Transactions on Emerging Telecommunications Technologies最新文献

{"title":"A Concise Survey on Modern Web-Based Phishing Techniques and Advanced Mitigation Strategies","authors":"Dhanavanthini Panneerselvam,&nbsp;Sibi Chakkaravarthy Sethuraman,&nbsp;Ajith Jubilson Emerson,&nbsp;Tarun Kumar Kanakam","doi":"10.1002/ett.70119","DOIUrl":"https://doi.org/10.1002/ett.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>Phishing is a tactical technique practiced by cyber-criminals, wherein the target systems are approached, made vulnerable, and exploited. A Phisher who does the act of phishing is always creative, calculative, and persistent. This potentially leads to the increase in the success rate of phishing and the individuals who are technically expertise even falls in phishing campaigns. This article discusses about the various web-based phishing techniques used by the modern day cyber criminals. Various mitigation techniques related to the state of the art machine learning and deep learning techniques are also studied. The article also extensively discusses about the features utilized for the detection. Additionally, a qualitative and quantitative comparison of different studies for mitigating the web phishing attacks is also examined.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Robotic Cloud Automation-Enabled Attack Detection and Secure Robotic Command Verification Using LADA-C-RNN and S-Fuzzy","authors":"Basava Ramanjaneyulu Gudivaka,&nbsp;Rajya Lakshmi Gudivaka,&nbsp;Raj Kumar Gudivaka,&nbsp;Dinesh Kumar Reddy Basani,&nbsp;Sri Harsha Grandhi,&nbsp;Faheem Khan","doi":"10.1002/ett.70115","DOIUrl":"https://doi.org/10.1002/ett.70115","url":null,"abstract":"<div>\u0000 \u0000 <p>The rise of digital technology and Artificial Intelligence (AI) has led to the increased use of smart robots in various sectors. However, security and trust are significant concerns about deploying robots in critical infrastructures. Therefore, a secure and reliable robotic command control system is essential for successful robot integration. None of the prevailing systems focused on attack prediction during cloud-based robot control and data processing. Hence, this paper proposes a secure model called RCA-assisted attack detection and robotic command verification using LADA-C-RNN and S-Fuzzy. The robot controller is initially registered using the user ID and password in the cloud application. During login, the SCTDA is used to verify the robot controller's authority. Then, the robot controller's task is subjected to the attack detection phase. In the attack detection phase, the dataset is initially gathered and preprocessed. Thereafter, the temporal pattern analysis is done, followed by feature extraction. Subsequently, the optimal features are selected via GMJFOA. Then, the selected features are inputted to the LADA-C-RNN, which performs attack detection. Next, the normal data is fed into the traffic prioritization. Then, the prioritized tasks are inputted to the robot command data verification, thus increasing the security level. Finally, the proposed approach had minimum latency with 98.42% accuracy.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Critical Review of Different Approaches of Multiparty Privacy Protection Methods and Effectiveness on Social Media","authors":"P. Jayaprabha,&nbsp;K. Paulose Jacob","doi":"10.1002/ett.70130","DOIUrl":"https://doi.org/10.1002/ett.70130","url":null,"abstract":"<div>\u0000 \u0000 <p>Social networking is a significant notion that has emerged for effective communication among multiple users. Social media services are in high demand among users all around the world. Privacy is important on social networking sites, and privacy concerns are particularly sensitive. Social media has numerous applications, and ensuring multiparty privacy (MP) among various users is a critical requirement. Massive research has been undertaken to manage secured MP across network users. However, certain issues still come up, like authentication, co-ownership of data by third parties, surveillance, and data misuse. The privacy preferences of a certain user are the priority by which the user can adjust or edit their network settings. Conflicts between users can be avoided, high security for personal data can be achieved, and highly confidential information can be maintained with the help of user preferences. Some security flaws in social media allow for the misuse of private information and the emergence of user conflicts. Therefore, privacy preservation techniques are developed and put into practice in order to address privacy concerns and provide improved security during data transfer. These techniques serve as technical assistance in recognizing and resolving disputes inside the MP management. For the construction of privacy preservation methods, real-world empirical data, user-centered MP controls, privacy-improved party analysis, hypothetical privacy support, and privacy assurance in the case of multiparty agreement are required.