Computer Networks最新文献_第5页

Research on indoor multi-floor positioning method based on LoRa 基于 LoRa 的室内多楼层定位方法研究

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110838

{"title":"Research on indoor multi-floor positioning method based on LoRa","authors":"","doi":"10.1016/j.comnet.2024.110838","DOIUrl":"10.1016/j.comnet.2024.110838","url":null,"abstract":"<div><div>Existing floor localization methods are plagued by low accuracy, high algorithmic complexity, dense node deployment, susceptibility to environmental factors, and the inability to track trajectories. This paper introduces a localization method designed to address the challenges of multi-floor environments, leveraging LoRa technology. The approach involves deploying LoRa vertical positioning devices and establishing offline and threshold fingerprint databases. To enhance localization accuracy, it combines Time-of-Flight (TOF) ranging values (referred to as \"RANGE\" in this paper) with Received Signal Strength Indicator (RSSI) values, referred to as \"RSSI-RANGE\". Subsequently, a multi-floor determination is achieved using the RSSI-RANGE floor determination algorithm and a range-based signal source autonomous switching mechanism. The fingerprinting technique is then employed for trajectory recognition. Comprehensive vertical information is obtained by combining floor determination and trajectory award. Gaussian filtering is utilized for fingerprint preprocessing to eliminate gross errors. The particle swarm optimization algorithm is employed to fine-tune the hyperparameters of the random forest algorithm following noise reduction. Using the random forest algorithm, optimal RSSI-RANGE values are derived, and the offline fingerprint database is established by applying Kriging interpolation. Localization is then achieved in the concluding online recognition phase. Empirical findings illustrate the system's high floor accuracy rate of 97.8%, achieving high determination accuracy and comprehensive floor localization when combined with trajectory recognition.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge-device collaborative computing for multi-view classification 多视角分类的边缘设备协同计算

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110823

{"title":"Edge-device collaborative computing for multi-view classification","authors":"","doi":"10.1016/j.comnet.2024.110823","DOIUrl":"10.1016/j.comnet.2024.110823","url":null,"abstract":"<div><div>Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore <em>collaborative inference</em> at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce <em>selective schemes</em> that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419305","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

Integrated trajectory optimization for UAV-enabled wireless powered MEC system with joint energy consumption and AoI minimization 针对无人机无线供电 MEC 系统的综合轨迹优化，实现能耗和 AoI 的联合最小化

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-03 DOI: 10.1016/j.comnet.2024.110842

{"title":"Integrated trajectory optimization for UAV-enabled wireless powered MEC system with joint energy consumption and AoI minimization","authors":"","doi":"10.1016/j.comnet.2024.110842","DOIUrl":"10.1016/j.comnet.2024.110842","url":null,"abstract":"<div><div>This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered mobile edge computing (MEC) system, where a UAV, equipped with RF Chains and MEC servers, can sustainably provide wireless energy for charging Internet of Things (IoT) devices and executing computing tasks from these devices while hovering at designated hover points. Our goal is to minimize the weighted sum of energy consumption and Age of information (AoI) in this system, which depended on the UAV’s hovering time at designated points and its flying time. To achieve this, we jointly optimize the deployment of hover points and the visiting order of these points by the UAV. It is NP hard and mixed-integer non-convex which is difficult to solve by traditional methods. To tackle this problem, we present a trajectory optimization algorithm for joint energy consumption and AoI (TOJEA), which consists of two phases. In the first phase, an Equilibrium Optimizer (EO) algorithm with a variable individual size via its coding and updating strategies, in which each particle (individual) with its concentration (position) represents a target solution i.e. the whole deployment of hover points, is proposed to optimize the number and locations of hover points. Based on the deployment of hover points, a low-complexity greedy algorithm is adopted in the second stage to generate the optimal visiting order for the UAV. Experimental results demonstrate that TOJEA outperforms other algorithms on ten instances with up to 400 IoT devices.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419366","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

Secure and Efficient Authentication using Linkage for permissionless Bitcoin network 在无权限比特币网络中使用链接进行安全高效的身份验证

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-02 DOI: 10.1016/j.comnet.2024.110840

