Computer Networks最新文献

{"title":"Multi-objective hierarchical edge infrastructure design for service chain workloads: A MOEA/D-driven joint planning and operation approach","authors":"Georgios Kontos ,&nbsp;Polyzois Soumplis ,&nbsp;Prodromos Makris ,&nbsp;Emmanouel Varvarigos","doi":"10.1016/j.comnet.2025.111988","DOIUrl":"10.1016/j.comnet.2025.111988","url":null,"abstract":"<div><div>Edge computing is poised to become a cornerstone of the emerging 6G landscape, where an ever-growing class of ultra-low-latency applications must be served close to the user. Despite its promise, real-world deployments remain nascent, with large-scale implementations anticipated by both Communication and Digital Service Providers (CSPs/DSPs) within the following years. Consequently, strategic edge network design is essential not only to maximize performance, but also to avoid redundant investments that can lead to an increased sum of Capital and Operational Expenditures (CAPEX/OPEX). In this work, we address a tri-fold problem: (i) the selection of deployment locations, (ii) the configuration of devices at the chosen location sites, and (iii) the assignment of the projected workload. Our objective is formulated as a weighted combination of the edge infrastructure’s establishment cost, the expected cumulative workload latency and the total expected energy consumption in the operational phase. To capture the spatial and temporal variability of demand, we solve the assignment subproblem over distinct snapshots, each representing a unique workload projection. We first present a Mixed Integer Linear Programming (MILP) formulation that yields the optimal solution; however, due to its computational intractability, we propose a novel adaptation of the Multi-Objective Evolutionary Algorithm by Decomposition (MOEA/D), with an embedded heuristic algorithm to assist in the chromosome fitness calculation. This method leverages the similarity among neighboring subproblems in a multi-objective framework to efficiently approximate the underlying Pareto frontier. In the experiments, the proposed method is contrasted with a sophisticated single-objective Rollout approach. Our results demonstrate the benefits of adopting a multi-objective algorithm in terms of performance, stability and interpretability across different scalarized subproblems. The proposed framework offers a practical intent-based decision support tool for edge infrastructure providers, weighing CAPEX against operating objectives ahead of initial deployment.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"276 ","pages":"Article 111988"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927414","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":"C2Detector: Interaction-enhanced semantic-aware detection method for C2 channels","authors":"Youqiang Luo,&nbsp;Ruijie Cai,&nbsp;Xiaokang Yin,&nbsp;Jingman Zhou,&nbsp;Fangfang Zhao,&nbsp;Zhenjie Xie,&nbsp;Shengli Liu","doi":"10.1016/j.comnet.2026.111992","DOIUrl":"10.1016/j.comnet.2026.111992","url":null,"abstract":"<div><div>With the continuous evolution of cyberattack techniques, Advanced Persistent Threats (APTs) establish covert Command and Control (C2) channels for long-term infiltration, posing severe security risks. Existing C2 detection methods heavily depend on metadata from the encryption handshake stage, rendering them vulnerable to evasion techniques such as mimicking legitimate TLS fingerprints or randomizing handshake parameters. Additionally, these methods are susceptible to network noise and lack of cross-protocol generalization. To address these challenges, we propose C2Detector, an interaction-enhanced and semantic-aware detection method. By shifting the focus from potentially unreliable handshake metadata to the semantics of the data transmission stage, C2Detector reconstructs network sessions into protocol-independent interaction-state transition sequences. This approach eliminates underlying noise and captures high-level interaction semantics. A spatio-temporal neural network is then employed to learn complex behavioral patterns from these sequences. In complex mixed-traffic environments, C2Detector achieves an F1-score of 0.989. Importantly, in a zero-shot generalization test, the model trained exclusively on TCP traffic successfully identified unseen DNS and ICMP C2 channels, achieving F1-scores of 0.931 and 0.826, respectively. These results suggest the method’s potential advantages in both accuracy and generalization.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"276 ","pages":"Article 111992"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927476","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":"LinkSketch: Hash offloading accelerated network measurement with linked arrays","authors":"Wendi Feng ,&nbsp;Chuanchang Liu ,&nbsp;Wei Zhang","doi":"10.1016/j.comnet.2025.111934","DOIUrl":"10.1016/j.comnet.2025.111934","url":null,"abstract":"<div><div>Network measurement is fundamental to network monitoring systems that ensure operational stability, security, and performance optimization. Sketches have emerged as a <em>de-facto</em> approach for scalable network measurement due to their compactness and efficiency. Conventional sketch implementations adopt <em>fixed-size</em> counters organized in a two-dimensional counter array (2DCA) indexed by <em>per-row</em> hash functions. However, they face two inherent limitations: <strong>i)</strong> memory inefficiency resulting from skewed traffic distributions, and <strong>ii)</strong> considerable