Computer Networks最新文献

{"title":"Memory-augmented deep feature extraction and temporal-dependencies prediction for network traffic anomaly detection","authors":"Chao Wang ,&nbsp;Ping Zhou ,&nbsp;Jiuzhen Zeng ,&nbsp;Yong Ma ,&nbsp;Ruichi Zhang","doi":"10.1016/j.comnet.2026.112037","DOIUrl":"10.1016/j.comnet.2026.112037","url":null,"abstract":"<div><div>Traffic anomaly detections are crucial for the network security. Most of existing unsupervised detection models are based on reconstruction and prediction methods that are deficient in generalization ability and processing temporal-dependencies. Although the memory module is introduced to mitigate the weak generalization ability, it encounters challenges such as data distribution drift over time and memory contamination. To address these issues, this paper proposes a novel unsupervised network traffic anomaly detection model, MAFE-TDP, which integrates a transformer-based feature extraction module, a memory module and a prediction-based temporal-dependencies extraction network. The generalization ability of the model and its robustness to memory contamination are enhanced by introducing the memory module with the FIFO memory replacement strategy and KNN method. The proposed anomaly scoring method fuses reconstruction error and prediction error, thus enlarging the gap between normal and abnormal data. Evaluation results on four real-word network traffic datasets demonstrate that MAFE-TDP outperforms existing state-of-the-art baseline methods in terms of AUC-ROC and AUC-PR metrics.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112037"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090325","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":"Real-time network security: Integrating ANN and dynamic graph-based clustering","authors":"Zohar Simhon ,&nbsp;Matan Weiss ,&nbsp;Revital Marbel ,&nbsp;Chen Hajaj ,&nbsp;Amit Dvir ,&nbsp;Ran Dubin","doi":"10.1016/j.comnet.2026.112016","DOIUrl":"10.1016/j.comnet.2026.112016","url":null,"abstract":"<div><div>The increasing sophistication and frequency of cyberattacks have made Network Intrusion Detection Systems (NIDS) a critical component of modern cybersecurity. This work presents D-MAGIC, a novel real-time NIDS that leverages zero-shot learning and graph-based dynamic clustering to detect both known and unknown threats. Unlike traditional systems that rely on labeled datasets and predefined attack signatures, D-MAGIC operates unsupervised, identifying anomalies by detecting deviations from normal network behavior. D-MAGIC employs dynamic clustering to identify coordinated attacks and emerging threats by embedding network flow relationships into a graph structure and clustering similar patterns. Additionally, a second anomaly detection method, called SAGA, is introduced. SAGA enhances detection by using approximate nearest neighbor (ANN) analysis to evaluate isolated nodes, which clustering alone cannot classify. By leveraging information from a node’s neighbors, SAGA improves the identification of subtle or emerging threats. This hybrid approach ensures more comprehensive detection, capturing both clustered anomalies and isolated outliers. Experimental results on the CIC-IDS-2017 and CSE-CIC-IDS-2018 datasets demonstrate that D-MAGIC achieves up to 12% improvement in F1 score over state-of-the-art methods, significantly reduces false positives, and ensures rapid, real-time detection with minimal latency. Furthermore, on the CIC-IDS-2017 dataset, SAGA achieves up to a 6% improvement in accuracy, showcasing its effectiveness in handling diverse attack scenarios.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112016"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398143","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":"TrafficCL: Contrastive learning on network traffic for accurate, efficient and robust IP cross-regional detection","authors":"Yiyang Huang ,&nbsp;Mingxin Cui ,&nbsp;Gaopeng Gou ,&nbsp;Chang Liu ,&nbsp;Yong Wang ,&nbsp;Bing Xia ,&nbsp;Guoming Ren ,&nbsp;Zheyuan Gu ,&nbsp;Xiyuan Zhang ,&nbsp;Gang Xiong","doi":"10.1016/j.comnet.2026.112022","DOIUrl":"10.1016/j.comnet.2026.112022","url":null,"abstract":"<div><div>Dynamic IP technologies such as IP address pool rotation by Internet operators and elastic IP drift by cloud service providers are widely adopted, breaking the static binding between IP addresses and geographical locations and posing severe challenges to the accuracy, efficiency, and robustness of IP cross-regional detection. Traditional solutions rely on third-party IP geolocation databases, whose large-scale batch update mode fails to synchronize IP regional attribution in a timely manner, struggling to adapt to dynamic IP changes. This results in insufficient detection accuracy and efficiency, compromising the stability of geographically related network services. To address this issue, this paper proposes TrafficCL, a traffic feature-based IP cross-regional detection method: it constructs a geographically associated traffic feature set, aligns traffic embedding distance with geographical distance via contrastive learning to enhance geographical attributes, integrates data augmentation to improve model robustness, designs a lightweight binary classification task for regional deviation detection, and adopts a targeted update strategy to avoid large-scale update latency. Experimental results show that TrafficCL significantly outperforms the active probing method PoP: on the Beijing cross-district dataset, the accuracy increases from 0.781 to 0.982, the F1-score improves by 2.2 times, and the processing efficiency for ten-thousand-level samples is enhanced by 23.6 times. When facing 10 % data loss, 10 % network feature fluctuation, and a positional offset of approximately 500 m, the F1-score degradation is less than 3 % in all cases, demonstrating excellent robustness. This method effectively improves the accuracy, efficiency, and robustness of IP cross-regional detection, and has practical significance for ensuring the stability of geographically related network services.