{"title":"Secure and Efficient Authentication using Linkage for permissionless Bitcoin network","authors":"","doi":"10.1016/j.comnet.2024.110840","DOIUrl":"10.1016/j.comnet.2024.110840","url":null,"abstract":"<div><div>The cryptocurrency’s permissionless and large-scale broadcasting requirements prohibit the traditional authentication implementation on the blockchain’s underlying peer-to-peer (P2P) networking. Thus, blockchain networking implementations remain vulnerable to networking integrity threats such as spoofing or hijacking. We design Secure and Efficient Authentication using Linkage (SEAL) to build connection security for permissionless Bitcoin networking. SEAL uses the linkage between the packets for a symmetric operation, in contrast to the traditional authentication approach relying on identity-credential-based trust. To make it appropriate for cryptocurrency networking, SEAL utilizes the packet header, protects the end-to-end connection, and separates the online process and the offline process so that the real-time overhead is minimal for greater efficiency and practicality. We implement SEAL on a functioning Bitcoin node and demonstrate that SEAL operates efficiently with minimal overhead. Specifically, it reduces the hash rate by only 1.3% compared to an unsecured node. Additionally, we use a network simulator to emulate the Bitcoin Mainnet and analyze SEAL’s impact on block propagation delay. SEAL yields 2.04 times smaller delay and 1.25 times smaller delay in block propagation than HMAC and ChaCha20-Poly1305, respectively. The key advantage of SEAL is that it requires fewer hash computations and simpler mixing operations, resulting in significantly lower computational overhead compared to traditional authentication schemes based on message authentication codes (MACs).</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419362","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

DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G DT-Block：面向 6G 安全高效通信的自适应垂直联合强化学习方案

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-01 DOI: 10.1016/j.comnet.2024.110841

{"title":"DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G","authors":"","doi":"10.1016/j.comnet.2024.110841","DOIUrl":"10.1016/j.comnet.2024.110841","url":null,"abstract":"<div><div>The necessities of security and data sharing have focused on federated learning because of using decentralized data sources. The existing works used federated learning for security, however, it still faces many challenges such as poor security and privacy, computational complexity, etc. In this research, we propose adaptive vertical federated learning using a reinforcement learning approach and blockchain. The proposed work includes three phases: user registration and authentication, machine learning-based client selection, and adaptive secure federated learning. Initially, all the users register their credentials to the cognitive agent, which generates a private key, public key, and random number using a Chaotic Isogenic Post Quantum Cryptography (CIPQC) algorithm. Second, optimal clients are selected for participating in federated learning which improves learning rate and reduces complexity. Here, optimal clients are selected by the Enhanced Multilayer Feed Forward Neural Network (EMFFN) algorithm by considering CSI, RSSI, bandwidth, energy, communication efficiency, and statistical efficiency. Finally, adaptive secure federated learning is performed by the Distributed Distributional Deep Deterministic Policy Gradient (D<span><math><msup><mrow></mrow><mrow><mn>4</mn></mrow></msup></math></span>PG) algorithm, where the local models are adaptively used by the private strategy based on its sensitivity. The aggregated global models are stored in DT-block (dendrimer tree-based blockchain) which stores the data in a dendrimer tree structure for increasing scalability and reducing search time during data retrieval. The simulation of this research is conducted by NS-3.26 network simulator and the performance of the proposed DT-Block model is estimated based on various performance metrics such as accuracy, delay, loss, f1-score, and security strength this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419303","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

Cyber threat indicators extraction based on contextual knowledge prompt 基于上下文知识提示的网络威胁指标提取

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-01 DOI: 10.1016/j.comnet.2024.110839

