Journal of Network and Computer Applications最新文献

{"title":"TPMCD: A method to optimizing cost and throughput for clustering tasks and hybrid containers in the cloud data center","authors":"Arash GhorbanniaDelavar","doi":"10.1016/j.jnca.2025.104132","DOIUrl":"10.1016/j.jnca.2025.104132","url":null,"abstract":"<div><div>The regulatory of task classification or clustering and hybrid containers in cloud data centers has a lower overhead of cost compared to virtual machines, also it has a direct impact on the load balance, accessibility of virtual machines, and increase of efficiency. Therefore, additional resources with high computing power usage are one of the important issues. In the proposed method merging the index parameters of response time, execution accuracy and their sensitivity rate have been used. In TPMCD(ThroughPut and cost optimizing Method for Clustering tasks and hybrid containers in the cloud Data center), customers agreement, as a service and performance of the connection, the efficiency of service quality and reliability of algorithms, requests, and confirmations (short, medium, long) due to the configuration of resources and containers and the intelligent detector threshold, protection of the increase in system efficiency and energy consumption decrease synchronously against dynamic workloads and changes in user requests. Classification and re-clustering of tasks in the algorithm have led to an improvement in the real execution time compared to the execution time of the studied algorithms. In the proposed method, by correctly allocating resources for scoring unbalanced data for allocating resources and applications and communicating between containers. In TPMCD, parameters of weight, size, and scoring are used in assigning tasks to processing resources. Confidence interval has been done in proposed method due to the possibility of a small difference in scheduling between different virtual machines. In the TPMCD algorithm, choosing the right VM and reducing the critical points, in the hosts where the load imbalance is created, the load balance is optimized by considering the sensitivity rate and scoring the average tasks. TPMCD method have optimized time and cost by decreasing redundancy. From the obtained results in the evaluation, this method performed better than other ones 7% in cost, 4% in throughput, and 9.5% in real execution time on average simultaneously. Finally, the proposed approach was 3% better than the KC method in the number of nodes used.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"237 ","pages":"Article 104132"},"PeriodicalIF":7.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378841","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":"Lurking in the shadows: Unsupervised decoding of beaconing communication for enhanced cyber threat hunting","authors":"Arash Mahboubi ,&nbsp;Khanh Luong ,&nbsp;Geoff Jarrad ,&nbsp;Seyit Camtepe ,&nbsp;Michael Bewong ,&nbsp;Mohammed Bahutair ,&nbsp;Ganna Pogrebna","doi":"10.1016/j.jnca.2025.104127","DOIUrl":"10.1016/j.jnca.2025.104127","url":null,"abstract":"<div><div>The escalating prevalence of Advanced Persistent Threats (APTs) necessitates the development of more robust solutions capable of effectively thwarting these attacks by monitoring system activities across individual hosts. Existing cloud-native security applications utilize a combination of rule-based and machine learning-based detection techniques to protect digital assets. However, these approaches have limitations. Rule-based detection depends on predefined rules to identify specific attack patterns. Persistent attackers can often evade detection by carefully ensuring that their behavior circumvents these rules. In contrast, machine learning-based detection techniques, which learn attack patterns from data, rely heavily on the availability of labeled data for training. However, labeled data is often unavailable and can be labor-intensive and costly to obtain. In this paper, we address the challenge of detecting APT attacks more holistically by leveraging attackers’ behavior during communication with Command and Control (C2) servers, a critical phase observed in most APT attacks. We aim to reduce false positive alerts for threat hunters by analyzing system network logs to detect potential network beaconing, a common attribute of various malware. We introduce a novel hybrid approach, called <em><strong>NetSpectra Sentinel</strong></em>, which employs a Continuous Time Hidden Markov Model (CT-HMM) to detect hidden states underlying observed patterns within the network logs and Time Series Decomposition (TSD) to model temporal patterns. We evaluate the effectiveness of our approach using 14 benchmark datasets and one synthetic dataset, comparing our method with other state-of-the-art statistical-based and botnet detection techniques. The results demonstrate that our technique achieves significantly higher accuracy in most cases, and even when existing techniques fail, our approach can still detect beaconing post-initial compromise with up to 90% accuracy. Additionally, we achieve up to four times better performance in terms of precision compared to existing statistical-based techniques.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104127"},"PeriodicalIF":7.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377185","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}

