j.comnet.2025.111062","url":null,"abstract":"<div><div>As the 5G technology and mobile smart devices evolve rapidly, the federated learning-based edge video caching has become a key technology to mitigate the explosive growth of traffic. However, due to energy-limited edge mobile devices, it is unrealistic to keep the maximum computational power of all smart devices in each round of communication in federated learning. Moreover, users’ implicit feedback behavior poses challenges to predicting popular content. To tackle these challenges, we propose a Federated deep Reinforcement learning-based Proactive Video Caching scheme (FRPVC), which not only improves the cache hit rate while addressing user privacy and security, but also minimizes the total system cost in energy-constrained mobile edge computing networks. FRPVC utilizes the user’s local implicit feedback data for training denoised auto-encoder models based on federated learning. We further formulate the user computational resource allocation problem as a Markov Decision Process (MDP) to minimize the expected long-term system cost and propose a DDQN-based resource allocation method to solve the optimal resource allocation policy, which can efficiently allocate the computational resources of each federated training client to minimize the total cost of the federated learning process. By validating under three real datasets, the experiments show that the proposed scheme outperforms the baseline algorithm in terms of cache hit rate and is close to the optimal algorithm. In addition, the experiments also show that FRPVC is able to effectively reduce the system cost under local resource constraints.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"258 ","pages":"Article 111062"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177531","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2025.111039
Yang Yang , Jiaxing Zhang , Yanjiao Chen , Qing Huang , Fei Chen , Jing Chen
With more and more attention to the security of cloud storage, people are increasingly inclined to remotely store their encrypted data. In what follows, many searchable encryption (SE) schemes have been proposed. Unfortunately, the existing SE schemes under multi-user and multi-owner model are usually inefficient in owner-level and attribute-level access controls. Therefore, this paper aims to further improve the efficiency of the encrypted search with two-level access control. In our design, the owner-level permission is inspired by BLS signature and the attribute-level permission is based on CP-ABE, both of which are driven by simplifying the related keys as much as possible, allowing users to efficiently search data from multiple owners by using a single trapdoor. In addition, the proposed scheme can also efficiently support permission revocation, thanks to our simplified key for a user which can be independently updated without affecting other users. For the sake of security, we perform the random masking technique on encrypted index and searching trapdoor for hiding the keyword embedded in them, while keeping their matching relationship for keyword search. The proposed scheme is strictly proven to have the security properties of keyword secrecy, keyword irreplaceability, two-level controlled search and forward secrecy. Finally, we give plenty of theoretical analysis and experimental results to validate the superiority of the proposed scheme.
{"title":"An efficient encrypted search with owner-level and attribute-level access controls","authors":"Yang Yang , Jiaxing Zhang , Yanjiao Chen , Qing Huang , Fei Chen , Jing Chen","doi":"10.1016/j.comnet.2025.111039","DOIUrl":"10.1016/j.comnet.2025.111039","url":null,"abstract":"<div><div>With more and more attention to the security of cloud storage, people are increasingly inclined to remotely store their encrypted data. In what follows, many searchable encryption (SE) schemes have been proposed. Unfortunately, the existing SE schemes under multi-user and multi-owner model are usually inefficient in owner-level and attribute-level access controls. Therefore, this paper aims to further improve the efficiency of the encrypted search with two-level access control. In our design, the owner-level permission is inspired by BLS signature and the attribute-level permission is based on CP-ABE, both of which are driven by simplifying the related keys as much as possible, allowing users to efficiently search data from multiple owners by using a single trapdoor. In addition, the proposed scheme can also efficiently support permission revocation, thanks to our simplified key for a user which can be independently updated without affecting other users. For the sake of security, we perform the random masking technique on encrypted index and searching trapdoor for hiding the keyword embedded in them, while keeping their matching relationship for keyword search. The proposed scheme is strictly proven to have the security properties of keyword secrecy, keyword irreplaceability, two-level controlled search and forward secrecy. Finally, we give plenty of theoretical analysis and experimental results to validate the superiority of the proposed scheme.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"258 ","pages":"Article 111039"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177532","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2024.110999
Jiang Xie , Shuhao Li , Xiaochun Yun , Chengxiang Si , Tao Yin
Network attacks pose serious threats to cybersecurity. Researchers provide well-known malicious sample datasets for evaluating methods to detect these attacks. However, we discover that these datasets exhibit a multi-label phenomenon, where a sample has multiple labels. Multi-label problems are ubiquitous, such as in malware detection, where different engines could assign different labels to the same unknown software. But multi-label phenomenon in computer network datasets is different from the traditional multi-label problem. These datasets, which are by default single-labeled, annotated, published, and utilized to evaluate various single-label detection methods. Researchers ignore the possibility that the samples within the datasets may be multi-labeled. Therefore, it is inappropriate to directly utilize these data for evaluating single-label detection methods.
