Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00054
Pu Wang, Zhiyi Zhang, Yuqian Zhou, Zhiqiu Huang
Image recognition software has been widely used in many vital areas, so it needs to be thoroughly tested with images as test data. However, for some special areas, such as medical treatment, there are only a few sufficient and credible test data. Some test data still depends on the training data, which results in the defect detection ability of the testing is not high. In this paper, we propose a new test data augmentation approach with combing domain knowledge and data mutation. Given an image, our approach extracts the features of the recognition targets in this image based on domain knowledge, then mutates these features to generate new images. In theory, our approach could generate high-quality test data, which helps testing image recognition software adequately, and improving the accuracy of image recognition software.
{"title":"Test Data Augmentation for Image Recognition Software","authors":"Pu Wang, Zhiyi Zhang, Yuqian Zhou, Zhiqiu Huang","doi":"10.1109/QRS-C51114.2020.00054","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00054","url":null,"abstract":"Image recognition software has been widely used in many vital areas, so it needs to be thoroughly tested with images as test data. However, for some special areas, such as medical treatment, there are only a few sufficient and credible test data. Some test data still depends on the training data, which results in the defect detection ability of the testing is not high. In this paper, we propose a new test data augmentation approach with combing domain knowledge and data mutation. Given an image, our approach extracts the features of the recognition targets in this image based on domain knowledge, then mutates these features to generate new images. In theory, our approach could generate high-quality test data, which helps testing image recognition software adequately, and improving the accuracy of image recognition software.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123249006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00050
Rui Wang, Yong Guan, Xiaojuan Li, Rui Zhang
Cyber physical system (CPS) is a multi-dimensional complicated system integrating computing, communication and physical environment. CPS is widely used in safety-critical areas such as aerospace, intelligent transportation and medical equipment. So ensuring the security and reliability of CPS is of great significance. Formal verification is one of the useful ways. This paper builds timed automata models for the communication process of CAN bus used in CPS. Our research especially analyses the gateway in the communication process, and simulates the transmission with different rates between the external environment and internal unit. The task also takes into account the packet transmission priority. The model checking tool Uppaal is used to verify the functional and real-time properties. The verification results illustrate that the established model can meet the relevant properties, and the packet can be transmitted in an orderly and efficient manner.
{"title":"Formal Verification of CAN Bus in Cyber Physical System","authors":"Rui Wang, Yong Guan, Xiaojuan Li, Rui Zhang","doi":"10.1109/QRS-C51114.2020.00050","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00050","url":null,"abstract":"Cyber physical system (CPS) is a multi-dimensional complicated system integrating computing, communication and physical environment. CPS is widely used in safety-critical areas such as aerospace, intelligent transportation and medical equipment. So ensuring the security and reliability of CPS is of great significance. Formal verification is one of the useful ways. This paper builds timed automata models for the communication process of CAN bus used in CPS. Our research especially analyses the gateway in the communication process, and simulates the transmission with different rates between the external environment and internal unit. The task also takes into account the packet transmission priority. The model checking tool Uppaal is used to verify the functional and real-time properties. The verification results illustrate that the established model can meet the relevant properties, and the packet can be transmitted in an orderly and efficient manner.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121810357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00091
Philip Makedonski, Ilie-Daniel Gheorghe-Pop, A. Rennoch, F. Kristoffersen, Bostjan Pintar, A. Ulrich
This article reports on experiences from the use of the ETSI Test Description Language (TDL) and its extension for structured test objective specification (TDL-TO) for the definition of functional and non-functional test purposes in the Internet of Things (IoT) domain. The experiences are based on results from different working groups at ETSI TC MTS and the ETSI Specialist Task Force (STF) 574, focusing on the definition of test purposes for functional, security, and performance testing of the CoAP and MQTT protocols as well as VxLTeinteroperability testing.