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112022"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039281","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":"PBA: Persistent backdoor attack on federated learning via distributed generative triggers","authors":"Wenyin Yang ,&nbsp;Weidong Wu ,&nbsp;Fen Liu ,&nbsp;Li Ma ,&nbsp;Zhiliang Zhang ,&nbsp;Baoyao Yang","doi":"10.1016/j.comnet.2026.112038","DOIUrl":"10.1016/j.comnet.2026.112038","url":null,"abstract":"<div><div>Federated learning (FL) is a distributed machine learning framework in which multiple clients collaboratively train a global model without sharing their raw data. However, its distributed nature and data heterogeneity make it particularly vulnerable to security threats, such as backdoor attacks. In backdoor attacks, adversaries inject hidden triggers into the training process, causing the global model to behave maliciously on specific inputs while maintaining normal performance otherwise. These attacks exploit the inability of the server to audit local client data by poisoning local datasets and model updates. Once aggregated, these updates compromise the global model. Existing backdoor attacks in FL face significant limitations in both attack effectiveness and persistence. To address these limitations, we propose the Persistent Backdoor Attack (PBA), a novel framework that significantly enhances attack success rates and persistence. PBA generates hidden triggers in a distributed manner by creating distinct triggers for different local models and injecting them into partial local datasets to conduct attacks. PBA employs dynamic trigger generation throughout the training process, substantially improving both stealthiness and attack persistence. Comprehensive experiments demonstrate that PBA consistently outperforms SOTA backdoor attacks in federated learning. For instance, on the Tiny-ImageNet dataset, PBA achieves a peak ASR that is over 1% higher than that of SOTA baselines, and stays over 10% higher by the end of training. Overall, PBA provides a more effective, stealthy, and durable solution for executing backdoor attacks in federated learning systems.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112038"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398085","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":"Automating bit-level field localization with hybrid neural network","authors":"Tao Huang ,&nbsp;Yansong Gao ,&nbsp;Yifeng Zheng ,&nbsp;Boyu Kuang ,&nbsp;Zhidan Yuan ,&nbsp;Anmin Fu","doi":"10.1016/j.comnet.2026.112041","DOIUrl":"10.1016/j.comnet.2026.112041","url":null,"abstract":"<div><div>Protocol Reverse Engineering (PRE), which can decipher the format specifications of unknown protocols, lays the groundwork for numerous security analysis applications. Network trace-based PRE has emerged as the dominant technology given its ease of implementation. However, its current identification precision is primarily limited to byte-level granularity. While a few advanced methods can achieve precise identification of fine-grained bit-level fields within given bytes, their target byte localization relies heavily on subjective prior domain knowledge and tedious manual labor, significantly restricting their generalizability and adoption. To address these limitations, we propose BitFiL that is an automated bit-level field localization method. BitFiL features a hybrid neural network architecture delicately designed to capture both intra-byte temporal features and inter-byte contextual structural features from known protocol bytes, enabling automated bit-level field localization and consequent field count identification for unknown protocol bytes. Experimental results demonstrate that BitFiL delivers accurate localization performance for bit-level fields in byte-oriented protocols, with robustness against variations in training-validation protocol combinations and training protocol set sizes. Although limited diversity in bit-level field samples may affect the identification accuracy of field counts, the overall prediction deviations remain relatively small, showcasing high accuracy, convergence, and stability.