In this paper, we focus on well-known malicious traffic and malware datasets with a comprehensive study, including sample analysis and multi-label classification: (1) We perform comprehensive statistics on 15 datasets, quantify the proportion of multi-label samples and the number of categories affected in them, and analyze the intrinsic connections between attacks. (2) We employ multiple classical multi-label algorithms to classify the multi-label samples in 9 datasets, and the experimental results show that they are superior to the single-label state-of-the-art (SOTA) method, and can improve accuracy and F1 by 39.6% and 57.69% on average.
We conclude that the multi-label phenomenon is ubiquitous in malicious traffic and malware datasets, and it should be considered in network attack detection.
{"title":"Sample analysis and multi-label classification for malicious sample datasets","authors":"Jiang Xie , Shuhao Li , Xiaochun Yun , Chengxiang Si , Tao Yin","doi":"10.1016/j.comnet.2024.110999","DOIUrl":"10.1016/j.comnet.2024.110999","url":null,"abstract":"<div><div>Network attacks pose serious threats to cybersecurity. Researchers provide well-known malicious sample datasets for evaluating methods to detect these attacks. However, we discover that these datasets exhibit a multi-label phenomenon, where a sample has multiple labels. Multi-label problems are ubiquitous, such as in malware detection, where different engines could assign different labels to the same unknown software. But multi-label phenomenon in computer network datasets is different from the traditional multi-label problem. These datasets, which are by default single-labeled, annotated, published, and utilized to evaluate various single-label detection methods. Researchers ignore the possibility that the samples within the datasets may be multi-labeled. Therefore, it is inappropriate to directly utilize these data for evaluating single-label detection methods.</div><div>In this paper, we focus on well-known malicious traffic and malware datasets with a comprehensive study, including sample analysis and multi-label classification: (1) We perform comprehensive statistics on 15 datasets, quantify the proportion of multi-label samples and the number of categories affected in them, and analyze the intrinsic connections between attacks. (2) We employ multiple classical multi-label algorithms to classify the multi-label samples in 9 datasets, and the experimental results show that they are superior to the single-label state-of-the-art (SOTA) method, and can improve accuracy and F1 by 39.6% and 57.69% on average.</div><div>We conclude that the multi-label phenomenon is ubiquitous in malicious traffic and malware datasets, and it should be considered in network attack detection.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"258 ","pages":"Article 110999"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178146","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2024.110997
Xiaorong Liu , Danyang Zheng , Huanlai Xing , Li Feng , Chengzong Peng , Xiaojun Cao
Recently, the in-network computing (INC) technique has been widely adopted by various applications including the reliability-sensitive ones such as remote surgery, and autonomous vehicle systems. To deploy reliable INC-enabled services, redundant task replicas are hosted by network devices to meet a specified service reliability threshold such as 99.9%, 99.99%, and 99.999%. Most existing works assume that this threshold is solely impacted by the software reliability while neglecting the hardware reliability. This neglectedness likely leads to unexpected service interruptions when the software replicas are co-deployed over one unreliable hardware. This work jointly considers the heterogeneous reliability brought by both software and hardware and identifies a novel phenomenon called “Software-Reliability-Only Experience Degradation” (SRO-ED). To address this, we mathematically establish the INC-enabled services adoption with heterogeneous reliability (ISAHR) problem to optimize service costs and prove its NP-hardness. We introduce an effective Cost-Reliability (CR) measure to indicate the average cost needed to satisfy each reliability unit while considering both software and hardware reliabilities. Next, we propose an innovative algorithm called CR measure-based INC services deployment (CR-D), which is proved to be logarithm-approximate in cost optimization. Extensive simulation results validate the logarithmic approximation of CR-D, and show that it outperforms the benchmarks by an average of 29.42% and 35.77% in cost optimization.