{"title":"Using TDL for Standardised Test Purpose Definitions","authors":"Philip Makedonski, Ilie-Daniel Gheorghe-Pop, A. Rennoch, F. Kristoffersen, Bostjan Pintar, A. Ulrich","doi":"10.1109/QRS-C51114.2020.00091","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00091","url":null,"abstract":"This article reports on experiences from the use of the ETSI Test Description Language (TDL) and its extension for structured test objective specification (TDL-TO) for the definition of functional and non-functional test purposes in the Internet of Things (IoT) domain. The experiences are based on results from different working groups at ETSI TC MTS and the ETSI Specialist Task Force (STF) 574, focusing on the definition of test purposes for functional, security, and performance testing of the CoAP and MQTT protocols as well as VxLTeinteroperability testing.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133310299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00117
Jiaxin Liu, Wei Dong, Binbin Liu, Yating Zhang, Daiyan Wang
This paper presents the ongoing work of studying the iterative program synthesis based on knowledge searched from the Internet, which can fairly reduce the scale of program space and improve the efficiency of synthesis. First, we implement a tool named Args(api Recommendation via General Search) to obtain the API knowledge from the Internet. Second, we propose an iterative method that incrementally constructs the program space to quickly approach the target program. The initial experimental result shows the effectiveness of our work.
本文介绍了基于互联网知识搜索的迭代程序综合的研究工作，该方法可以较好地减小程序空间的规模，提高综合效率。首先，我们实现了一个名为Args(api Recommendation via General Search)的工具，从互联网上获取api知识。其次，我们提出了一种迭代方法，增量构建程序空间以快速接近目标程序。初步的实验结果表明了我们工作的有效性。
{"title":"Effective Iterative Program Synthesis with Knowledge Searched from Internet","authors":"Jiaxin Liu, Wei Dong, Binbin Liu, Yating Zhang, Daiyan Wang","doi":"10.1109/QRS-C51114.2020.00117","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00117","url":null,"abstract":"This paper presents the ongoing work of studying the iterative program synthesis based on knowledge searched from the Internet, which can fairly reduce the scale of program space and improve the efficiency of synthesis. First, we implement a tool named Args(api Recommendation via General Search) to obtain the API knowledge from the Internet. Second, we propose an iterative method that incrementally constructs the program space to quickly approach the target program. The initial experimental result shows the effectiveness of our work.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134272031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahstract-Points-to analysis is a fundamental, but computationally intensive technique for static program analysis, optimization, debugging and verification. Context-Free Language (CFL) reachability has been proposed and widely used in demand-driven points-to analyses that aims for computing specific points-to relations on demand rather than all variables in the program. However, CFL-reachability-based points-to analysis still faces challenges when applied in practice especially for flow-sensitive points-to analysis, which aims at improving the precision of points-to analysis by taking account of the execution order of program statements. We propose a scalable approach named Parseeker to parallelize flow-sensitive demand-driven points-to analysis via CFL-reachability in order to improve the performance of points-to analysis with high precision. Our core insights are to (1) produce and process a set of fine-grained, parallelizable queries of points-to relations for the objective program, and (2) take a CFL-reachability-based points-to analysis to answer each query. The MapReduce is used to parallelize the queries and three optimization strategies are designed for further enhancing the efficiency.
{"title":"Parallelizing Flow-Sensitive Demand-Driven Points-to Analysis","authors":"Haibo Yu, Qiang Sun, Kejun Xiao, Yuting Chen, Tsunenori Mine, Jianjun Zhao","doi":"10.1109/QRS-C51114.2020.00026","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00026","url":null,"abstract":"Ahstract-Points-to analysis is a fundamental, but computationally intensive technique for static program analysis, optimization, debugging and verification. Context-Free Language (CFL) reachability has been proposed and widely used in demand-driven points-to analyses that aims for computing specific points-to relations on demand rather than all variables in the program. However, CFL-reachability-based points-to analysis still faces challenges when applied in practice especially for flow-sensitive points-to analysis, which aims at improving the precision of points-to analysis by taking account of the execution order of program statements. We propose a scalable approach named Parseeker to parallelize flow-sensitive demand-driven points-to analysis via CFL-reachability in order to improve the performance of points-to analysis with high precision. Our core insights are to (1) produce and process a set of fine-grained, parallelizable queries of points-to relations for the objective program, and (2) take a CFL-reachability-based points-to analysis to answer each query. The MapReduce is used to parallelize the queries and three optimization strategies are designed for further enhancing the efficiency.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115787443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00051
Changbo Hou, Xuejiao Zhao, Yun Lin
It is still challenging to efficiently construct semantic map with a monocular camera. In this paper, deep learning is introduced to combined with SLAM to realize semantic map production. We replace depth estimation module of SLAM with FCN which effectively solves the contradiction of triangulation. The Fc layers of FCN are modified to convolutional layers. Redundant calculation of Fc layers is avoided after optimization, and images can be input in any size. Besides, Faster RCNN, namely, a two-stage object detection network is utilized to obtain semantic information. We fine-tune RPN and Fc layers by transfer learning. The two algorithms are evaluated on official dataset. Results show that the average relative error of depth estimation is reduced by 12.6%, the accuracy of object detection is improved by 10.9%. The feasibility of the combination of deep learning and SLAM is verified.