performance overhead induced by repetitive hash computations. State-of-the-arts (SOTAs) alleviate memory inefficiency via <em>bit-packing</em> techniques but suffer from degraded performance due to complex value aggregation processes. This paper presents <span>LinkSketch</span>, a high-performance and memory-efficient sketch framework that addresses the accuracy-memory-performance trade-off. <span>LinkSketch</span> introduces two key architectural innovations: <strong>i)</strong> a counter-group-based 2DCA with compact counters supported by a multi-layer overflow linking structure, and <strong>ii)</strong> a hash offloading mechanism that transparently delegates repetitive hash computations to existing CPU instruction extensions without additional hardware support. Theoretical analysis and experimental evaluation demonstrate that LinkSketch achieves comparable accuracy and memory efficiency while improving throughput by more than 3 ×  compared to SOTAs.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"275 ","pages":"Article 111934"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797256","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":"Multi-objective reinforcement learning for adaptive load balancing in cloud data centers","authors":"Xindi He ,&nbsp;Ting Wang","doi":"10.1016/j.comnet.2025.111903","DOIUrl":"10.1016/j.comnet.2025.111903","url":null,"abstract":"<div><div>Efficient load balancing (LB) in cloud data centers is crucial for optimizing resource allocation and enhancing service delivery. However, LB for diverse tasks of different users is typically multi-objective, where the complexity of balancing multiple objectives poses significant challenges as user preferences for these objectives can change dynamically based on varying operational demands. Traditional multi-objective LB solutions often fall short in such dynamic environments due to their inability to adapt effectively to shifting priorities among different objectives. To address these limitations, this paper introduces the Multi-Objective Distributed Load Balancing (MODLB) framework, which incorporates a customized multi-objective version of the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm, MOTD3, and a tailored Preference Alignment (PA) mechanism. This innovative approach allows MODLB to dynamically adjust to changing user preferences, facilitating real-time optimal decision-making. Comprehensive experimental results demonstrate that MODLB significantly outperforms state-of-the-art multi-objective reinforcement learning algorithms and traditional LB solutions across various simulated environments. Moreover, ablation studies confirm the crucial roles of the MOTD3 algorithm and the PA mechanism in enhancing MODLB’s ability to navigate the Pareto frontier with higher precision, thereby effectively balancing the trade-offs between global response times and load distribution fairness.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"275 ","pages":"Article 111903"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747769","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":"Analyzing the impact of shifts in encrypted mobile-app traffic on multimodal few-shot learning","authors":"Davide Di Monda ,&nbsp;Giampaolo Bovenzi ,&nbsp;Antonio Montieri ,&nbsp;Valerio Persico ,&nbsp;Antonio Pescapè","doi":"10.1016/j.comnet.2025.111935","DOIUrl":"10.1016/j.comnet.2025.111935","url":null,"abstract":"<div><div>Network management is essential for ensuring efficient and secure Internet operations, with traffic classification serving as a core element. Currently, traffic classification is facing increasing challenges due to the growing presence of <em>highly dynamic mobile-app traffic</em>, being fueled by the continuous release of new apps, frequent updates, evolving communication patterns, and stricter encrypted protocols. These <em>shifts</em> can significantly alter traffic patterns, making it difficult to keep state-of-the-art machine and deep learning-based traffic classifiers up to date, due to the severe lack of current, high-quality traffic datasets needed to train and adapt such data-driven models. <em>Few-Shot Learning</em> (<span>FSL</span>) offers a promising solution by enabling classification even when only a limited amount of labeled traffic data is available. However, the investigation of <span>FSL</span> in the domain of traffic classification is still in its early stages, with the impact of traffic shifts being largely underexplored. In this paper, we evaluate how the traffic from new apps (those unseen during training) and shifted apps (those affected by traffic changes) impacts classification performance by leveraging <span>Meta Mimetic</span>, a state-of-the-art multimodal <span>FSL</span> approach. <span>Meta Mimetic</span> exploits multiple views of traffic data and integrates an ad-hoc learning procedure to adapt to the evolving mobile-app traffic using minimal supervised data. Our experiments are conducted on two publicly available datasets, encompassing both new apps and shifted apps. First, <em>we assess the presence of traffic changes in shifted apps</em>through Markov-based statistical modeling and evaluate the impact on the performance of <span>Meta Mimetic</span> when considering such traffic. We then show that <span>Meta Mimetic</span> <em>exhibits strong adaptability</em> to shifts introduced by stricter encrypted protocols, having a performance degradation 2 ×  lower than single-modal baselines, as further validated using <em>eXplainable AI (XAI)</em>. Finally, in case of extreme data scarcity, we show that <span>Meta Mimetic</span> can effectively use old traffic for data augmentation, regardless of shifts, achieving up to a <span><math><mrow><mo>+</mo><mn>12</mn><mo>%</mo></mrow></math></span> F1-score improvement over alternative methods.