{"title":"A cost-provable solution for reliable in-network computing-enabled services deployment","authors":"Xiaorong Liu , Danyang Zheng , Huanlai Xing , Li Feng , Chengzong Peng , Xiaojun Cao","doi":"10.1016/j.comnet.2024.110997","DOIUrl":"10.1016/j.comnet.2024.110997","url":null,"abstract":"<div><div>Recently, the in-network computing (INC) technique has been widely adopted by various applications including the reliability-sensitive ones such as remote surgery, and autonomous vehicle systems. To deploy reliable INC-enabled services, redundant task replicas are hosted by network devices to meet a specified service reliability threshold such as 99.9%, 99.99%, and 99.999%. Most existing works assume that this threshold is solely impacted by the software reliability while neglecting the hardware reliability. This neglectedness likely leads to unexpected service interruptions when the software replicas are co-deployed over one unreliable hardware. This work jointly considers the heterogeneous reliability brought by both software and hardware and identifies a novel phenomenon called “Software-Reliability-Only Experience Degradation” (SRO-ED). To address this, we mathematically establish the INC-enabled services adoption with heterogeneous reliability (ISAHR) problem to optimize service costs and prove its NP-hardness. We introduce an effective Cost-Reliability (CR) measure to indicate the average cost needed to satisfy each reliability unit while considering both software and hardware reliabilities. Next, we propose an innovative algorithm called CR measure-based INC services deployment (CR-D), which is proved to be logarithm-approximate in cost optimization. Extensive simulation results validate the logarithmic approximation of CR-D, and show that it outperforms the benchmarks by an average of 29.42% and 35.77% in cost optimization.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"257 ","pages":"Article 110997"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129158","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2024.111005
Guangrong Yang , An He , Guangwei Wu , Jianing Zhao , Jinhuan Zhang , Anfeng Liu
With the rapid development of mobile sensing technology and Unmanned Aerial Vehicles (UAVs) technology, Mobile Crowd Sensing (MCS) system are playing an increasingly important role in smart cities. Billions of Sensor Devices(SDs) generate vast amounts of data, providing strong support for the efficient functioning of smart cities. However, collecting this data in a low-cost, secure, and efficient manner presents significant challenges. Malicious Mobile Data Collectors (MDCs) may transmit false data, which can affect the quality of services. Therefore, ensuring the high quality of data collection is crucial for the security and performance of smart city applications. A Hash Value Verification joint UAVs (HVVU) Scheme is proposed for trust assessment and selection between MDCs and Internet of Things devices. This scheme aims to select Collectors that are both low-cost and highly trustworthy, thereby enhancing data quality. (1) An active verifiable trust evaluation framework assisted by UAVs is proposed. The data collected by UAVs is used as a baseline and compared with the data submitted by MDCs to calculate and update their verification scores, effectively preventing collusion attacks. (2) A hash-based interactive trust evaluation scheme is proposed, where MDCs interact with others during data collection and calculate the hash value of the interaction packets. This process ensures the authenticity and reliability of the data while simultaneously updating interaction scores in real-time. (3) A ranking mechanism based on the overall contribution degree and a winning strategy selection method based on the ranking are proposed. These methods take into account the comprehensive scores, bids, and collection rates, incentivizing MDCs to maintain data quality while reducing costs. (4) A UAV path encryption and secondary optimization scheme is proposed. This scheme securely collects data from more SDs while controlling energy consumption. The experimental results indicate that, compared with the Collaboration Trust Interconnections System (CTIS), HVVU scheme increases the collection rate while controlling costs, with data accuracy nearly 100%. It achieves an approximate recognition rate of 98% for malicious MDCs, with a false recognition rate controlled at around 2%. This demonstrates the effectiveness and practicality of the scheme.