{"title":"Depth Estimation and Object Detection for Monocular Semantic SLAM Using Deep Convolutional Network","authors":"Changbo Hou, Xuejiao Zhao, Yun Lin","doi":"10.1109/QRS-C51114.2020.00051","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00051","url":null,"abstract":"It is still challenging to efficiently construct semantic map with a monocular camera. In this paper, deep learning is introduced to combined with SLAM to realize semantic map production. We replace depth estimation module of SLAM with FCN which effectively solves the contradiction of triangulation. The Fc layers of FCN are modified to convolutional layers. Redundant calculation of Fc layers is avoided after optimization, and images can be input in any size. Besides, Faster RCNN, namely, a two-stage object detection network is utilized to obtain semantic information. We fine-tune RPN and Fc layers by transfer learning. The two algorithms are evaluated on official dataset. Results show that the average relative error of depth estimation is reduced by 12.6%, the accuracy of object detection is improved by 10.9%. The feasibility of the combination of deep learning and SLAM is verified.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00057
Guozhen Gao, Z. Que, Zhengguo Xu
Recently deep learning based remaining useful life (RUL) prediction approaches have gained increasing attention due to their scalability and generalization ability. Although deep learning based approaches can obtain promising point prediction performance, it is not easy for them to estimate the uncertainty in RUL prediction. In this paper, a recurrent neural process model is proposed to address the prognostics uncertainty problem based on deep learning. Compared with the original neural process model, a recurrent layer is added to extract sequential information from input sliding windows. The RUL prediction problem can be considered as finding a regression function mapping the sliding window input to its corresponding RUL. By obtaining the distribution over the regression functions, the recurrent neural process is able to model the probability distribution of the RUL. As a probabilistic model, stochastic variational inference and reparameterization trick is applied to learn the parameters of the model. The proposed method is validated through the C-MAPSS turbofan engine dataset.
{"title":"Predicting Remaining Useful Life with Uncertainty Using Recurrent Neural Process","authors":"Guozhen Gao, Z. Que, Zhengguo Xu","doi":"10.1109/QRS-C51114.2020.00057","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00057","url":null,"abstract":"Recently deep learning based remaining useful life (RUL) prediction approaches have gained increasing attention due to their scalability and generalization ability. Although deep learning based approaches can obtain promising point prediction performance, it is not easy for them to estimate the uncertainty in RUL prediction. In this paper, a recurrent neural process model is proposed to address the prognostics uncertainty problem based on deep learning. Compared with the original neural process model, a recurrent layer is added to extract sequential information from input sliding windows. The RUL prediction problem can be considered as finding a regression function mapping the sliding window input to its corresponding RUL. By obtaining the distribution over the regression functions, the recurrent neural process is able to model the probability distribution of the RUL. As a probabilistic model, stochastic variational inference and reparameterization trick is applied to learn the parameters of the model. The proposed method is validated through the C-MAPSS turbofan engine dataset.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134252709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00038
Takuto Ohka, Shun Matsumoto, Masatsugu Ichino, H. Yoshiura
Identifying people from anonymous location histories is important for two purposes. i.e. to clarify privacy risks in using the location histories and to find evidence of who went where and when. Although linking with social network accounts is an excellent approach for such identification, previous methods need information about social relationships and have a limitation on the number of target data sets. Moreover, they make limited use of time information. We present models that overcome these problems by estimating the sameness and difference of people by using combinations of time and distance. Our proposed method uses these models along with multi-resolution models for both sides of linking, i.e. location histories and social network accounts. Evaluation using real data demonstrated the effectiveness of our method even when linking only one pseudonymized and obfuscated location history to 1 of 10,000 social network accounts without any information about social relationships.