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112041"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090319","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":"FedHome: A federated learning framework for smart home device classification and attack detection by broadband service providers","authors":"Md Mizanur Rahman ,&nbsp;Faycal Bouhafs ,&nbsp;Sayed Amir Hoseini ,&nbsp;Frank den Hartog","doi":"10.1016/j.comnet.2026.112040","DOIUrl":"10.1016/j.comnet.2026.112040","url":null,"abstract":"<div><div>The rise of the Internet of Things (IoT) has led to the integration of various devices into smart homes, significantly increasing the complexity and vulnerability of home networks. Consequent network performance issues often lead to complaints directed at Broadband Service Providers (BSPs), which may arise from either legitimate usage or malicious cyber attacks. BSPs, however, lack visibility into client-side networks, which is partly due to privacy concerns. This makes it hard to identify the true cause of performance problems. While previous research has tackled these challenges using Machine Learning (ML) techniques, few studies have approached the problem from the perspective of BSPs. They need a solution that is scalable, accurate, and privacy-preserving. Existing centralized ML models fail to generalize across these heterogeneous environments and provide low accuracy. We address this gap by introducing a novel Federated Learning (FL) framework for smart home device classification and attack detection. The proposed approach offers a privacy-preserving, scalable framework that can achieve accuracies of more than 80%. This framework can be installed inside the existing resource-constrained home gateways, making it suitable for large-scale deployment by BSPs.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112040"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090404","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-scale attention fusion for enhanced transformer models in intrusion detection systems","authors":"Samia Saidane ,&nbsp;Francesco Telch ,&nbsp;Kussai Shahin ,&nbsp;Fabrizio Granelli","doi":"10.1016/j.comnet.2025.111985","DOIUrl":"10.1016/j.comnet.2025.111985","url":null,"abstract":"<div><div>Modern network environments generate vast streams of complex and evolving traffic data, presenting significant challenges for accurate and real-time intrusion detection. Conventional deep learning approaches often fail to effectively capture the multi-scale temporal patterns and are frequently hampered by severe class imbalance and catastrophic forgetting when faced with non-stationary data streams. To overcome these limitations, this paper introduces a novel Multi-scale Attention Fusion (MAF) module, a general-purpose architectural enhancement for transformer-based models designed to achieve synergistic optimization across three critical dimensions: computational efficiency, continual learning adaptability, and multi-scale temporal perception. We present two instantiations of this approach: <strong>MEGA+MAF</strong> and <strong>FNet+MAF</strong>. These models synergistically combine short-term local context, long-range dependencies, and global sequence information through an adaptive, learnable gating mechanism. A comprehensive evaluation across four diverse benchmark datasets—<em>ToN-IoT, X-IIoTID, CICEVS2024</em>, and <em>CSE-CIC-IDS2018</em>—demonstrates state-of-the-art performance with balanced optimization across all three dimensions: (1) <strong>FNet+MAF</strong> achieved superior computational efficiency with up to 8.5 ×  <strong>lower memory</strong> and 5.3 ×  <strong>faster inference</strong> while maintaining high accuracy; (2) <strong>MEGA+MAF</strong> demonstrated exceptional continual learning capability, achieving <strong>99.10%</strong> accuracy in dynamic streaming environments while effectively eliminating backward forgetting (<strong>0.00%</strong>) and minimizing forward forgetting (<strong>0.06%</strong>); and (3) Both models exhibited robust multi-scale perception, capturing threats across short-term bursts, mid-range sessions, and global traffic patterns with up to <strong>99.97%</strong> F1-score. Our ablation study after 20 epochs of training identifies 80 tokens as optimal, achieving 85.20% accuracy with 145.1 samples/second throughput. Interpretability analyses further confirm that the models learn robust and semantically meaningful feature representations aligned with network security semantics. The proposed framework represents a significant advancement toward building adaptive, next-generation intrusion detection systems capable of evolving with emerging threats while maintaining operational efficiency in resource-constrained environments.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 111985"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981824","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":"Reliable feedback-driven federated learning for distributed NWDAFs at the edge","authors":"Parsa Rajabzadeh ,&nbsp;Abdelkader Outtagarts ,&nbsp;Yassine Hadjadj-Aoul ,&nbsp;Soraya Ait-Chellouche","doi":"10.1016/j.comnet.2025.111972","DOIUrl":"10.1016/j.comnet.2025.111972","url":null,"abstract":"<div><div>Evolving technologies and rapid proliferation of connected devices are making network management and optimization increasingly complex. Network Data Analytics Function (NWDAF) has been defined as a 3GPP component to provide analytics using Machine Learning (ML) algorithms and statistical analysis methods for Network functions (NFs), Application Functions (AFs), and operations and management (OAM). The NWDAF instances could be placed in the cloud or offloaded to the edge to reduce service latency for NFs service consumer. The resulting distributed NWDAF architecture enables the deployment of various distributed learning approaches across these NWDAF instances. This paper proposes a new Reliable Feedback-Driven Federated Learning (FDFL) method for a reliable training in privacy-preserving manner. The feedback mechanism, which employs a weighted influence-based federated average, ensures convergence towards the optimum. This paper, also, introduces a Model Integrity Evaluation (MIE) mechanism to detect and outcast dishonest NWDAF clients with corrupted data from model aggregation procedure. As a privacy protection mechanism, it is recommended to use Local Differential Privacy (LDP) for masking model parameter exchanges in our framework. Extensive testing has been conducted across multiple scenarios to evaluate convergence rate with increasing scale, resilience against corruption, training time and communication efficiency of our FDFL approach compared to the current centralized NWDAF and other potential federated learning methods. The test results outline remarkable superiority of our approach over existing and other potential state-of-the-art solutions.