Journal of Network and Computer Applications最新文献

{"title":"HERALD: Hybrid Ensemble Approach for Robust Anomaly Detection in encrypted DNS traffic","authors":"Umar Sa’ad ,&nbsp;Demeke Shumeye Lakew ,&nbsp;Nhu-Ngoc Dao ,&nbsp;Sungrae Cho","doi":"10.1016/j.jnca.2025.104342","DOIUrl":"10.1016/j.jnca.2025.104342","url":null,"abstract":"<div><div>The proliferation of encrypted Domain Name System (DNS) traffic through protocols like DNS over Hypertext Transfer Protocol Secure presents significant privacy advantages but creates new challenges for anomaly detection. Traditional security mechanisms that rely on payload inspection become ineffective, necessitating advanced strategies capable of detecting threats in encrypted traffic. This study introduces the Hybrid Ensemble Approach for Robust Anomaly Detection (HERALD), a novel framework designed to detect anomalies in encrypted DNS traffic. HERALD combines unsupervised base detectors, including Isolation Forest (IF), One-Class Support Vector Machine (OCSVM), and Local Outlier Factor (LOF), with a supervised Random Forest meta-model, leveraging the strengths of both paradigms. Our comprehensive evaluation demonstrates HERALD’s exceptional performance, achieving 99.99 percent accuracy, precision, recall, and F1-score on the CIRA-CIC-DoHBrw-2020 dataset, while maintaining competitive computational efficiency with 110s training time and 2.2ms inference time. HERALD also demonstrates superior generalization capabilities on cross-dataset evaluations, exhibiting minimal performance degradation of only 2-4 percent when tested on previously unseen attack patterns, outperforming purely supervised models, which showed 5-8 percent degradation. The interpretability analysis, incorporating feature importance, accumulated local effects, and local interpretable model-agnostic explanations, provides insights into the relative contributions of each base detector, with OCSVM emerging as the most influential component, followed by IF and LOF. This study advances the field of network security by offering a robust, interpretable, and adaptable solution for detecting anomalies in encrypted DNS traffic that balances a high detection rate with a low false-positive rate.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104342"},"PeriodicalIF":8.0,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223445","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 robust fault-tolerant framework for VM failure predication and efficient task scheduling in dynamic cloud environments","authors":"S. Sheeja Rani ,&nbsp;Oruba Alfawaz ,&nbsp;Ahmed M. Khedr","doi":"10.1016/j.jnca.2025.104340","DOIUrl":"10.1016/j.jnca.2025.104340","url":null,"abstract":"<div><div>Due to the dynamic nature of cloud computing, maintaining fault-tolerance is essential to ensure the reliability and performance of virtualized environments. Failures in Virtual Machines (VMs) disrupt the seamless operation of cloud-based services, making it vital to implement a strong failure prediction system. As a solution, this work proposes a Segmented Regressive Learning-based Multivariate Raindrop Optimized Lottery Scheduling (SRL-MROLS) for dynamic cloud environments. Initially, the VM failure prediction is carried out using a Segmented Regressive Q-learning algorithm, where a set of VMs is provided as input. Segmented regression analyzes the average failure rate of VMs, while a reward-based framework guides the decision-making process for accurate failure prediction. Once a failure is predicted, a relocation process is triggered, involving the migration of workloads or tasks from the failing VM to an alternate VM. Next, a Multivariate Elitism Raindrop Optimization approach is employed to identify the optimal VM for task migration. Finally, a Deadline-Aware Stochastic Prioritized Lottery Scheduling is employed for efficient allocation of tasks to the selected VMs, maintaining seamless operations even in the event of VM failures. This process significantly improves task scheduling by maximizing throughput and minimizing response time in cloud environments. Experimental results demonstrate the superior performance of SRL-MROLS across different metrics. Specifically, it achieves an average improvement of 6.4% in failure prediction accuracy, 27.4% in throughput, and a 13% reduction in response time. Additionally, it reduces failure prediction time by 15%, migration cost by 14.3%, and makespan by 15%, significantly outperforming conventional techniques.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104340"},"PeriodicalIF":8.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181227","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":"Securing edge based smart city networks with software defined Networking and zero trust architecture","authors":"Abeer Iftikhar ,&nbsp;Faisal Bashir Hussain ,&nbsp;Kashif Naseer Qureshi ,&nbsp;Muhammad Shiraz ,&nbsp;Mehdi Sookhak","doi":"10.1016/j.jnca.2025.104341","DOIUrl":"10.1016/j.jnca.2025.104341","url":null,"abstract":"<div><div>Smart cities are rapidly evolving by adopting Internet of Things (IoT) devices, edge and cloud computing, and mobile connectivity. While these advancements enhance urban efficiency and connectivity, they also significantly increase the risk of cyber threats targeting critical infrastructure. Modern interdependent systems require flexible resilience, allowing them to adapt to changing conditions while maintaining core functions. Smart city networks, however, face unique security vulnerabilities due to their scale and heterogeneity. Altered to