{"title":"Time-aware multi-resolutional approach to re-identifying location histories by using social networks","authors":"Takuto Ohka, Shun Matsumoto, Masatsugu Ichino, H. Yoshiura","doi":"10.1109/QRS-C51114.2020.00038","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00038","url":null,"abstract":"Identifying people from anonymous location histories is important for two purposes. i.e. to clarify privacy risks in using the location histories and to find evidence of who went where and when. Although linking with social network accounts is an excellent approach for such identification, previous methods need information about social relationships and have a limitation on the number of target data sets. Moreover, they make limited use of time information. We present models that overcome these problems by estimating the sameness and difference of people by using combinations of time and distance. Our proposed method uses these models along with multi-resolution models for both sides of linking, i.e. location histories and social network accounts. Evaluation using real data demonstrated the effectiveness of our method even when linking only one pseudonymized and obfuscated location history to 1 of 10,000 social network accounts without any information about social relationships.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115902094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00122
Sa Meng, Liang Luo, Peng Sun, Yuan Gao
The public cloud is a type of cloud computing offered by third-party providers over the public Internet, making them available to Internet users. The public cloud is featured in large-scale, high complexity, dynamic resource change. However, how to provide secure and reliable cloud services to the widest range of Internet users is a big challenge for the development of cloud computing. Blockchain is a new decentralized distributed computing paradigm. The data stored in the blockchain has the characteristics of unforgeability, whole process trace, traceability, openness and transparency, and collective maintenance. Based on these characteristics, blockchain has laid a solid foundation of trust and created a reliable cooperation mechanism. Applying blockchain technology to the cloud computing platform and improving the service quality of the cloud computing platform by using the blockchain mechanism is a research topic with great application prospects.
{"title":"Reliability Service Assurance in Public Clouds based on Blockchain","authors":"Sa Meng, Liang Luo, Peng Sun, Yuan Gao","doi":"10.1109/QRS-C51114.2020.00122","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00122","url":null,"abstract":"The public cloud is a type of cloud computing offered by third-party providers over the public Internet, making them available to Internet users. The public cloud is featured in large-scale, high complexity, dynamic resource change. However, how to provide secure and reliable cloud services to the widest range of Internet users is a big challenge for the development of cloud computing. Blockchain is a new decentralized distributed computing paradigm. The data stored in the blockchain has the characteristics of unforgeability, whole process trace, traceability, openness and transparency, and collective maintenance. Based on these characteristics, blockchain has laid a solid foundation of trust and created a reliable cooperation mechanism. Applying blockchain technology to the cloud computing platform and improving the service quality of the cloud computing platform by using the blockchain mechanism is a research topic with great application prospects.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134081074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00123
Peng Sun, Liang Luo, Shangxin Liu, Weifeng Wu
Mobile Edge Computing received significant attention in recent years. MEC can effectively reduce the data transmission pressure from end to cloud, while meeting the requirements of low latency and high bandwidth in 5G scenarios, and has wide application prospects in industrial and medical fields. In this paper, we propose to adopt the deployment of computing resources in the telecom operator's C-RAN (Centralized Radio Access Network) to form a landing solution for MEC. At the same time, it is combined with smart street light equipped with 5G base stations to form the IoT front-end of the C-RAN network for data collection. Finally, an adaptive rule engine is used to routinely monitor data and detect data anomalies in a timely manner. The anomaly monitoring solution can meet the rapid response capability to anomalies in 5G communication.
{"title":"Adaptive Rule Engine for Anomaly Detection in 5G Mobile Edge Computing","authors":"Peng Sun, Liang Luo, Shangxin Liu, Weifeng Wu","doi":"10.1109/QRS-C51114.2020.00123","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00123","url":null,"abstract":"Mobile Edge Computing received significant attention in recent years. MEC can effectively reduce the data transmission pressure from end to cloud, while meeting the requirements of low latency and high bandwidth in 5G scenarios, and has wide application prospects in industrial and medical fields. In this paper, we propose to adopt the deployment of computing resources in the telecom operator's C-RAN (Centralized Radio Access Network) to form a landing solution for MEC. At the same time, it is combined with smart street light equipped with 5G base stations to form the IoT front-end of the C-RAN network for data collection. Finally, an adaptive rule engine is used to routinely monitor data and detect data anomalies in a timely manner. The anomaly monitoring solution can meet the rapid response capability to anomalies in 5G communication.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133732915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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