Journal of Network and Computer Applications最新文献

{"title":"HELP4DNS: Leveraging the programmable data plane for effective and robust defense against DDoS attacks on DNS","authors":"Mehmet Emin Şahin ,&nbsp;Mehmet Demirci","doi":"10.1016/j.jnca.2025.104198","DOIUrl":"10.1016/j.jnca.2025.104198","url":null,"abstract":"<div><div>DNS is a critical component of the Internet infrastructure, and securing it has been an active research domain, with a particular emphasis on countering DDoS attacks. With the rise of programmable data planes, novel defensive strategies taking advantage of their flexibility and line-rate packet processing capabilities have been developed to counter a range of DDoS attacks. This study proposes two novel methodologies against DNS flood and DNS amplification attacks within programmable data planes using P4. The first approach involves constraining the concurrent active queries per client to mitigate DNS query flood attacks, thereby ensuring that clients generating a high volume of requests adhere to predetermined limits. The proposed method uses concurrent query limits per client by employing a modified token bucket algorithm within an updatable Bloom filter data structure to track and limit DNS queries. This approach effectively rate limits malicious client requests, preventing server overload and safeguarding benign users from any resulting disruptions. The second method is a DNS firewall implemented on the P4 switch situated on the victim’s side to prevent DNS amplification attacks. The proposed firewall utilizes an updatable Bloom filter on a P4 switch, enabling stateful processing of DNS queries at the application layer. Additionally, it supports stateful tracking of fragmented DNS responses resulting from the Extension Mechanisms for DNS. While IP fragmentation occurs at the IP layer, the proposed approach achieves stateful tracking of fragmented DNS responses at the application layer. In this manner, only the responses corresponding to legitimate requests are forwarded among the received DNS responses by the victim, while responses stemming from DNS amplification attacks are blocked. Evaluation results have demonstrated that the proposed approach effectively blocks high-volume DNS amplification attack packets with minimal memory space requirements.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104198"},"PeriodicalIF":7.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903639","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":"An Internet of Things platform for heterogeneous data integration: Methodology and application examples","authors":"M. Muñoz,&nbsp;M. Torres,&nbsp;J.D. Gil,&nbsp;J.L. Guzmán","doi":"10.1016/j.jnca.2025.104197","DOIUrl":"10.1016/j.jnca.2025.104197","url":null,"abstract":"<div><div>The Internet of Things (IoT) has revolutionized the industrial sector, yet digital transformation in this domain faces challenges due to the lack of standardized methodologies for IoT platform development. Many existing approaches struggle with issues such as data heterogeneity, lack of interoperability, and limited scalability when applied to large-scale industrial environments. In order to address the aforementioned gap, this work presents an IoT platform specifically designed for the agro-industrial sector, with interoperability as a core feature. Built using open technologies and an interoperable data model based on the OMA NGSI standard within the FIWARE framework, the platform enables seamless communication between heterogeneous devices and systems. Its scalable architecture allows the easy integration of new devices and scenarios. Additionally, the platform encapsulates industrial models (e.g., climate, production) as services and uses ETL processes to manage data heterogeneity, ensuring interoperability across systems. The platform, developed through a successful collaboration between academia and industry, has been validated in three sceneries that are exemplified in this work. To evaluate its scalability and performance, extensive load tests were conducted in a cloud environment, demonstrating its ability to handle high volumes of concurrent requests while maintaining efficient resource consumption.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104197"},"PeriodicalIF":7.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891376","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 DDoS attack detection method based on IQR and DFFCNN in SDN","authors":"Meng Yue,&nbsp;Huayang Yan,&nbsp;Ruize Han,&nbsp;Zhijun Wu","doi":"10.1016/j.jnca.2025.104203","DOIUrl":"10.1016/j.jnca.2025.104203","url":null,"abstract":"<div><div>Software-Defined Networking (SDN) is an innovative network architecture that enhances network flexibility by decoupling the data plane from the control plane. However, SDN is also faces severe security threats. One of the most damaging threats is the Distributed Denial-of-Service (DDoS) attack, which can disrupt network functionality and adversely affect legitimate users. Current solutions against DDoS attacks in SDN encounter challenges such as inadequate feature extraction, limited generalization of detection models, and frequent requests for data from network devices. These issues result in low detection accuracy and high resource consumption. We propose a DDoS attack detection method based on abnormal alarm and deep detection. First, we use the anomaly detection capability of interquartile range (IQR) to monitor the packet_in message rate of each switch and design a dynamic threshold alarm algorithm. This algorithm can preliminarily identify abnormal switches. In addition, we propose an integrated-feature-selection method to expose the most-relevant flow features, and extract new SDN flowtable features. Based on these features, we design a Deep Feature Fusion Convolutional Neural Network (DFFCNN) model to execute deep DDoS attack detection. This model combines a self-attention mechanism with multi-scale features extraction, enhancing its ability to capture data patterns. Experimental results on three typical datasets—IDS2017, IDS2018, and DDoS2019—demonstrate that the proposed method achieves an average detection accuracy of 99.54 % and a false positive rate of 0.53 %. This represents an improvement of 1.65 % over existing detection methods and reduces the false positive rate by 1.38 %. Additionally, the proposed two-stage detection method decreases CPU utilization by an average of 12.8 % to the existing polling detection method.