{"title":"Cyber threat indicators extraction based on contextual knowledge prompt","authors":"","doi":"10.1016/j.comnet.2024.110839","DOIUrl":"10.1016/j.comnet.2024.110839","url":null,"abstract":"<div><div>Extracting Indicators of Compromise (IOC) from security-related social data (e.g., security blogs, hacker forums) is crucial for predicting cyber risks and mitigating cyber attacks proactively. However, existing IOC extraction approaches suffer from two serious limitations. First, they fail to learn the multiculti-granular and fine-grained IOC features, resulting in high false positives. Second, current methods cannot incorporate symbolic rules and contextual knowledge, resulting in poor interpretability. In this paper, we propose AIIOC, an <u>A</u>ccurate and <u>I</u>nterpretable <u>I</u> <u>O</u> <u>C</u> extraction model based on contextual knowledge prompts. Particularly, AIIOC first proposes a multi-granularity attention mechanism to learn fine-grained IOC features and boost the accuracy of IOC identification. Additionally, AIIOC designs a novel sequence labeling method that integrates symbolic rules and contextual knowledge prompts, which can encode symbolic rules and contextual semantics of IOC in trainable recurrent neural networks to improve both accuracy and interpretability. Experimental results on two real-world datasets verify that AIIOC outperforms state-of-the-art methods and showcases promising interpretability by incorporating symbolic rules and contextual knowledge prompts.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419363","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

Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks 在 CR-IoT 网络中利用专用能量发射器实现高效多通道能量采集

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-01 DOI: 10.1016/j.comnet.2024.110834

{"title":"Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks","authors":"","doi":"10.1016/j.comnet.2024.110834","DOIUrl":"10.1016/j.comnet.2024.110834","url":null,"abstract":"<div><div>Radio Frequency (RF) energy harvesting is strongly believed to be a sustainable solution to the power depletion problem in battery powered IoT devices. In addition to harvesting energy from ambient RF signals, the use of dedicated energy transmitters (ETs) that transmit energy to nearby IoT devices via RF signals has recently been proposed. In this paper, we study the problem of designing an energy harvesting policy for a group of cognitive radio-enabled IoT (CR-IoT) devices served by a number of ETs to maximize the minimum of their charging rates. With the help of cognitive radios, a CR-IoT node is capable of changing its frequency channel of operation allowing for multi-channel energy harvesting. Frequency channels are assumed to be opportunistically accessible depending on the activity of wireless users that are licensed to use those channels. The problem entails the design of the ET’s transmission policy (to what CR-IoT device, and over what channel) and the design of an ambient harvesting policy for every CR-IoT device (when it is not served by ETs). The problem is formulated as a mixed integer linear program (MILP). The objective is to maximize a lower bound on the total harvested energy in a given time frame per CR-IoT node. This optimization is subject to scheduling, total energy budget, and maximum transmit power constraints. Given the intractability of MILP formulations, a sub-optimal algorithm is proposed. Extensive experimentation is carried out to assess the effectiveness of the proposed sub-optimal algorithm by comparing it to the MILP’s solution obtained using IBM CPLEX solver with a limit on the execution time. We also combine our sub-optimal algorithm withe the CPLEX solver to produce a new two-stages algorithm that improves the original one by around 47%. Finally, we investigate the effect of multiple parameters including number of ETs, number of channels, and channel availability probability on the minimum charging rate.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419312","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

aCroSS: AI-driven cross-layer adaptive streaming for short video applications aCroSS：面向短视频应用的人工智能驱动跨层自适应流媒体技术

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-09-30 DOI: 10.1016/j.comnet.2024.110832

{"title":"aCroSS: AI-driven cross-layer adaptive streaming for short video applications","authors":"","doi":"10.1016/j.comnet.2024.110832","DOIUrl":"10.1016/j.comnet.2024.110832","url":null,"abstract":"<div><div>As short video applications gain popularity, researchers are exploring ways to enhance Quality of Experience (QoE) for short videos while maximizing network bandwidth efficiency. Despite the growing interest, existing Adaptive Bitrate (ABR) algorithms primarily concentrate on content prefetching strategies and often overlook the dynamic interaction between network congestion control and ABR. This interaction is especially critical for short video streaming, where network conditions can fluctuate rapidly, and user expectations for seamless playback are high. To address these challenges, we propose aCroSS, an AI-driven framework for adaptive short video streaming that jointly optimizes both the application and transport layers to enhance QoE and bandwidth utilization. The aCroSS algorithm leverages advanced machine learning techniques to adapt in real time to fluctuating network conditions and dynamic user behaviors, delivering a more robust and responsive streaming experience. Our simulation results demonstrate that aCroSS consistently outperforms existing baseline algorithms, achieving more than a 10% improvement in utility scores across various network trace datasets. This highlights the effectiveness of aCroSS in delivering superior performance in diverse streaming environments.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419361","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 fault tolerant node placement algorithm for WSNs and IoT networks 用于 WSN 和 IoT 网络的容错节点放置算法

