Pub Date : 2022-10-01DOI: 10.1109/smartcloud55982.2022.00010
Geetika Tiwari, Ruchi Jain
Cloud computing has been promoted as one of the most effective methods of hosting and delivering services via the internet. Despite its broad range of applications, cloud security remains a serious worry for cloud computing. Many secure solutions have been developed to safeguard communication in such environments, the majority of which are based on attack signatures. These systems are often ineffective in detecting all forms of threats. A machine learning approach was recently presented. This implies that if the training set lacks sufficient instances in a specific class, the judgment may be incorrect. In this research, we present a novel firewall mechanism for safe cloud computing environments called machine learning and deep learning system. Proposed Methods identifies and classifies incoming traffic packets using a novel combination methodology named most frequent decision, in which the nodes’ one previous decisions are coupled with the machine learning algorithm’s current decision to estimate the final attack category classification. This method improves learning performance as well as system correctness. UNSW-NB-15, a publicly accessible dataset, is utilized to derive our findings. Our data demonstrate that it enhances anomaly detection by 97.68 percent.
{"title":"Detecting and Classifying Incoming Traffic in a Secure Cloud Computing Environment Using Machine Learning and Deep Learning System","authors":"Geetika Tiwari, Ruchi Jain","doi":"10.1109/smartcloud55982.2022.00010","DOIUrl":"https://doi.org/10.1109/smartcloud55982.2022.00010","url":null,"abstract":"Cloud computing has been promoted as one of the most effective methods of hosting and delivering services via the internet. Despite its broad range of applications, cloud security remains a serious worry for cloud computing. Many secure solutions have been developed to safeguard communication in such environments, the majority of which are based on attack signatures. These systems are often ineffective in detecting all forms of threats. A machine learning approach was recently presented. This implies that if the training set lacks sufficient instances in a specific class, the judgment may be incorrect. In this research, we present a novel firewall mechanism for safe cloud computing environments called machine learning and deep learning system. Proposed Methods identifies and classifies incoming traffic packets using a novel combination methodology named most frequent decision, in which the nodes’ one previous decisions are coupled with the machine learning algorithm’s current decision to estimate the final attack category classification. This method improves learning performance as well as system correctness. UNSW-NB-15, a publicly accessible dataset, is utilized to derive our findings. Our data demonstrate that it enhances anomaly detection by 97.68 percent.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127806675","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 : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00027
Shufeng He, Dianqi Sun
This paper focuses on the intuitive, three-dimensional and convenient marine geological data service requirements of various applications. Based on the accumulation of 3D seabed visual modeling technology in the past, this paper realizes the uneven columnar sampling geological data processing, the rapid optimization processing of gravity and magnetic data, the extraction of key features of marine data field and the optimization of visual display can quickly and intuitively meet the service requirements for marine geological and geophysical data products, to realize related data analysis and simulation.
{"title":"Research on 3D Product Service System Based on Spherical Model","authors":"Shufeng He, Dianqi Sun","doi":"10.1109/SmartCloud55982.2022.00027","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00027","url":null,"abstract":"This paper focuses on the intuitive, three-dimensional and convenient marine geological data service requirements of various applications. Based on the accumulation of 3D seabed visual modeling technology in the past, this paper realizes the uneven columnar sampling geological data processing, the rapid optimization processing of gravity and magnetic data, the extraction of key features of marine data field and the optimization of visual display can quickly and intuitively meet the service requirements for marine geological and geophysical data products, to realize related data analysis and simulation.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124094974","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 : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00019
Peng Nie, Zhenwei Yang, Ziyuan Zhang
With the rapid rise of Internet of vehicles applications, a large number of time delay sensitive tasks, such as autonomous driving and virtual reality, have emerged. These tasks require the mobile terminal to have a lower transmission delay to the server. Offloading tasks to adjacent edge servers is an effective way to reduce latency, and it is also a common deployment constraint. How to optimize the allocation of edge computing resources under this constraint is a major challenge. This paper proposes a truthful dynamic online double auction mechanism, different from the traditional double auction mechanism, this paper considers multiple heterogeneous edge server nodes, each server node acts as an independent service provider, and also considers the deployment constraints of vehicles on different edge servers, that is, vehicle users only offload tasks to adjacent edge servers, and in the execution time of the task, it needs to maintain a continuous connection with the server. Then, according to the supply-demand relationship of the market, a monotonic approximate algorithm is designed to determine the winner in polynomial time. In terms of pricing, a critical-valuebased pricing strategy is proposed. Simulation results verify the effectiveness of the mechanism.
{"title":"Dynamic Online Double Auction Mechanism based on Deployment Constraints in the Internet of Vehicles","authors":"Peng Nie, Zhenwei Yang, Ziyuan Zhang","doi":"10.1109/SmartCloud55982.2022.00019","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00019","url":null,"abstract":"With the rapid rise of Internet of vehicles applications, a large number of time delay sensitive tasks, such as autonomous driving and virtual reality, have emerged. These tasks require the mobile terminal to have a lower transmission delay to the server. Offloading tasks to adjacent edge servers is an effective way to reduce latency, and it is also a common deployment constraint. How to optimize the allocation of edge computing resources under this constraint is a major challenge. This paper proposes a truthful dynamic online double auction mechanism, different from the traditional double auction mechanism, this paper considers multiple heterogeneous edge server nodes, each server node acts as an independent service provider, and also considers the deployment constraints of vehicles on different edge servers, that is, vehicle users only offload tasks to adjacent edge servers, and in the execution time of the task, it needs to maintain a continuous connection with the server. Then, according to the supply-demand relationship of the market, a monotonic approximate algorithm is designed to determine the winner in polynomial time. In terms of pricing, a critical-valuebased pricing strategy is proposed. Simulation results verify the effectiveness of the mechanism.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"260 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131519993","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 : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00038
B. Thuraisingham
This paper first describes the security and privacy challenges for the Internet of Things IoT) systems and then discusses some of the solutions that have been proposed. It also describes aspects of Trustworthy Machine Learning (TML) and then discusses how TML may be applied to handle some of the security and privacy challenges for IoT systems.