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Security and Privacy Preservation via Interference Tolerant Fast Convergence Zeroing Neural Network With Reptile Search Optimization Algorithm in Fog-Cloud Computing","authors":"Pakkarisamy Janakiraman Sathish Kumar,&nbsp;Neha Verma,&nbsp;Shivani Gupta,&nbsp;Rajendran Jothilakshmi","doi":"10.1002/ett.70114","DOIUrl":"https://doi.org/10.1002/ett.70114","url":null,"abstract":"<div>\u0000 \u0000 <p>More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud computing environments is a widely used application among them. With training limited to sharing parameters, DDL can offer more privacy protection than centralized deep learning. Nevertheless, DDL still faces two significant security obstacles when it comes to fog-cloud computing are How to ensure that users' identities are not stolen by outside enemies, and How to prevent users' privacy from being disclosed to other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing Neural Network for Security and Privacy Preservation with Reptile Search Optimization Algorithm in Fog-Cloud Computing environment (SPP-ITFCZNN-RSOA-FCC) is proposed. ITFCZNN is proposed for security and privacy preservation, Then Reptile Search Optimization Algorithm (RSOA) is proposed to optimize the ITFCZNN, and Effective Lightweight Homomorphic Cryptographic Algorithm (ELHCA) is used to encrypt and decrypt the local gradients. The proposed SPP-ITFCZNN-RSOA-FCC system attains a better security balance, efficiency, and functionality than existing efforts. The proposed SPP-ITFCZNN-RSOA-FCC is implemented using Python. The performance metrics like accuracy, resource overhead, computation overhead, and communication overhead are considered. The performance of the SPP-ITFCZNN-RSOA-FCC approach attains 29.16%, 20.14%, and 18.93% high accuracy, and 11.03%, 26.04%, and 23.51% lower Resource overhead compared with existing methods including FedSDM: Federated learning dependent smart decision making component for ECG data at internet of things incorporated Edge-Fog-Cloud computing (SPP-FSDM-FCC), A collaborative computation with offloading in dew-enabled vehicular fog computing to compute-intensive with latency-sensitive dependence-aware tasks: Federated deep Q-learning method (SPP-FDQL-FCC), and a fog-edge-enabled intrusion identification scheme for smart grids (SPP-FSVM-FCC) respectively.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 the Road Ahead: A Survey on Internet of Vehicles Security Powered by a Conceptual Blockchain-Based Intrusion Detection System for Smart Cities","authors":"Fatima Al-Quayed,&nbsp;Noshina Tariq,&nbsp;Mamoona Humayun,&nbsp;Farrukh Aslam Khan,&nbsp;Muhammad Attique Khan,&nbsp;Thanaa S. Alnusairi","doi":"10.1002/ett.70133","DOIUrl":"https://doi.org/10.1002/ett.70133","url":null,"abstract":"<p>The Internet of Vehicles (IoV) is a critical component of the smart city. Various nodes exchange sensitive data for urban mobility, such as identification, position, messages, speed, and traffic statistics. Along with developing smart cities come threats to privacy and security through networks. Security is of the highest priority, considering various security-privacy risks from the wellness, safety, and confidentiality of men and women inside the vehicle. This survey presents a detailed analysis of state-of-the-art and evolving security challenges to IoV systems. It handles security challenges, such as data integrity and privacy. It also includes a critical review of the literature to identify gaps in current security mechanisms. It uses complete mathematical modeling and case studies to show the practical effectiveness of the proposed solutions. It aims to guide future development and implementation of more secure, efficient, and resilient IoV systems, particularly in smart city environments. It also introduces a novel Intrusion Detection System (IDS) with Artificial Intelligence (AI), smart contracts, and blockchain technology. These smart contracts ensure instant security with the utmost level of vulnerability through blockchain technology. In addition, we proposed a hybrid multi-layered framework using Fog to conserve the resources at the vehicle level. We used mathematical proof to assess this framework. Merging blockchain, smart contracts, and AI into IoVs could increase human security by removing significant vulnerabilities.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70133","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"SDHO-KGNN: An Effective Knowledge-Enhanced Optimal Graph Neural Network Approach for Fraudulent Call Detection","authors":"Pooja Mithoo,&nbsp;Manoj Kumar","doi":"10.1002/ett.70101","DOIUrl":"https://doi.org/10.1002/ett.