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"276 ","pages":"Article 111935"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886015","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":"HybridGuard: Enhancing minority-class intrusion detection in dew-enabled edge-of-things networks","authors":"Binayak Kar ,&nbsp;Ujjwal Sahu ,&nbsp;Ciza Thomas ,&nbsp;Jyoti Prakash Sahoo","doi":"10.1016/j.comnet.2025.111966","DOIUrl":"10.1016/j.comnet.2025.111966","url":null,"abstract":"<div><div>Securing networks in Dew-Enabled edge-of-things (EoT) networks from sophisticated intrusions is a challenge that is at once critical and challenging. This paper presents HybridGuard, a state-of-the-art framework that combines Machine Learning and Deep Learning to raise the bar for intrusion detection. HybridGuard addresses data imbalance by performing mutual information-based feature selection to ensure that the most important features are always considered to improve detection performance, especially for minority attacks. The proposed framework leverages Wasserstein Conditional Generative Adversarial Networks (WCGAN-GP) to alleviate class imbalance, hence enhancing the precision of detection. In the framework, a two-phase architecture named “DualNetShield” was integrated to introduce advanced network traffic analysis and anomaly detection techniques, enhancing the granular identification of threats within complex EoT environments. HybridGuard, tested on UNSW-NB15, CIC-IDS-2017, and IOTID20 datasets, demonstrates robust performance over a wide variety of attack scenarios, outperforming the existing solutions in adaptation to evolving cybersecurity threats. This innovative approach establishes HybridGuard as a powerful tool for safeguarding EoT networks against modern intrusions.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"276 ","pages":"Article 111966"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886012","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":"Towards a robust transport network with self-adaptive network digital twin","authors":"Cláudio Modesto ,&nbsp;João Borges ,&nbsp;Cleverson Nahum ,&nbsp;Lucas Matni ,&nbsp;Cristiano Bonato Both ,&nbsp;Kleber Cardoso ,&nbsp;Glauco Gonçalves ,&nbsp;Ilan Correa ,&nbsp;Silvia Lins ,&nbsp;Andrey Silva ,&nbsp;Aldebaro Klautau","doi":"10.1016/j.comnet.2025.111967","DOIUrl":"10.1016/j.comnet.2025.111967","url":null,"abstract":"<div><div>The ability of the Network digital twin (NDT) to remain aware of changes in its physical counterpart, known as the physical twin (PTwin), is a fundamental condition to enable timely synchronization, also referred to as <em>twinning</em>. In this way, considering a transport network, a key requirement is to handle unexpected traffic variability and dynamically adapt to maintain optimal performance in the associated virtual model, known as the virtual twin (VTwin). In this context, we propose a self-adaptive implementation of a novel NDT architecture designed to provide accurate delay predictions, even under fluctuating traffic conditions. This architecture addresses an essential challenge, underexplored in the literature: improving the resilience of data-driven NDT platforms against traffic variability and improving synchronization between the VTwin and its physical counterpart. Therefore, the contributions of this article rely on NDT lifecycle by focusing on the operational phase, where telemetry modules are used to monitor incoming traffic, and concept drift detection techniques guide retraining decisions aimed at updating and redeploying the VTwin when necessary. We validate our architecture with a network management use case, across various emulated network topologies, and diverse traffic patterns to demonstrate its effectiveness in preserving acceptable performance and predicting quality of service (QoS) metrics under unexpected traffic variation, such as delay and jitter. The results in all tested topologies, using the normalized mean square error as the evaluation metric, demonstrate that our proposed architecture, after a traffic concept drift, achieves a performance improvement in per-flow delay and jitter prediction of at least 64% and 21%, respectively, compared to a configuration without NDT synchronization.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"276 ","pages":"Article 111967"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886467","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":"HAT: Leveraging hierarchical attention and temporal modeling for API-based malware detection","authors":"Zhengyu Zhu ,&nbsp;Shan Liao ,&nbsp;Lei Zhang ,&nbsp;Liang Liu","doi":"10.1016/j.comnet.2025.111971","DOIUrl":"10.1016/j.comnet.2025.111971","url":null,"abstract":"<div><div>While runtime parameters have been incorporated to enhance API-based malware detection, existing approaches still fall short in fully capturing the structural and temporal characteristics of API call sequences, thereby limiting their generalization capability. In this paper, we propose <strong>HAT</strong>, a novel detection method that jointly models API sequences from both structural and temporal perspectives. HAT leverages a hierarchical attention mechanism to learn the varying importance of API names and their parameters, and integrates two complementary temporal modules to uncover execution patterns of malware that are underexplored in prior work. Extensive experiments on multiple datasets demonstrate that HAT consistently outperforms existing methods. Compared to approaches relying only on API names, HAT improves the F1-score by 5.50% to 30.87%. Compared to parameter-augmented approaches, it achieves superior detection and generalization, with F1-score improvements of 4.10% to 7.07%, benefiting from its unified modeling of structural and temporal aspects.