{"title":"Security and privacy of industrial big data: Motivation, opportunities, and challenges","authors":"Naveed Anjum ,&nbsp;Zohaib Latif ,&nbsp;Hongsong Chen","doi":"10.1016/j.jnca.2025.104130","DOIUrl":"10.1016/j.jnca.2025.104130","url":null,"abstract":"<div><div>With the rapid growth of the Industrial Internet of Things (IIoT), an abundance of data is generated, and various data acquisition, analytics, and storage mechanisms are developed intelligently for smart industrial productions. Big heterogeneous data of IIoT suffers from security and privacy issues, which are the main hurdles for smooth industrial operations and pose a serious concern to the widespread adoption of IIoT. The existing studies suffer from security loopholes and privacy-preserved solutions for industrial data in a distributed environment. However, emerging technologies like Blockchain, Federated Learning (FL), and Sixth Generation (6G) are potential candidates to provide reliability, security, and privacy in IIoT networks. The blockchain offers the temper proof of security due to its distributive absolute nature. The FL does not share data with the centralized system for training purposes, which ensures data privacy. Finally, 6G communication is used for faster data acquisition and low latency in the mobility-based distributed nature of industrial big data.</div><div>In this survey, we present an in-depth analysis of these emerging technologies in IIoT, their motivations, various IIoT applications, current challenges, and future directions regarding industrial big data security and privacy. In addition, an exhaustive investigation of privacy and security threats in industrial big data (acquisition, analytics, and storage) is considered. To this end, various industrial applications, software tools for big data, blockchain, FL, and 6G, as well as a proof of concept for anomaly detection on time-series data, are provided in detail. Lastly, this study aims to provide research challenges and future directions in industrial applications to achieve big data security and privacy.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"237 ","pages":"Article 104130"},"PeriodicalIF":7.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378840","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":"nNFST: A single-model approach for multiclass novelty detection in network intrusion detection systems","authors":"Xuan-Ha Nguyen,&nbsp;Kim-Hung Le","doi":"10.1016/j.jnca.2025.104128","DOIUrl":"10.1016/j.jnca.2025.104128","url":null,"abstract":"<div><div>The rapid evolution of cyberattack techniques necessitates advanced intrusion detection systems (IDS) capable of multiclass novelty detection (MND), accurately classifying known attacks while identifying novel ones. Despite numerous successful studies focused on multi-class attack classification or novel attack detection separately, a significant research gap remains in achieving the effective MND for IDS. In this paper, we introduce the neighbour null Foley–Sammon transformation (nNFST), a novel single-model algorithm designed to address the MND challenge in IDS. nNFST employs a novel technique based on the inverse nearest neighbour algorithm to compute within-class and between-class variation. This technique preserves both the local distribution structure within each class and the global distribution structure across classes, thereby mitigating the impact of singular points on the algorithm and enhancing accuracy on complex data. Furthermore, nNFST leverages the kernel trick to improve detection accuracy and sparse matrix multiplication to reduce training costs. Comprehensive evaluation results on four public datasets demonstrate nNFST’s superior performance compared to related works in different tasks, achieving 97.12% to 99.56% accuracy in multiclass classification tasks, 94.53% to 99.33% accuracy in novel attack detection tasks, and a 0.825 to 0.975 Matthews correlation coefficient in MND tasks. These results highlight nNFST’s potential to significantly enhance IDS capabilities by concurrently classifying known attacks and identifying unknown attacks.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104128"},"PeriodicalIF":7.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377184","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":"Advanced aerial monitoring and vehicle classification for intelligent transportation systems with YOLOv8 variants","authors":"Murat Bakirci","doi":"10.1016/j.jnca.2025.104134","DOIUrl":"10.1016/j.jnca.2025.104134","url":null,"abstract":"<div><div>Aerial monitoring assumes a pivotal role within the domain of Intelligent Transportation Systems (ITS), imparting invaluable data and discernments that ameliorate the efficacy, security, and holistic operability of transportation networks. Image processing, encompassing the derivation of valuable insights through the manipulation of visual data captured by imaging apparatus, resides at the core and is poised to establish a firm footing in forthcoming ITS applications. In this context, numerous machine learning methodologies have been devised to enhance image processing, with novel approaches continually emerging. YOLOv8 emerged earlier this year and is still in the process of assimilating its potential application within the domain of ITS. In this study, a comprehensive assessment was conducted on all constituent variants of YOLOv8, specifically within the context of its application in the domain of aerial traffic monitoring. Using a custom-modified commercial drone, extensive datasets were acquired encompassing a diverse range of flight scenarios and traffic dynamics. To optimize model performance, meticulous consideration was given to ensuring dataset inclusivity, encompassing the full spectrum of vehicular typologies, while maintaining a homogeneous structure that accommodates an array of environmental nuances, including illumination and shading variations. The outcomes evince that both YOLOv8l and YOLOv8x exhibit notable superiority over other variants, manifesting exceptional detection efficacy even amid high-density traffic scenarios and the presence of obstructive elements. Contrastingly, in comparison to earlier iterations of YOLO, the current models demonstrate heightened precision in vehicle classification, yielding a reduction in misclassification instances. Although YOLOv8n exhibits a relatively subdued performance relative to other models, its potential is discernible in real-time applications, particularly within the purview of ITS, owing to its commendable proficiency in detection rates.