industry expectations and requirements, traditional security models are generally restrictive. With its \"never trust, always verify' motto, the Zero Trust (ZT) security model starkly differs from traditional models. ZT builds on network design by mandating real time identity verification, giving minimum access permission and mandating respect for the principle of least privilege. Software Defined Networking (SDN) extends one step further by offering central control over the network, policy based autonomous application and immediate response to anomalies. To address these challenges, our proposed Trust-based Resilient Edge Networks (TREN) framework integrates ZT principles to enhance smart city security. Under the umbrella of SDN controllers, SPP, the underpinning component of TREN, performs real time trust analysis and autonomous policy enforcement, for instance, applying high level threat defense mechanisms. TREN dynamically defends against advanced threats like DDoS and Sybil attacks by isolating malicious nodes and adapting defense tactics based on real-time trust and traffic analysis. Trust analysis and policy control modules provide dynamic adaptive coverage, permitting effective proactive defense. Mininet-based simulations demonstrate TREN's efficacy, achieving 95 % detection accuracy, a 20 % latency reduction, and a 25 % increase in data throughput when compared to baseline models.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104341"},"PeriodicalIF":8.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254748","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 profit-effective function service pricing approach for serverless edge computing function offloading","authors":"Siyuan Liu ,&nbsp;Li Pan ,&nbsp;Shijun Liu","doi":"10.1016/j.jnca.2025.104338","DOIUrl":"10.1016/j.jnca.2025.104338","url":null,"abstract":"<div><div>In recent years, edge computing services have continued to develop and have been better integrated with serverless computing, leading to the improvement of the performance and concurrent request handling capabilities of edge servers. Therefore, an increasing number of IoT devices are willing to pay a certain amount of service processing fees to offload some computing tasks to edge servers for execution, with the aim of meeting their latency requirements. However, the computing capacity and storage space of edge servers at a single base station are still limited. Therefore, base stations must decide which task images to cache for future execution and price these computing services to control the computing offloading of IoT devices, so as to maximize their expected profit under the constraints of limited computing capacity and memory space. In this paper, we stand from the perspective of base stations and formulate the caching and pricing of function images at a base station, as well as the function offloading process of IoT devices, as a Markov Decision Process (MDP). We adopt a Proximal Policy Optimization (PPO)-based function service pricing adjustment algorithm to optimize the profit of base stations. Finally, we evaluate our approach through simulation experiments and compare it with baseline methods. The results show that our approach can significantly improve base stations’ expected profit in various scenarios.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104338"},"PeriodicalIF":8.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160067","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":"Elastic RAN slicing technology with multi-timescale SLA assurances for heterogeneous services provision in 6G","authors":"Yamin Shen ,&nbsp;Ping Wang ,&nbsp;Chiou-Jye Huang ,&nbsp;Shenxu Kuang ,&nbsp;Song Li ,&nbsp;Zihan Li","doi":"10.1016/j.jnca.2025.104330","DOIUrl":"10.1016/j.jnca.2025.104330","url":null,"abstract":"<div><div>Digital transformation brings diverse applications along with varying Quality of Service (QoS) and isolation requirements. Network slicing, a key 5G technology anticipated to persist in 6G, aims to meet these heterogeneous requirements. However, due to conflicting usage of scarce resources among services, especially with multi-timescale Service Level Agreement (SLA) requirements including QoS and isolation, implementing slicing in the Radio Access Network (RAN) domain is a significant challenge. Therefore, this paper formulates the radio resource allocation problem posed by the coexistence of multiple URLLC (Ultra-Reliable and Low-Latency Communications) with varying delay requirements and eMBB (Enhanced Mobile Broadband) as a multi-timescale optimization problem. Consequently, a novel MPC (Model Predictive Control)-based RAN slicing resource allocation model called MPC-RSS is proposed. Specifically, MPC-RSS ensures elastic QoS through delay-tracking mechanism and far-sighted schemes. Meanwhile, it maintains elastic isolation by introducing logical and physical isolation constraint terms. Compared with the existing state-of-the-art approaches, simulation results show that MPC-RSS can achieve better and more elastic SLA performance. Our proposal provides a choice for 6G RAN to empower vertical industries achieving digital upgrades.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104330"},"PeriodicalIF":8.