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104203"},"PeriodicalIF":7.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883148","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":"Predictive safe delivery with machine learning and digital twins collaboration for decentralized crowdsourced systems","authors":"Feruz Elmay ,&nbsp;Maha Kadadha ,&nbsp;Rabeb Mizouni ,&nbsp;Shakti Singh ,&nbsp;Azzam Mourad ,&nbsp;Hadi Otrok","doi":"10.1016/j.jnca.2025.104196","DOIUrl":"10.1016/j.jnca.2025.104196","url":null,"abstract":"<div><div>Crowdsourced last-mile delivery leverages workers willing to complete delivery tasks posted by requesters for incentives through dedicated platforms. While this is effective and affordable, crowdsourced delivery faces significant challenges, including the workforce’s uncertain availability and capability to deliver packages safely, inadequate package monitoring, and a lack of trust among the participants. These issues have become increasingly pronounced due to the rapid growth of e-commerce, where last-mile delivery constitutes a substantial portion of overall delivery costs and delays. Existing centralized works attempt to tackle the uncertainty of workforce availability and capability by analyzing worker behavior or predicting the fulfillment success in the allocation mechanism. On the other hand, decentralized solutions introduce blockchain-based platforms for transparent and secure interaction and transaction recording to tackle the challenge of lack of trust. These existing approaches, however, overlook a critical aspect of crowdsourced delivery, where successful task completion relies not only on worker commitment but also on delivered package status. In response, this work proposes a comprehensive framework leveraging machine learning, blockchain, and digital twins for package safe and transparent delivery. The proposed approach utilizes machine learning models to predict the delivery success of packages off-chain secured using IPFS and smart contracts events. Subsequently, a task allocation mechanism hosted on a blockchain integrates the projected delivery success with the Quality of Service (QoS) metric to assign tasks to workers with an increased likelihood of successful completion. Package digital twins are designed and employed to monitor packages in real-time, providing transparent feedback on package status and delivery. The trained models demonstrate an average accuracy of 96% and an F1-score of 94%. Evaluation with real-world data revealed substantial enhancements: task success rates improved by 48%, quality of service by 41%, worker reputation by 26%, and package delivery quality by 16%.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104196"},"PeriodicalIF":7.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873940","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 security authentication and key agreement scheme for railway space-ground integrated network based on ideal lattice","authors":"Yong Chen,&nbsp;Zhaofeng Xin,&nbsp;Bingwang Zhang,&nbsp;Junli Jia","doi":"10.1016/j.jnca.2025.104194","DOIUrl":"10.1016/j.jnca.2025.104194","url":null,"abstract":"<div><div>At present, the Global System for Mobile Communications- Railway (GSM-R) is widely used in high-speed railway, but it is a 2G narrowband system that cannot meet the needs of intelligent development of high-speed railways. In the future, space-ground integrated railway communication network will gradually become an inevitable trend of railway development. Aiming at the problems of identity non-mutual authentication, data privacy leakage and low communication efficiency in the railway space-ground integrated network, this paper proposes a space-ground integrated security authentication and key agreement scheme based on ideal lattice. Firstly, the public key cryptosystem based on ideal lattice is designed to complete the mutual authentication of identity between train, Middle Earth Orbit (BDS) satellite and Authentication Server Function/Unified Data Management (AUSF/UDM), which can effectively resist the man-in-the-middle attack and replay attacks. Secondly, a zero-knowledge proof non-interactive Schnorr protocol digital signature algorithm is proposed and a random number mechanism is added to ensure the secure transmission of authentication information and improve the communication efficiency while reducing the number of communication interactions. Then, the session key is generated using the ideal lattice key agreement mechanism and hash function, which ensures the forward and backward security of the session key. Finally, the proposed method has been analyzed for efficiency and security through ideal lattice correctness analysis, BAN logic theory proofs and Tamarin protocol simulation tool. The results show that the proposed method can not only resist DoS attacks and realize the traceability of malicious attacks, but also has low computational overhead, which can better satisfy the security requirements of next generation space-ground integrated railway communication system.