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>Rapid advancements in mobile communication technologies have led to the progression of telecom scams that not only deplete individual fortunes but also affect social income. Hence, fraudulent call detection gains significance, which not only aims to proactively recognize the frauds, but also alleviate the fraudulent activities to manage external losses. Though the traditional methods, such as rule-based systems and supervised machine learning techniques, actively engage in detecting such fraudulent activities, they fail to adapt to the evolving fraud patterns. Therefore, this research introduces a sheepdog hunt optimization-enabled knowledge-enhanced optimal graph neural network classifier (SDHO-KGNN) approach for detecting fraudulent calls accurately. The effectiveness of the proposed SDHO-KGNN approach is achieved through the combination of the power of graph representation learning with expert insights, which allows the proposed SDHO-KGNN approach to capture complex relationships and patterns within telecom data. Additionally, the integration of the SDHO algorithm enhances model performance by optimizing the discrimination between legitimate and fraudulent calls. Moreover, the SDHO-KGNN classifier captures the intricate call patterns and relationships within dynamic call networks, thereby attaining a better accuracy, precision, and recall of 93.8%, 95.91%, and 95.53% for 90% of the training.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Enhancing Wireless System Secrecy Capacity Through NOMA Scheme and Multiple UAV-Mounted IRSs Amidst Colluding and Non-Colluding Eavesdroppers","authors":"Duc Thinh Vu,&nbsp;Ba Cao Nguyen,&nbsp;Tran Manh Hoang,&nbsp;Taejoon Kim,&nbsp;Bui Vu Minh,&nbsp;Phuong T. Tran","doi":"10.1002/ett.70135","DOIUrl":"https://doi.org/10.1002/ett.70135","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a novel approach to enhance the secrecy performance of nonorthogonal multiple access (NOMA) systems by leveraging multiple unmanned aerial vehicles (UAVs) equipped with intelligent reflecting surfaces (IRSs). In this proposed system, multiple UAVs mounted with IRSs (shortened as U/Ss) are strategically deployed to assist two legitimate users in the presence of multiple colluding eavesdroppers (CEs) or noncolluding eavesdroppers (NCEs) attempting to intercept messages. The paths from the transmitter to the users are combined with those involving the U/Ss to maximize the received message power. We derive mathematical expressions for the secrecy capacities (SCs) of the proposed U/S-NOMA systems over Nakagami-<span></span><math></math> channels. Additionally, asymptotic expressions for SCs in the high transmit power region are provided. Numerical results demonstrate that the SCs of U/S-NOMA systems significantly surpass those of traditional NOMA networks lacking U/Ss. Notably, the U/S-NOMA systems achieve their highest SCs more rapidly than traditional NOMA systems. Consequently, the integration of U/Ss proves effective in reducing transmit power and enhancing the secrecy performance of NOMA systems. Furthermore, we also delve into the impact of key parameters such as the number of reflecting elements (REs) in U/Ss, carrier frequency, U/Ss' positions, fading order, bandwidth, number of eavesdroppers, and NOMA power allocation coefficients. Valuable recommendations are presented based on a thorough investigation of these crucial parameters.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Enhancing Autonomous Vehicle Security: Federated Learning for Detecting GPS Spoofing Attack","authors":"Maqsood Muhammad Khan,&nbsp;Mohsin Kamal,&nbsp;Maliha Shabbir,&nbsp;Saad Alahmari","doi":"10.1002/ett.70138","DOIUrl":"https://doi.org/10.1002/ett.70138","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous vehicles (AVs) are poised to transform modern transportation, providing superior traffic management and improved user experiences. However, there exists a considerable risk to the acquisition of Position, Velocity and Time (PVT) in AVs, since the acquisition of PVT is vulnerable to Global Positioning System (GPS) spoofing attacks that could redirect the AV to wrong paths or lead to security threats. To address these issues, we propose a novel approach for detecting GPS spoofing attacks in AVs using Federated Learning (FL) with trajectories obtained from the Car Learning to Act (CARLA) simulator. Each vehicle autonomously performs localization using sensor data that includes yaw rate, steering angle, as well as wheel speed. The obtained localized coordinates (authentic and spoofed) are utilized to compute weights. These weights are aggregated at the Roadside Unit (RSU) and shared with the global model utilizing Support Vector Machines (SVM) for classification. The global model updates local models through FL, ensuring data privacy and collaborative learning. The experimental results show that the proposed model achieves 99% accuracy, 98% F1 score, and the AUC-ROC of 99% outperforming traditional machine learning methods including the K-Nearest Neighbors (KNN) and Random Forest (RF). The results demonstrate the practicality of using FL to improve the security of AVs against GPS spoofing attacks with limited data sharing, thereby offering a potential approach for real-world applications.