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223446","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":"Lightweight verifiable privacy preserving federated learning","authors":"Li Zhang ,&nbsp;Bing Tang ,&nbsp;Jianbo Xu","doi":"10.1016/j.jnca.2025.104335","DOIUrl":"10.1016/j.jnca.2025.104335","url":null,"abstract":"<div><div>Federated learning (FL) has garnered considerable attention owing to its capability of accomplishing model training through the sharing local models without accessing training datasets. Nevertheless, it has been demonstrated that the shared models still possess sensitive information related to the training data. Moreover, there is a possibility that malicious aggregation servers can return manipulated global models. While the verification problem in FL has been explored in existing schemes, most of these schemes employ bilinear pairing operations and homomorphic hash computations dependent on the model’s dimension, leading to substantial computational costs. Additionally, some schemes necessitate multiple parties to collectively manage one or more sets of confidential keys for privacy preservation and validation, which renders them vulnerable to collusion attacks between certain clients and servers. Consequently, we propose a privacy-preserving federated learning mechanism under a dual-server architecture. This mechanism adopts a coding matrix computation-based approach to ensure the privacy security of local models at the client side and achieves the aggregation of local models through collaborative efforts between two servers situated at the server side. To verify the correctness of the aggregated model, a Model Verification Code (MVC) mechanism is designed. By effectively combining the MVC mechanism with the coded matrix computation, there is no requirement for all clients to possess identical sets of confidential keys during the privacy preservation and verification process. Meanwhile, this ensures the fulfillment of security requirements under the malicious threat posed by the server. The computational overhead of this mechanism remains low since it avoids the application of complex cryptographic primitives. We perform extensive experiments on real datasets, and the experimental results further demonstrate the proposed scheme exhibits lightweight characteristics while ensuring the validity and usability of the model.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104335"},"PeriodicalIF":8.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160066","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":"3D UAV path planning based on an improved TD3 deep reinforcement learning for data collection in an urban environment","authors":"Mohammad Nazemi Jenabi,&nbsp;Hadi Asharioun,&nbsp;Mahdi Pourgholi","doi":"10.1016/j.jnca.2025.104336","DOIUrl":"10.1016/j.jnca.2025.104336","url":null,"abstract":"<div><div>With the rapid growth in the number of users and services in communication networks, unmanned aerial vehicles (UAVs) are expected to play a significant role in future wireless communication systems. One of the key applications of UAVs is data collection in Internet of Things (IoT) networks. This paper addresses a three-dimensional (3D) UAV path planning optimization problem aimed at minimizing the completion time of data collection in urban environments, taking into account real-world constraints such as frequent communication link blockages between UAVs and sensors caused by buildings. To tackle this challenge, we propose an improved Deep Reinforcement Learning (DRL) algorithm, referred to as the Dropout-Based Prioritized TD3 Algorithm (DPTD3). This method integrates the TD3 algorithm with the Prioritized Experience Replay Buffer (PER) strategy and introduces a new Actor network architecture incorporating the Dropout technique. Simulation results demonstrate that the proposed 3D UAV path planning approach reduces both data collection time and UAV energy consumption compared to a two-dimensional (2D) path planning method. Furthermore, the results indicate that during training, the DPTD3 algorithm outperforms other state-of-the-art DRL approaches in terms of both stability and performance.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104336"},"PeriodicalIF":8.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160068","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":"Secure event-triggered control for vehicle platooning against dual deception attacks","authors":"Ali Nikoutadbir ,&nbsp;Sajjad Torabi ,&nbsp;Sadegh Bolouki","doi":"10.1016/j.jnca.2025.104323","DOIUrl":"10.1016/j.jnca.2025.104323","url":null,"abstract":"<div><div>This paper addresses the challenge of achieving secure consensus in a vehicular platoon under dual deception attacks using an event-triggered control approach. The platoon consists of a leader and multiple follower vehicles that intermittently exchange position and velocity information to maintain stability. The study focuses on two types of deception attacks: gain modification attacks, where controller gains are manipulated, and false data injection attacks, which compromise sensor and control data integrity to destabilize the platoon. The research analyzes the duration, frequency, and impact of these attacks on system stability. To address these challenges, a robust event-triggered control scheme is proposed to ensure secure consensus despite the attacks. Sufficient consensus conditions are derived for both distributed static and dynamic event-triggered control schemes, considering constraints on attack duration and frequency. The influence of system matrices and triggering parameters on attack resilience is also analyzed. Additionally, a topology-switching scheme is introduced as a mitigation strategy when attack conditions exceed tolerable limits. The effectiveness of the proposed methodology is validated through simulations across various case studies, demonstrating its ability to maintain platoon stability under dual deception attacks.