{"title":"HVVU: A Hash Value Verification joint UAVs scheme for trust data collection in smart cities","authors":"Guangrong Yang , An He , Guangwei Wu , Jianing Zhao , Jinhuan Zhang , Anfeng Liu","doi":"10.1016/j.comnet.2024.111005","DOIUrl":"10.1016/j.comnet.2024.111005","url":null,"abstract":"<div><div>With the rapid development of mobile sensing technology and Unmanned Aerial Vehicles (UAVs) technology, Mobile Crowd Sensing (MCS) system are playing an increasingly important role in smart cities. Billions of Sensor Devices(SDs) generate vast amounts of data, providing strong support for the efficient functioning of smart cities. However, collecting this data in a low-cost, secure, and efficient manner presents significant challenges. Malicious Mobile Data Collectors (MDCs) may transmit false data, which can affect the quality of services. Therefore, ensuring the high quality of data collection is crucial for the security and performance of smart city applications. A Hash Value Verification joint UAVs (HVVU) Scheme is proposed for trust assessment and selection between MDCs and Internet of Things devices. This scheme aims to select Collectors that are both low-cost and highly trustworthy, thereby enhancing data quality. (1) An active verifiable trust evaluation framework assisted by UAVs is proposed. The data collected by UAVs is used as a baseline and compared with the data submitted by MDCs to calculate and update their verification scores, effectively preventing collusion attacks. (2) A hash-based interactive trust evaluation scheme is proposed, where MDCs interact with others during data collection and calculate the hash value of the interaction packets. This process ensures the authenticity and reliability of the data while simultaneously updating interaction scores in real-time. (3) A ranking mechanism based on the overall contribution degree and a winning strategy selection method based on the ranking are proposed. These methods take into account the comprehensive scores, bids, and collection rates, incentivizing MDCs to maintain data quality while reducing costs. (4) A UAV path encryption and secondary optimization scheme is proposed. This scheme securely collects data from more SDs while controlling energy consumption. The experimental results indicate that, compared with the Collaboration Trust Interconnections System (CTIS), HVVU scheme increases the collection rate while controlling costs, with data accuracy nearly 100%. It achieves an approximate recognition rate of 98% for malicious MDCs, with a false recognition rate controlled at around 2%. This demonstrates the effectiveness and practicality of the scheme.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"257 ","pages":"Article 111005"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129199","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2024.110978
Junhao Ma, Yantao Yu, Guojin Liu, Tiancong Huang
Vehicular crowdsensing leverages the mobility and sensing capabilities of vehicles to provide efficient data collection and monitoring services for urban areas. However, maintaining data freshness in urban sensing environments while addressing issues such as complex spatiotemporal data correlations, dynamic city conditions, and the trade-off between task performance and costs remains a significant challenge. To address this issue, we propose freshness aware Multi-Vehicular Crowdsensing (freshMVCS), a decentralized multi-agent deep reinforcement learning framework for long-term vehicular scheduling in data collection tasks. Following the decentralized training decentralized execution paradigm, each agent in freshMVCS is embedded with an independent recurrent neural network and intrinsic reward mechanism to enhance exploration capabilities, while achieving collaboration through shared task information. Extensive experiments conducted on real-world datasets demonstrate that the freshMVCS approach excels in maintaining data freshness, achieving high collection rates, and minimizing Age of Information threshold violations. These results indicate its effectiveness in accomplishing long-term data collection tasks within complex and dynamic urban sensing environments.
{"title":"Freshness aware vehicular crowdsensing with multi-agent reinforcement learning","authors":"Junhao Ma, Yantao Yu, Guojin Liu, Tiancong Huang","doi":"10.1016/j.comnet.2024.110978","DOIUrl":"10.1016/j.comnet.2024.110978","url":null,"abstract":"<div><div>Vehicular crowdsensing leverages the mobility and sensing capabilities of vehicles to provide efficient data collection and monitoring services for urban areas. However, maintaining data freshness in urban sensing environments while addressing issues such as complex spatiotemporal data correlations, dynamic city conditions, and the trade-off between task performance and costs remains a significant challenge. To address this issue, we propose freshness aware Multi-Vehicular Crowdsensing (freshMVCS), a decentralized multi-agent deep reinforcement learning framework for long-term vehicular scheduling in data collection tasks. Following the decentralized training decentralized execution paradigm, each agent in freshMVCS is embedded with an independent recurrent neural network and intrinsic reward mechanism to enhance exploration capabilities, while achieving collaboration through shared task information. Extensive experiments conducted on real-world datasets demonstrate that the freshMVCS approach excels in maintaining data freshness, achieving high collection rates, and minimizing Age of Information threshold violations. These results indicate its effectiveness in accomplishing long-term data collection tasks within complex and dynamic urban sensing environments.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"257 ","pages":"Article 110978"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129308","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}
Existing Attribute-Based Access Control (ABAC) systems face significant challenges, primarily due to their reliance on a single attribute management authority, which raises concerns regarding reliability and security. To address these issues, this paper presents SharAcc, an innovative sharded blockchain-based access control system that decentralizes attribute management and access control processes. By leveraging sharding technology, SharAcc distributes attribute management and access control processes across multiple shards, significantly boosting system efficiency and security. The core innovations of SharAcc include a multi-shard architecture with attribute management shards and a validation shard, an improved Merkle Patricia Trie (iMPT) for efficient user attribute storage and management, a Merkle Directed Acyclic Graph (Merkle DAG) for precise transaction recording and verification, and a batch consensus mechanism based on skip lists to ensure efficient transaction verification. Experimental results demonstrate that SharAcc effectively overcomes the scalability and security limitations of existing ABAC schemes, achieving a throughput of up to 10,000 transactions per second and a confirmation latency reduced to as low as 200 ms. This decentralized design and sharded blockchain approach offer new perspectives for future access control systems.