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 111972"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A platform perspective for the computing continuum: Synergetic orchestration of compute and network resources for hyper-distributed applications","authors":"Nikos Filinis ,&nbsp;Ioannis Dimolitsas ,&nbsp;Dimitrios Spatharakis ,&nbsp;Paolo Bono ,&nbsp;Anastasios Zafeiropoulos ,&nbsp;Cristina Emilia Costa ,&nbsp;Roberto Bruschi ,&nbsp;Symeon Papavassiliou","doi":"10.1016/j.comnet.2026.112029","DOIUrl":"10.1016/j.comnet.2026.112029","url":null,"abstract":"<div><div>The rapid advancements in technologies across the Computing Continuum have reinforced the need for the interplay of various network and compute orchestration mechanisms within distributed infrastructure architectures to support the hyper-distributed application (HDA) deployments. A unified approach to managing heterogeneous components is crucial for reconciling conflicting objectives and creating a synergetic framework. To undertake these challenges, we present NEPHELE, a platform that realizes a hierarchical multi-layered orchestration architecture that incorporates infrastructure and application orchestration workflows across diverse resource management layers. The proposed platform integrates well-defined components spanning network and multi-cluster compute domains to enable intent-driven, dynamic orchestration. At its core, the Synergetic Meta-Orchestrator (SMO) integrates diverse application requirements, generating deployment plans by interfacing with underlying orchestrators over distributed compute and network infrastructure. In the current work, we present the NEPHELE architecture, enumerate its interaction workflows, and evaluate key components of the overall architecture based on the instantiation and usage of the NEPHELE platform. The platform is evaluated in a multi-domain infrastructure setup to assess the operational overhead of the introduced orchestration functionality, considering also the assessment of different topology configurations on resource instantiation times and allocation dynamics, and network latency. Finally, we demonstrate the platform’s effectiveness in orchestrating distributed application graphs under varying placement intents, performance constraints, and workload stress conditions. The evaluation results outline the effectiveness of NEPHELE in orchestrating various infrastructure layers and application lifecycle scenarios through a unified interface.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112029"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039819","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":"Deadline-aware service scheduling via multi-head MARL in device-edge-cloud environments","authors":"Ayeh Mahjoubi,&nbsp;Arunselvan Ramaswamy,&nbsp;Karl-Johan Grinnemo","doi":"10.1016/j.comnet.2026.112019","DOIUrl":"10.1016/j.comnet.2026.112019","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) enables latency-sensitive Internet of Things (IoT) applications by offloading computation to nearby edge servers. However, most existing intelligent scheduling approaches either neglect the full three-tier device-edge-cloud architecture or fail to account for heterogeneous IoT services composed of multiple dependent tasks with diverse deadlines and resource demands. These gaps hinder adaptability under dynamic network conditions. We propose a multi-agent reinforcement learning (MARL) framework in which cellular IoT devices and the edge server act as cooperative agents to optimize task offloading and scheduling. Each agent employs a multi-head deep Q-network (MH-DQN)-with one head per service type-to efficiently manage heterogeneous service workflows. We further implement a multi-head Double DQN (MH-DDQN) variant to improve stability and convergence. In addition, we benchmark our approach against four heuristic baselines and an adaptive simulated annealing (SA) scheduler. Simulation results demonstrate that both MH-DQN and MH-DDQN substantially outperform the heuristic baselines, and that the learned policies match or slightly exceed the SA acceptance ratio while achieving noticeably lower processing times, maintaining high deadline compliance (up to approximately 85% acceptance at heavy load), and reducing latency. MH-DDQN achieves the fastest convergence, greater stability, and slightly lower delays, highlighting its advantage for adaptive scheduling in complex MEC environments.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112019"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039821","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}