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"237 ","pages":"Article 104134"},"PeriodicalIF":7.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395510","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":"Enhanced heartbeat protocol for near real-time coordinated spectrum sharing with expanded use cases","authors":"Joseph Tolley ,&nbsp;Cameron Makin ,&nbsp;Kenneth King ,&nbsp;Carl B. Dietrich","doi":"10.1016/j.jnca.2025.104126","DOIUrl":"10.1016/j.jnca.2025.104126","url":null,"abstract":"<div><div>The goal of Spectrum Access Systems (SASs) and similar systems that coordinate access to shared radio frequency bands is to fairly and efficiently allocate spectrum use amongst users in a locality such as a county. In the US 3.5-GHz Citizens Broadband Radio Service (CBRS) band, a SAS communicates with secondary users (SUs) of the band through protocols as defined by the US Federal Communications Commission (FCC) and Wireless Innovation Forum (WInnForum) to allocate spectrum and update each endpoint on the status of an SU’s granted channel within the band. These protocols include heartbeats that regularly inform an SU of their permitted status and alert them to Primary User (PU) activity requiring an SU to vacate their occupied channel. The heartbeat protocol maintains continuity of communication between the SAS and the SUs to exchange minimal types of information. However, the reliance on this limited message format, combined with a synchronous protocol that enforces response times on the scale of minutes, significantly restricts the system’s capability. This limitation excludes many use cases, such as those requiring near real-time adaptation, more extensive management of transmission parameters, or the delivery of information like backup SU channel assignments.</div><div>We propose an alternative heartbeat protocol named the Enhanced Heartbeat Protocol (EHP) that uses asynchronous messaging and a strategically extended message format to enable a much wider range of use cases, including support for fast-moving swarms of unmanned aerial vehicles (UAVs) or other high-mobility applications. Simulations demonstrate that this asynchronous protocol is scalable, providing rapid and reliable notifications to a large number of SUs. Additionally, the protocol’s performance degrades gracefully as the number of SUs increases, making it more robust under various conditions compared to the current standard. The goal of our EHP is to provide SASs with more up-to-date spectrum information, enable scalable centralized systems, and introduce additional parameters to increase the utility and flexibility of SASs in diverse wireless communication scenarios.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"236 ","pages":"Article 104126"},"PeriodicalIF":7.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377186","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}

{"title":"QuIDS: A Quantum Support Vector machine-based Intrusion Detection System for IoT networks","authors":"Rakesh Kumar,&nbsp;Mayank Swarnkar","doi":"10.1016/j.jnca.2024.104072","DOIUrl":"10.1016/j.jnca.2024.104072","url":null,"abstract":"<div><div>With the increasing popularity of IoT, there has been a noticeable surge in security breaches associated with vulnerable IoT devices. To identify and counter such attacks. Intrusion Detection Systems (IDS) are deployed. However, these IoT devices use device-specific application layer protocols like MQTT and CoAP, which pose an additional burden to the traditional IDS. Several Machine Learning (ML) and Deep Learning (DL) based IDS are developed to detect malicious IoT network traffic. However, in recent times, a variety of IoT devices have been available on the market, resulting in the frequent installation and uninstallation of IoT devices based on users’ needs. Moreover, ML and DL-based IDS must train with sufficient device-specific attack training data for each IoT device, consuming a noticeable amount of training time. To solve these problems, we propose QuIDS, which utilizes a Quantum Support Vector Classifier to classify attacks in an IoT network. QuIDS requires very little training data compared to ML or DL to train and accurately identify attacks in the IoT network. QuIDS extracts eight flow-level features from IoT network traffic and utilizes them over four quantum bits for training. We experimented with QuIDS on two publicly available datasets and found the average recall rate, precision, and f1-score of the QuIDS as 91.1%, 84.3%, and 86.4%, respectively. Moreover, comparing QuIDS with the ML and DL methods, we found that QuIDS outperformed by 37.7%, 24.4.6%, and 36.9% more average recall and precision rates than the ML and DL methods, respectively.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104072"},"PeriodicalIF":7.