{"title":"SharAcc: Enhancing scalability and security in Attribute-Based Access Control with sharding-based blockchain and full decentralization","authors":"Yuqing Ding , Zhongcheng Wu , Yongchun Miao , Manyu Ding","doi":"10.1016/j.comnet.2024.110992","DOIUrl":"10.1016/j.comnet.2024.110992","url":null,"abstract":"<div><div>Existing Attribute-Based Access Control (ABAC) systems face significant challenges, primarily due to their reliance on a single attribute management authority, which raises concerns regarding reliability and security. To address these issues, this paper presents SharAcc, an innovative sharded blockchain-based access control system that decentralizes attribute management and access control processes. By leveraging sharding technology, SharAcc distributes attribute management and access control processes across multiple shards, significantly boosting system efficiency and security. The core innovations of SharAcc include a multi-shard architecture with attribute management shards and a validation shard, an improved Merkle Patricia Trie (iMPT) for efficient user attribute storage and management, a Merkle Directed Acyclic Graph (Merkle DAG) for precise transaction recording and verification, and a batch consensus mechanism based on skip lists to ensure efficient transaction verification. Experimental results demonstrate that SharAcc effectively overcomes the scalability and security limitations of existing ABAC schemes, achieving a throughput of up to 10,000 transactions per second and a confirmation latency reduced to as low as 200 ms. This decentralized design and sharded blockchain approach offer new perspectives for future access control systems.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"257 ","pages":"Article 110992"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129378","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2025.111035
Haopeng Ma, Xiaoying Huang, Ke Ruan, Zehua Hu, Yongqing Zhu
In contemporary computer networks, precise prediction of network traffic is essential for efficient resource allocation, congestion control, and the delivery of high-quality services. Some models decompose raw traffic data into seasonal and trend components and employ distinct modeling strategies for each forecasting task. Nevertheless, these models fail to fully leverage multiscale information to enhance the representation of seasonal and trend components. Fine-grained data at larger scales more accurately captures seasonal features with inherent periodicity, thereby significantly enhancing coarse-grained traffic characteristics. Moreover, the coarse-grained feature effectively guides the overall trajectory of the fine-grained feature within the trend component and mitigates the impact of noise on them. In addition, current models suffer from a lack of effective integration between local and global attention features, which hinders their ability to extract complex traffic features in high-precision prediction scenarios. To tackle these challenges, we introduce an innovative framework known as the Multiscale Sparse Time-Frequency Cross Attention Network (MSTFCAN). This framework proposes a mechanism for enhancing and fusing multi-scale trend and seasonal features, while utilizing the sparse time-frequency cross attention mechanism to extract and fuse time-domain and spectral information at each scale. The MSTFCAN framework introduces Multichannel Variable Convergence (MCVCon) modules, Multiscale Seasonality-Trend Decomposition Fusion Engine (MSDFE), and Sparse Time-Frequency Cross-Attention Unit (STFCAU) to bolster the model’s capability of feature extraction and variable interactions for raw flow data. To demonstrate the superior performance of the MSTFCAN framework in terms of prediction accuracy, extensive experiments have been conducted on real-world datasets.