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm","authors":"R. Barona,&nbsp;E. Babu Raj","doi":"10.1002/ett.70092","DOIUrl":"https://doi.org/10.1002/ett.70092","url":null,"abstract":"<div>\u0000 \u0000 <p>The increase in Distributed Denial of Service (DDoS) attacks poses a considerable threat to the security and stability of the current network, especially in Internet of Things (IoT) and cloud environments. Traditional detection methods often struggle with the inability to achieve a balance between detection accuracy and computational efficiency. In this manuscript, the Classification of Multiclass DDOS Attack Detection using Bayesian Weighted Random Forest Optimized with Gazelle Optimization Algorithm (DDOS-AD-BWRF-GOA) is proposed. First, the raw data is gathered from the CICDDoS2019 dataset. Then, input data are preprocessed utilizing Adaptive Bitonic Filtering for normalizing the values. The preprocessed data are fed to the Improved Feed Forward Long Short-Term Memory technique for selecting features that increase the model's execution time. The selected features are supplied to the Bayesian Weighted Random Forest (BWRF), which classifies the multiclass DDOS attack. In general, Bayesian Weighted Random Forest does not adopt any optimization methods to define optimal parameters to guarantee exact DDOS identification. Hence, GOA is proposed to optimize the Bayesian Weighted Random Forest classifier. The proposed method is implemented in MATLAB. The performance metrics, such as Accuracy, Precision, Recall, <i>F</i>1-score, Specificity, Error rate, and Computational time are evaluated. The proposed method attains 15.34%, 24.1%, and 18.9% higher accuracy and 12.4%, 18.24%, and 22.6% higher precision when analyzed with existing techniques: Hybrid deep learning method for DDOS detection and classification (HDL-DDOS-DC), Edge-HetIoT Defense against DDoS attack utilizing learning techniques (EHD-DDOS-LT), and Digital twin-enabled intelligent DDOS detection for autonomous core networks (DTI-DDOS-ACN), respectively.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Resource Provisioning in Cloud Computing Using Optimized Wasserstein Deep Convolutional Generative Adversarial Networks","authors":"C. Santhiya,&nbsp;S. Padmavathi","doi":"10.1002/ett.70128","DOIUrl":"https://doi.org/10.1002/ett.70128","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud computing (CC) has revolutionized the way resources are managed and delivered by providing scalable, on-demand services. However, dynamic resource provisioning remains a complex challenge due to unpredictable workloads, varying user demands, and the need to maintain cost efficiency. Traditional resource allocation techniques lack the adaptability required to optimize resource usage under dynamic conditions. This manuscript presents a novel approach for dynamic resource provisioning using an Optimized Wasserstein Deep Convolutional Generative Adversarial Network (DRP-WDCGAN-AHBA). Initially, the input data are collected from the Grid Workloads Dataset, which provides a comprehensive representation of workload patterns in cloud environments. The input data undergo rigorous preprocessing using Adaptive Self-Guided Filtering (ASGF) to ensure data quality. Then, Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN) is used to forecast CPU utilization over specified time intervals of 5, 15, 30, and 60 min. The Adaptive Hybrid Bat Algorithm (AHBA) is employed to optimize resource allocation dynamically and ensure efficient utilization. The proposed DRP-WDCGAN-AHBA model attains 20.36%, 18.63%, and 21.24% lower energy consumption and 16.78%, 23.64%, and 26.32% lower response time when compared with existing models, such as Multi-agent QoS-aware autonomic resource provisioning method BPM in containerized multi-cloud environs for elastic (DRP-QoS-EDSAE), Multi-objective dependent Scheduling Method for Effective Resource Utilization in Cloud Computing (DRP-LS-CSO-ARNN), and Energy-aware fully adaptive resource provisioning in collaborative CPU-FPGA cloud environs: Journal of Parallel and Distributed Computing (EFARP-CPU-FPGA).</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}