{"title":"Trustworthy Machine Learning for Securing IoT Systems","authors":"B. Thuraisingham","doi":"10.1109/SmartCloud55982.2022.00038","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00038","url":null,"abstract":"This paper first describes the security and privacy challenges for the Internet of Things IoT) systems and then discusses some of the solutions that have been proposed. It also describes aspects of Trustworthy Machine Learning (TML) and then discusses how TML may be applied to handle some of the security and privacy challenges for IoT systems.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117019343","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}
With the continuous accumulation of large-scale power grid data, the traditional centralized data analysis method is more and more expensive for data transmission. Based on this, we designed a grid big data monitoring and analysis system and transferred the computation process to the edge node close to the data source through an edge computing strategy. On the one hand, data processing and data analysis algorithms are encapsulated by container technology, and the algorithm is mirrored to the edge nodes of the power network through the system to complete the computation. On the other hand, the computing clusters are deployed at the edge nodes of the power network, which is responsible for the scheduling, execution, and status monitoring of computing tasks. Computing tasks can be flexibly managed in a cluster by extending user-defined resources. Through the reserved parameters, users can intervene in task execution policies, and tasks can be configured. The edge node sends the calculation result or early warning information to the central monitoring service through the asynchronous message. Compared with the traditional centralized data analysis system, the proposed method relieves the problem of the overhead of massive data transmission in the network, reduces the application cost, helps to apply the data analysis to more edge side nodes, and fully excavates the potential value of grid data.
{"title":"Power Grid Data Monitoring and Analysis System based on Edge Computing","authors":"Tianyou Wang, Yuanze Qin, Yu Huang, Yiwei Lou, Chongyou Xu, Lei Chen","doi":"10.1109/SmartCloud55982.2022.00012","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00012","url":null,"abstract":"With the continuous accumulation of large-scale power grid data, the traditional centralized data analysis method is more and more expensive for data transmission. Based on this, we designed a grid big data monitoring and analysis system and transferred the computation process to the edge node close to the data source through an edge computing strategy. On the one hand, data processing and data analysis algorithms are encapsulated by container technology, and the algorithm is mirrored to the edge nodes of the power network through the system to complete the computation. On the other hand, the computing clusters are deployed at the edge nodes of the power network, which is responsible for the scheduling, execution, and status monitoring of computing tasks. Computing tasks can be flexibly managed in a cluster by extending user-defined resources. Through the reserved parameters, users can intervene in task execution policies, and tasks can be configured. The edge node sends the calculation result or early warning information to the central monitoring service through the asynchronous message. Compared with the traditional centralized data analysis system, the proposed method relieves the problem of the overhead of massive data transmission in the network, reduces the application cost, helps to apply the data analysis to more edge side nodes, and fully excavates the potential value of grid data.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127209222","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 : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00013
Zhenwei Yang, Ziyuan Zhang, Peng Nie
The vehicular edge computing technology extends the Internet of Vehicles(IoV) from cloud computing to edge computing, enabling IoV to support in-vehicle applications such as autonomous driving, high-definition video, and navigation planning with low latency and low bandwidth consumption costs. Due to the high deployment cost and maintenance cost of edge computing nodes, to improve the revenue of service providers and encourage edge computing service providers to deploy computing nodes, it is necessary to design an incentive mechanism for edge computing service providers. Auctions are an effective incentive design solution. This paper designs an optimal auction mechanism to maximize the revenue of edge computing service providers, which ensures the two important attributes of individual rationality and incentive compatibility and ensures the feasibility of allocation and efficient use of resources. Specifically, we designed a system model for pricing and allocating edge computing service providers in the Internet of Vehicles environment, and transformed the optimal auction problem of resources under the Internet of Vehicles into a mathematical programming model of the optimal auction with constraints. And designed a matching algorithm, allocation algorithm, and price calculation algorithm based on a neural network. Finally, we experiment and analyze the algorithm. The simulation results show that the proposed scheme is superior to the VCG algorithm in terms of revenue and resource utilization.
{"title":"A Deep-Learning-Based Optimal Auction for Vehicular Edge Computing Resource Allocation","authors":"Zhenwei Yang, Ziyuan Zhang, Peng Nie","doi":"10.1109/SmartCloud55982.2022.00013","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00013","url":null,"abstract":"The vehicular edge computing technology extends the Internet of Vehicles(IoV) from cloud computing to edge computing, enabling IoV to support in-vehicle applications such as autonomous driving, high-definition video, and navigation planning with low latency and low bandwidth consumption costs. Due to the high deployment cost and maintenance cost of edge computing nodes, to improve the revenue of service providers and encourage edge computing service providers to deploy computing nodes, it is necessary to design an incentive mechanism for edge computing service providers. Auctions are an effective incentive design solution. This paper designs an optimal auction mechanism to maximize the revenue of edge computing service providers, which ensures the two important attributes of individual rationality and incentive compatibility and ensures the feasibility of allocation and efficient use of resources. Specifically, we designed a system model for pricing and allocating edge computing service providers in the Internet of Vehicles environment, and transformed the optimal auction problem of resources under the Internet of Vehicles into a mathematical programming model of the optimal auction with constraints. And designed a matching algorithm, allocation algorithm, and price calculation algorithm based on a neural network. Finally, we experiment and analyze the algorithm. The simulation results show that the proposed scheme is superior to the VCG algorithm in terms of revenue and resource utilization.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115123055","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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