{"title":"MSTFCAN: Multiscale sparse temporal-frequency cross attention network for traffic prediction","authors":"Haopeng Ma, Xiaoying Huang, Ke Ruan, Zehua Hu, Yongqing Zhu","doi":"10.1016/j.comnet.2025.111035","DOIUrl":"10.1016/j.comnet.2025.111035","url":null,"abstract":"<div><div>In contemporary computer networks, precise prediction of network traffic is essential for efficient resource allocation, congestion control, and the delivery of high-quality services. Some models decompose raw traffic data into seasonal and trend components and employ distinct modeling strategies for each forecasting task. Nevertheless, these models fail to fully leverage multiscale information to enhance the representation of seasonal and trend components. Fine-grained data at larger scales more accurately captures seasonal features with inherent periodicity, thereby significantly enhancing coarse-grained traffic characteristics. Moreover, the coarse-grained feature effectively guides the overall trajectory of the fine-grained feature within the trend component and mitigates the impact of noise on them. In addition, current models suffer from a lack of effective integration between local and global attention features, which hinders their ability to extract complex traffic features in high-precision prediction scenarios. To tackle these challenges, we introduce an innovative framework known as the Multiscale Sparse Time-Frequency Cross Attention Network (MSTFCAN). This framework proposes a mechanism for enhancing and fusing multi-scale trend and seasonal features, while utilizing the sparse time-frequency cross attention mechanism to extract and fuse time-domain and spectral information at each scale. The MSTFCAN framework introduces Multichannel Variable Convergence (MCVCon) modules, Multiscale Seasonality-Trend Decomposition Fusion Engine (MSDFE), and Sparse Time-Frequency Cross-Attention Unit (STFCAU) to bolster the model’s capability of feature extraction and variable interactions for raw flow data. To demonstrate the superior performance of the MSTFCAN framework in terms of prediction accuracy, extensive experiments have been conducted on real-world datasets.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"258 ","pages":"Article 111035"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177346","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}
Pub Date : 2025-02-01DOI: 10.1016/j.comnet.2024.110990
Kai Zhao, Lina Wang, Fangchao Yu, Bo Zeng, Zhi Pang
Federated learning (FL) is an increasingly popular privacy-preserving collaborative machine learning paradigm that enables clients to train a global model collaboratively without sharing their raw data. Despite its advantages, FL is vulnerable to untargeted Byzantine poisoning attacks in which malicious clients send incorrect model updates during training to disrupt the global model’s performance or prevent it from converging. Existing defenses based on anomaly detection typically rely on additional auxiliary datasets and assume a known and fixed proportion of malicious clients. To overcome these shortcomings, we propose FedMP, a multi-pronged defense algorithm against untargeted Byzantine poisoning attacks. FedMP’s primary idea is to detect anomalous variations in the magnitude and direction of model updates across communication rounds. In particular, FedMP first utilizes an adaptive scaling module to limit the impact of malicious updates with anomalous amplitudes. Then, FedMP identifies and filters malicious model updates with abnormal directions through dynamic clustering and partial filtering methods. Finally, FedMP extracts pure ingredients from the filtered updates as reputation scores for model aggregation to further reduce the influence of malicious updates. Comprehensive evaluations across three publicly accessible datasets demonstrate that FedMP significantly outperforms the existing Byzantine robust defenses under a high proportion of malicious clients (0.7 in our experiments) and high Non-IID degree (0.1 in our experiments) scenarios.
{"title":"FedMP: A multi-pronged defense algorithm against Byzantine poisoning attacks in federated learning","authors":"Kai Zhao, Lina Wang, Fangchao Yu, Bo Zeng, Zhi Pang","doi":"10.1016/j.comnet.2024.110990","DOIUrl":"10.1016/j.comnet.2024.110990","url":null,"abstract":"<div><div>Federated learning (FL) is an increasingly popular privacy-preserving collaborative machine learning paradigm that enables clients to train a global model collaboratively without sharing their raw data. Despite its advantages, FL is vulnerable to untargeted Byzantine poisoning attacks in which malicious clients send incorrect model updates during training to disrupt the global model’s performance or prevent it from converging. Existing defenses based on anomaly detection typically rely on additional auxiliary datasets and assume a known and fixed proportion of malicious clients. To overcome these shortcomings, we propose FedMP, a multi-pronged defense algorithm against untargeted Byzantine poisoning attacks. FedMP’s primary idea is to detect anomalous variations in the magnitude and direction of model updates across communication rounds. In particular, FedMP first utilizes an adaptive scaling module to limit the impact of malicious updates with anomalous amplitudes. Then, FedMP identifies and filters malicious model updates with abnormal directions through dynamic clustering and partial filtering methods. Finally, FedMP extracts pure ingredients from the filtered updates as reputation scores for model aggregation to further reduce the influence of malicious updates. Comprehensive evaluations across three publicly accessible datasets demonstrate that FedMP significantly outperforms the existing Byzantine robust defenses under a high proportion of malicious clients (0.7 in our experiments) and high Non-IID degree (0.1 in our experiments) scenarios.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"257 ","pages":"Article 110990"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129161","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}
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