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-09-30 DOI: 10.1016/j.comnet.2024.110835

{"title":"A fault tolerant node placement algorithm for WSNs and IoT networks","authors":"","doi":"10.1016/j.comnet.2024.110835","DOIUrl":"10.1016/j.comnet.2024.110835","url":null,"abstract":"<div><div>The operation of Internet of Things (IoT) Network and Wireless Sensor Networks (WSNs) can be often disrupted by a number of factors, such as node faults, security attacks, as well as path disconnections. Despite any disruptions or any failures of network components the functionality and performance of the network should remain unaltered. An approach that can assist in resolving the above issues is the use of mobile nodes. In this work, we present a Fault Tolerant Node Placement Algorithm (FTNPA) that utilizes mobile nodes for addressing failures in the network. We initially propose a Mobile Fault Tolerant (MobileFT) Framework, that supplies the two main functionalities of the Fault Tolerant Node Placement Algorithm (FTNPA), which are the detection and the recovery. Then, we present two variations of the FTNPA algorithm, the Decentralized FTNPA and the Centralized FTNPA. The first variation uses a decentralized detection method, where the detection is performed by the neighboring nodes in the network, as well as a local recovery method, where a mobile node is placed in a certain position to assist the affected area. Whereas, the second variation employs a centralized detection method, where the sink is responsible for the detection, and a recovery method that creates alternatives paths of mobile nodes towards a destination node. Simulation results show that the proposed algorithms can significantly contribute to the detection and recovery of faults in IoT Networks and WSNs.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419359","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 blind flow fingerprinting and correlation method against disturbed anonymous traffic based on pattern reconstruction 基于模式重构的受干扰匿名流量盲流指纹识别和关联方法

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-09-29 DOI: 10.1016/j.comnet.2024.110831

{"title":"A blind flow fingerprinting and correlation method against disturbed anonymous traffic based on pattern reconstruction","authors":"","doi":"10.1016/j.comnet.2024.110831","DOIUrl":"10.1016/j.comnet.2024.110831","url":null,"abstract":"<div><div>Tor is the most widely used anonymous communication system at present which can anonymize users’ network behavior. At the same time, many illegal network activities also appear more frequently with the help of Tor, posing serious challenges for cyberspace security. Therefore, flow fingerprinting and flow correlation analysis methods are put forward to de-anonymize the malicious anonymous behaviors, which utilize external traffic features as the side-channel information. However, the adversary often reduces the ability of above two methods by adding the disturbance to the anonymous traffic. As a countermeasure against the interference, disturbance-resistant analysis methods can effectively identify those adversarial behaviors while knowing how the traffic is modified. However, in real scenarios, it is unrealistic to distinguish between disturbed and non-disturbed anonymous traffic, let alone to have a clear grasp of the disturbing strategy. In this paper, we propose a blind anonymous traffic analysis method called Blind Analyzer based on pattern reconstruction skills in a “masking-generation” manner. Specifically, Blind Analyzer extracts the pattern knowledge from non-disturbed traffic samples by masking and reconstructing them. During the method application, disturbed anonymous traces are processed following the same way, aiming at removing the incremental noise at the masking stage and restoring the original shape at the reconstruction stage. Besides, a conditional discriminator is designed to determine whether the generated sample has obvious class characteristics. Benefited from the proposed method, we can improve the effectiveness of the anonymous network behavior analysis since the disturbed traffic can be restored as normal ones accurately enough. Experiment results on three datasets show that reconstructed traffic samples output by Blind Analyzer are more useful for base analysis models, which improve the corresponding metric values by 11.23% and 6.61% in max for flow fingerprinting and correlation tasks, respectively.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419365","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