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104194"},"PeriodicalIF":7.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870050","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":"DRacv: Detecting and auto-repairing vulnerabilities in role-based access control in web application","authors":"Ke Xu ,&nbsp;Bing Zhang ,&nbsp;Jingyue Li ,&nbsp;Haitao He ,&nbsp;Rong Ren ,&nbsp;Jiadong Ren","doi":"10.1016/j.jnca.2025.104191","DOIUrl":"10.1016/j.jnca.2025.104191","url":null,"abstract":"<div><div>Traditional methods for analyzing Broken Access Control (BAC) vulnerabilities have limitations regarding low coverage of access control rules, high false positive rate (FPR). Additionally, state-of-the-art strategies for repairing BAC vulnerabilities utilizing statement-level replacement as a repair method may introduce new logical errors. To address these challenges, we propose a novel approach called DRacv (<u>D</u>etect and <u>R</u>epair <u>A</u>ccess <u>C</u>ontrol <u>V</u>ulnerabilities) to identify and auto-repair vulnerabilities in Role-Based Access Control (RBAC) mode used in web applications. To detect vulnerabilities, DRacv first constructs a Fine-grained Global Multi-attribute Architectural Navigation Graph model (<em>FG-MANG</em>) for web applications through dynamic execution and static analysis, which characterizes full relationships between roles, privileges, and accessible page resources. Based on access control rules extracted from <em>FG-MANG</em>, DRacv generates targeted attack payloads to detect BAC vulnerabilities, significantly reducing FPR and eliminating redundant attack payloads. To auto-repair the identified vulnerabilities, DRacv first precisely extracts access control privilege parameters, validation functions, and contextual statements to construct the patch code templates. These templates generate user- and role-level verification patch codes for different users and roles. Instead of changing the vulnerable code, the patch codes behave like firewalls. They are added as separate files and invoked by the web page with vulnerability to defend against access control compromises. DRacv was evaluated on 12 popular open-source web applications in PHP and JAVA. From the applications, DRacv identified 35 vulnerabilities (11 were new) with only one false positive, achieving an FPR of 2.78%. We also compared DRacv’s detection results with state-of-the-art studies. Results show that DRacv outperforms those studies regarding the number of vulnerabilities detected and FPR. Among the 35 vulnerabilities detected, DRacv automatically repaired 34 of them, achieving a repair rate of 97.14%. The evaluation results also show that DRacv auto-fixed more vulnerabilities than the two state-of-the-art auto-repairing methods.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104191"},"PeriodicalIF":7.7,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860208","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":"Variational Deep Clustering approaches for anomaly-based cyber-attack detection","authors":"Van Quan Nguyen ,&nbsp;Viet Hung Nguyen ,&nbsp;Long Thanh Ngo ,&nbsp;Le Minh Nguyen ,&nbsp;Nhien-An Le-Khac","doi":"10.1016/j.jnca.2025.104182","DOIUrl":"10.1016/j.jnca.2025.104182","url":null,"abstract":"<div><div>Detecting network anomalies is a critical cybersecurity task, yet existing methods struggle with high-dimensional data and limited interpretability in latent space. These challenges hinder precise differentiation between normal and anomalous activities due to (i) the chaotic distribution of normal samples, (ii) the absence of constraints to optimize the normal region’s hypervolume leading to high false alarm rates, (iii) the lack of prior knowledge for estimating the probability distribution of normal data, and (iv) slow inference times.</div><div>This research introduces two innovative deep generative models: Deep Clustering Variational Auto-Encoder (DCVAE) and Deep Clustering Support Vector Data Description Variational Auto-Encoder (DC-SVDD-VAE), designed to enhance learning latent features for detecting network anomalies. Both models incorporate a clustering layer within the Encoder to discover a clustering architecture suitable for normal network data. They also leverage prior information, specifically a Gaussian probability distribution, to estimate the posterior distribution that generates normal network data. Additionally, the DC-SVDD-VAE model integrates SVDD layers, which refine the clustering structure by mapping it onto an optimally sized hypersphere before computing the posterior probability. These approaches improve the separation between normal and abnormal regions at latent space, making it easier to identify significant/distinguishing latent features.</div><div>Both models were evaluated in conjunction with seven distinct one-class anomaly detection methods to assess the efficiency of the proposed solutions and the robustness of the generated features. These detectors were assessed using well-known intrusion benchmark datasets, including NSL-KDD, UNSW-NB15, CIC-IDS-2017, CSE-CIC-IDS-2018, and CTU-13. The experimental findings revealed that both models outperformed existing baselines and state-of-the-art approaches in terms of accuracy. Furthermore, inference stage processing times showed a notable decrease.