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104323"},"PeriodicalIF":8.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157537","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":"Autoformer-based mobility and handoff-aware prediction for QoE enhancement in adaptive video streaming in 4G/5G networks","authors":"Maram Helmy ,&nbsp;Mohamed S. Hassan ,&nbsp;Mahmoud H. Ismail ,&nbsp;Usman Tariq","doi":"10.1016/j.jnca.2025.104324","DOIUrl":"10.1016/j.jnca.2025.104324","url":null,"abstract":"<div><div>Traditional Adaptive Bitrate (ABR) algorithms in Dynamic Adaptive Streaming over HTTP (DASH) rely on basic throughput estimation techniques that often struggle to quickly adapt to network fluctuations. As users move across different transportation modes or change from one access point to another (e.g., Wi-Fi to cellular networks or between 4G/5G cells), available bandwidth can vary sharply, causing interruptions, abrupt quality shifts, which impact the ability of conventional ABR algorithms to provide seamless playback and maintain high quality-of-experience (QoE). To address these issues, this paper introduces a novel and comprehensive framework that significantly enhances the adaptability and intelligence of ABR algorithms. The proposed solution integrates three key components: a transformer-based throughput prediction model, a Mobility-Aware Throughput Prediction engine (MATH-P), and a Handoff-Aware Throughput Prediction engine (HATH-P). The transformer-based model outperforms state-of-the-art approaches in predicting throughput for both 4G and 5G networks, leveraging its ability to capture complex temporal patterns and long-term dependencies. The MATH-P engine adapts throughput predictions to varying mobility scenarios, while the HATH-P one manages seamless transitions by accurately predicting 4G/5G handoff events and selecting the appropriate throughput prediction model. The proposed systems were integrated into existing ABR algorithms, replacing traditional throughput estimation techniques. Experimental results demonstrate that the MATH-P and HATH-P engines significantly improve video streaming performance, reducing stall durations, enhancing video quality, and ensuring smoother playback.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104324"},"PeriodicalIF":8.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121249","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":"PRAETOR:Packet flow graph and dynamic spatio-temporal graph neural network-based flow table overflow attack detection method","authors":"Kaixi Wang ,&nbsp;Yunhe Cui ,&nbsp;Guowei Shen ,&nbsp;Chun Guo ,&nbsp;Yi Chen ,&nbsp;Qing Qian","doi":"10.1016/j.jnca.2025.104333","DOIUrl":"10.1016/j.jnca.2025.104333","url":null,"abstract":"<div><div>The flow table overflow attack on SDN switches is considered to be a destructive attack in SDN. By exhausting the computing and storage resources of SDN switches, this attack severely disrupts the normal communication functions of SDN networks. Graph neural networks are now being employed to detect flow table overflow attacks in SDN. When a flow graph is constructed, flow features are commonly utilized as nodes to represent the characteristics of flow table overflow attacks. However, a graph solely relying on these nodes and attributes may not fully encompass all the nuances of the flow table overflow attack. Additionally, GNN model may be difficult in capturing the graph information between different flow graphs over time, thus decreasing the detection accuracy of packet flow graph. To address these issues, we introduce PRAETOR, a detection method for flow table overflow attacks that leverages a packet flow graph and a dynamic spatio-temporal graph neural network. More particularly, The PaFlo-Graph algorithm and the EGST model are introduced by PRAETOR. The PaFlo-Graph algorithm generates a packet flow graph for each flow. It utilizes packet information to construct the graph with more detail, thereby better reflecting the characteristics of flow table overflow attacks. The EGST model is a dynamic spatio-temporal graph convolutional network designed to detect flow table overflow attacks by analyzing packet flow graphs. Experiments were conducted under two network topologies, where we used tcpreplay to replay packets from the bigFlow dataset to simulate SDN network flow. We also employed sFlow to sample packet features. Based on the sampled data, two datasets were constructed, each containing 1,760 network flows. For each packet, eight key features were extracted to represent its characteristics. The evaluation metrics include TPR, TNR, accuracy, precision, recall, F1-score, confusion matrix, ROC curves, and PR curves. Experimental results show that the proposed PaFlo-Graph algorithm generates more detailed flow graphs compared to KNN and CRAM, resulting in an average improvement of 6.49% in accuracy and 8.7% in precision. Furthermore, the overall detection framework, PRAETOR, achieves detection accuracies of 99.66% and 99.44% on Topo1 and Topo2, respectively. The precision scores reach 99.32% and 99.72%, and the F1-scores are 99.57% and 100%, respectively, indicating superior detection performance compared to other methods.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"243 ","pages":"Article 104333"},"PeriodicalIF":8.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121251","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}