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790082","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":"Complex networks for Smart environments management","authors":"Annamaria Ficara,&nbsp;Hocine Cherifi,&nbsp;Xiaoyang Liu,&nbsp;Luiz Fernando Bittencourt,&nbsp;Maria Fazio","doi":"10.1016/j.jnca.2024.104088","DOIUrl":"10.1016/j.jnca.2024.104088","url":null,"abstract":"","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104088"},"PeriodicalIF":7.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825324","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":"Heterogeneous graph representation learning via mutual information estimation for fraud detection","authors":"Zheng Zhang ,&nbsp;Xiangyu Su ,&nbsp;Ji Wu ,&nbsp;Claudio J. Tessone ,&nbsp;Hao Liao","doi":"10.1016/j.jnca.2024.104046","DOIUrl":"10.1016/j.jnca.2024.104046","url":null,"abstract":"<div><div>In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labeled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and semantic information and embed it into the low-dimensional node representation. Recently, maximizing the mutual information between the local node embedding and the summary representation has achieved the promising results on node classification tasks. However, existing deep graph infomax methods still have the following limitations. Firstly, attribute information of nodes in the graph is not fully utilized for capturing the semantic relationships between nodes. Secondly, the local and global supervision signal are not simultaneously exploited for the node embedding learning. Thirdly, the multiplex heterogeneous relations among nodes are ignored. To address these issues, a heterogeneous graph representation learning model by mutual information estimation (MIE-HetGRL) is proposed in this paper to identify the fraudsters in the fraud review graph. Concretely, a high-order mutual information estimation is proposed to integrate the local and global mutual information as the supervision signal. Then we devise a semantic attention fusion module to aggregate the relation-aware node embeddings into a compact node representation. Finally, a joint contrastive learning is designed for facilitating the training and optimization of model. The experimental results show that our proposed model significantly outperforms state-of-the-art baselines for fraud detection.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"234 ","pages":"Article 104046"},"PeriodicalIF":7.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143864","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 holistic survey of UAV-assisted wireless communications in the transition from 5G to 6G: State-of-the-art intertwined innovations, challenges, and opportunities","authors":"Mobasshir Mahbub ,&nbsp;Mir Md. Saym ,&nbsp;Sarwar Jahan ,&nbsp;Anup Kumar Paul ,&nbsp;Alireza Vahid ,&nbsp;Seyyedali Hosseinalipour ,&nbsp;Bobby Barua ,&nbsp;Hen-Geul Yeh ,&nbsp;Raed M. Shubair ,&nbsp;Tarik Taleb","doi":"10.1016/j.jnca.2025.104131","DOIUrl":"10.1016/j.jnca.2025.104131","url":null,"abstract":"<div><div>Due to the rapid progress in communication technologies, unmanned aerial vehicles (UAVs) have become increasingly capable of providing reliable and cost-effective wireless communication from aerial vantage points. Unlike conventional stationary infrastructure, UAVs exhibit attractive features such as high scalability and improved line-of-sight (LoS) connectivity. Consequently, UAV-assisted wireless communications have become a promising paradigm to enhance coverage and connectivity in terrestrial and non-terrestrial networks. Nevertheless, the efficient deployment of UAVs in continuously evolving wireless network scenarios has remained to be a challenging task. These challenges have attracted a large body of research literature and subsequently several survey papers on UAV-assisted wireless communications. One of the distinctive features of research in UAV-assisted wireless networks is its broad array of experimental and analytical tools and techniques. A thorough review of these methodologies can swiftly familiarize researchers with the most recent efforts within this expansive field. However, most of the existing review/survey papers in this domain lack a comprehensive discussion about the advanced technologies used in UAV-assisted wireless networks, such as rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), digital twin (DT), cognitive radio (CR), space-air-ground integrated network (SAGIN), cell-free massive multiple-input multiple-output (CF mMIMO), integrated sensing and communication (ISAC), quantum technology, holographic MIMO (HMIMO). Motivated by this limitation and considering the novel UAV-assisted communication scenarios that can benefit from the adoption of such technologies; this work provides a thorough analysis of state-of-the-art intertwined technologies relative to UAV-assisted communications along with a discussion of their effectiveness and limitations. Furthermore, this study provides a brief overview of the comprehensive challenges of UAV-assisted networks, along with their security challenges, and opens future direction in this domain. This work finally explores the unique challenges in each of the existing technologies developed for UAV-assisted wireless networks that have been limitedly explored in prior literature aimed at providing a set of directions for future works.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"237 ","pages":"Article 104131"},"PeriodicalIF":7.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402546","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}