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104182"},"PeriodicalIF":7.7,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863318","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":"Task offloading strategy of vehicle edge computing based on reinforcement learning","authors":"Lingling Wang,&nbsp;Wenjie Zhou,&nbsp;Linbo Zhai","doi":"10.1016/j.jnca.2025.104195","DOIUrl":"10.1016/j.jnca.2025.104195","url":null,"abstract":"<div><div>The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost consumption under the maximum delay constraint by jointly considering the positions, speeds and computation resources of vehicles. Due to the complexity of the problem, we propose the vehicle deep Q-network (V-DQN) algorithm. In V-DQN algorithm, we firstly propose a vehicle adaptive feedback (VAF) algorithm to obtain the priority setting of processing tasks for service vehicles. Then, the V-DQN algorithm is implemented based on the result of VAF to realize task offloading strategy. Specially, the interruption problem caused by the movement of the vehicle is formulated as a return function to evaluate the task offloading strategy. The simulation results show that our proposed scheme significantly reduces cost consumption.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104195"},"PeriodicalIF":7.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847508","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":"An overview and solution for democratizing AI workflows at the network edge","authors":"Andrej Čop,&nbsp;Blaž Bertalanič,&nbsp;Carolina Fortuna","doi":"10.1016/j.jnca.2025.104180","DOIUrl":"10.1016/j.jnca.2025.104180","url":null,"abstract":"<div><div>With the process of democratization of the network edge, hardware and software for networks are becoming available to the public, overcoming the confines of traditional cloud providers and network operators. This trend, coupled with the increasing importance of AI in 6G and beyond cellular networks, presents opportunities for innovative AI applications and systems at the network edge. While AI models and services are well-managed in cloud systems, achieving similar maturity for serving network needs remains an open challenge. Existing open solutions are emerging and are yet to consider democratization requirements. In this work, we identify key requirements for democratization and propose NAOMI, a solution for democratizing AI/ML workflows at the network edge designed based on those requirements. Guided by the functionality and overlap analysis of the O-RAN AI/ML workflow architecture and MLOps systems, coupled with the survey of open-source AI/ML tools, we develop a modular, scalable, and distributed hardware architecture-independent solution. NAOMI leverages state-of-the-art open-source tools and can be deployed on distributed clusters of heterogeneous devices. The results show that NAOMI performs up to 40% better in deployment time and up to 73% faster in AI/ML workflow execution for larger datasets compared to AI/ML Framework, a representative open network access solution, while performing inference and utilizing resources on par with its counterpart.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"239 ","pages":"Article 104180"},"PeriodicalIF":7.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834932","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":"Innovative detection of IoT cyber threats using a GBiTCN-Temformer and MKOA framework","authors":"Vipin Rai ,&nbsp;Pradeep Kumar Mishra ,&nbsp;Shivani Joshi ,&nbsp;Rajiv Kumar ,&nbsp;Avinash Dwivedi ,&nbsp;Amrita","doi":"10.1016/j.jnca.2025.104192","DOIUrl":"10.1016/j.jnca.2025.104192","url":null,"abstract":"<div><div>In the digital era, the Internet of Things (IoT) platform is progressively utilized in several applications due to its versatility, adaptability, and accessibility. Despite that, the lack of security protocols, improper device updates, and unauthorized access exposes the IoT environment to diverse cyber threats, which affect network security and user confidentiality. To enhance cybersecurity, various deep learning (DL) approaches have been developed, yet these techniques have struggled to detect various attacks affecting the IoT platform, often yielding imprecise results and high false positive rates. This research proposes a novel framework that leverages the Modified Kepler optimization algorithm (MKOA) based feature selection model and the Gated Bidirectional Temporal Convolutional Network (GBiTCN) based Temporal transformer (Temformer) detection model to improve the cybersecurity and integrity of the IoT devices. The MKOA technique is introduced to select optimal features from the normalized data, thereby analyzing the characteristics of attacks and reducing data dimensionality. In addition, this study introduces a hybrid detection model, which combines the Gated BiTCN and Temformer approaches. The Gated BiTCN captures the bidirectional semantic features of network traffic, while the Temformer extracts correlations among different sequences for precise cyber-attack detection. Further, the effectiveness of this proposed technique is measured by utilizing IoT attack datasets with distinct parameters and it attains a higher accuracy of 98.76 % during attack detection tasks. These results demonstrate that the proposed technique provides exceptional performance in cyber threat detection. Experimental validation shows that the model significantly contributes to cyber-attack detection and enhances the cybersecurity of IoT networks effectively.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"240 ","pages":"Article 104192"},"PeriodicalIF":7.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859544","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}