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"276 ","pages":"Article 111971"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886495","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":"Joint spectrum allocation and power control for D2D communication and sensing in 6G networks using DRL-based hyper-heuristics","authors":"Gabriel Pimenta de Freitas Cardoso,&nbsp;Paulo Henrique Portela De Carvalho,&nbsp;Paulo Roberto de Lira Gondim","doi":"10.1016/j.comnet.2025.111969","DOIUrl":"10.1016/j.comnet.2025.111969","url":null,"abstract":"<div><div>The ongoing evolution of mobile communication systems, particularly toward the sixth generation (6G), has opened new frontiers in the integration of communication and sensing technologies. In this context, Industry 4.0 demands efficient and intelligent solutions for supporting a growing number of interconnected devices while ensuring low latency and high spectral efficiency.</div><div>This study addresses the complex problem of joint resource allocation in systems that integrate primary communications, device-to-device (D2D) communication, and sensing, with a special focus on power control and spectrum sharing.It proposes a novel hyper-heuristic (HH) strategy powered by Deep Reinforcement Learning (DRL) that dynamically allocates resources and optimizes spectral usage in a 6G-enabled environment. Unlike traditional heuristic-based approaches that rely on fixed rules, DRL-based HH learns from interactions with the environment and selects appropriate low-level heuristics (LLHs) for managing interference, meeting performance constraints, and improving D2D and sensor operations. A realistic simulation scenario inspired by industrial environments was modeled for evaluations of the strategy’s effectiveness.</div><div>The results show the method can effectively balance the competing demands of different system components, dynamically adapt to environmental changes, and maintain compliance with detection and transmission constraints. By extending existing models to including D2D communication, channel uncertainties, and spectrum reallocation over time, the study contributes with a scalable and intelligent solution for future wireless systems in complex industrial settings.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"276 ","pages":"Article 111969"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886497","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":"Dynamic shielding: Defense mechanism against gradient attacks on distributed GANs","authors":"Chao Wang,&nbsp;Yingze Liu,&nbsp;Xiuyuan Liu,&nbsp;Yunhua He,&nbsp;Ke Xiao","doi":"10.1016/j.comnet.2025.111888","DOIUrl":"10.1016/j.comnet.2025.111888","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) have gained significant attentions due to their powerful generative abilities. However, the requirements for data privacy protection and data decentralization have led to the emergence of Distributed GANs (DGANs), where multiple discriminators collaboratively train the central generator model. However, the mode of distributed training poses the risk of fake gradient attacks where the discriminator forges the gradient to destroy or induce the training process of the generator. We previously studied the impact of gradient attacks on generative models in our previous work. In this paper, we propose a generative dual-gradient defense scheme to address the aforementioned issue, which combines spectral graph analysis and Conditional Variational Autoencoders (CVAE). From a spectral analysis perspective, we elucidate the strong correlation and temporal consistency between malicious behaviors of discriminators and convert the problem of detecting malicious gradient updates into a Spatio-Temporal graph segmentation problem. Additionally, integrating normalized spectral clustering theory, we introduce a model anomaly detection method (<em>Q</em>-NCUT) and provide the corresponding theoretical proof. To further purify the set of benign discriminators, we propose a generative model defense strategy based on dynamic thresholding of reconstruction errors, conditioned on iteration time, leveraging the reconstruction error characteristics of CVAE. The qualitative experiments and quantitative experiments demonstrate that compared to existing defense methods, our defense mechanism better preserves the distribution characteristics of generated data. Especially, in the context of DGANs utilizing the DCGAN, our defense achieves a relative reduction in FID values ranging from 5.75 % to 14.06 % compared to the Dnc baseline, and the defense effectiveness is 1.18 to 1.43 times better than that of existing methods on the MNIST datasets, respectively.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"275 ","pages":"Article 111888"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694144","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}