Pub Date : 2022-10-01DOI: 10.1109/SmartCloud55982.2022.00031
Xiaoqi Zhang, Guangsong Li, Yongjuan Wang
Since its inception, blockchain technology attracts great attention from the industry and academia. With its development, cryptocurrencies such as bitcoin based on blockchain technology gradually emerge and enter the financial field. Meanwhile, malicious behaviors aimed at bitcoin become more and more common and cause huge damage to cryptocurrency users and the evolution of blockchain technology, which prompt researchers to establish various models to deal with this problem. In this paper, we collected the historical bitcoin transaction dataset and extracted features from it. After standardizing features, we used an unsupervised learning model based on Generative Adversarial Networks (GAN) to detect dataset containing more than 30 million normal and 108 malicious samples and reached a precision of 23% and recall value close to 100%.
{"title":"GAN-based Abnormal Transaction Detection in Bitcoin","authors":"Xiaoqi Zhang, Guangsong Li, Yongjuan Wang","doi":"10.1109/SmartCloud55982.2022.00031","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00031","url":null,"abstract":"Since its inception, blockchain technology attracts great attention from the industry and academia. With its development, cryptocurrencies such as bitcoin based on blockchain technology gradually emerge and enter the financial field. Meanwhile, malicious behaviors aimed at bitcoin become more and more common and cause huge damage to cryptocurrency users and the evolution of blockchain technology, which prompt researchers to establish various models to deal with this problem. In this paper, we collected the historical bitcoin transaction dataset and extracted features from it. After standardizing features, we used an unsupervised learning model based on Generative Adversarial Networks (GAN) to detect dataset containing more than 30 million normal and 108 malicious samples and reached a precision of 23% and recall value close to 100%.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"26 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":"125005368","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/SmartCloud55982.2022.00032
Yuqi He, Zhiquan Lai, Zhejiang Ran, Lizhi Zhang, Dongsheng Li
The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices’ neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines.
{"title":"Accelerating Sample-based GNN Training by Feature Caching on GPUs","authors":"Yuqi He, Zhiquan Lai, Zhejiang Ran, Lizhi Zhang, Dongsheng Li","doi":"10.1109/SmartCloud55982.2022.00032","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00032","url":null,"abstract":"The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices’ neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"12 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":"130028499","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/SmartCloud55982.2022.00022
Zhu Li, Xia Yu
Load clustering is the early basis of power grid system planning, load modeling, demand side management, load forecasting and other work. The traditional load classification method based on user types can not meet the needs of power grid services. Iterative Self-Organizing Data Analysis Algorithm (ISODATA) is an unsupervised learning dynamic clustering algorithm based on statistical pattern recognition. In view of the current problems that the initial clustering number of each algorithm is difficult to take and easy to fall into local optimum, the principle and implementation steps of ISODATA are introduced, and this algorithm is applied to the power load curve clustering. The clustering analysis is combined with specific power load curve samples, and the results prove that the clustering effect is better and the time improvement is larger. ISODATA is compared with the traditional clustering method to compare the clustering effect and the time loss of the algorithm. The results of the comparison experiments show that ISODATA has good clustering effect when applied to power load curve clustering.Isodata-based clustering of power load curves can fine distinguish users and provide decision support and scientific basis for the reliable operation of power system.
{"title":"Power Load Curve Clustering based on ISODATA","authors":"Zhu Li, Xia Yu","doi":"10.1109/SmartCloud55982.2022.00022","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00022","url":null,"abstract":"Load clustering is the early basis of power grid system planning, load modeling, demand side management, load forecasting and other work. The traditional load classification method based on user types can not meet the needs of power grid services. Iterative Self-Organizing Data Analysis Algorithm (ISODATA) is an unsupervised learning dynamic clustering algorithm based on statistical pattern recognition. In view of the current problems that the initial clustering number of each algorithm is difficult to take and easy to fall into local optimum, the principle and implementation steps of ISODATA are introduced, and this algorithm is applied to the power load curve clustering. The clustering analysis is combined with specific power load curve samples, and the results prove that the clustering effect is better and the time improvement is larger. ISODATA is compared with the traditional clustering method to compare the clustering effect and the time loss of the algorithm. The results of the comparison experiments show that ISODATA has good clustering effect when applied to power load curve clustering.Isodata-based clustering of power load curves can fine distinguish users and provide decision support and scientific basis for the reliable operation of power system.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"5 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":"132514275","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}
There are many kinds of energy data, how to realize unified storage, processing and sharing of energy data is a big problem. As the national energy data center, State Grid aims to build a database that can store distributed heterogeneous asynchronous energy data. The storage of image files in the big energy database will take up a lot of space in the system, but not all parts of the image are needed. Therefore, it is very necessary to accurately segment the effective area of the image to store it so as to achieve the purpose of data compression. This paper proposes the Attention U-Net framework, which combines the traditional semantic segmentation network U-Net with the Attention module to focus on the region of interest in the image, emphasize foreground information, and suppress background information. The results show that compared with U-Net, the accuracy is improved by 1.77% and after the segmentation is completed, each image saves an average of 2MB of storage space.
{"title":"A Semantic Segmentation Algorithm for Distributed Energy Data Storage Optimization based on Neural Networks","authors":"Dong Mao, Zhongxu Li, Zuge Chen, Hanyu Rao, Jiuding Zhang, Zehan Liu","doi":"10.1109/SmartCloud55982.2022.00024","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00024","url":null,"abstract":"There are many kinds of energy data, how to realize unified storage, processing and sharing of energy data is a big problem. As the national energy data center, State Grid aims to build a database that can store distributed heterogeneous asynchronous energy data. The storage of image files in the big energy database will take up a lot of space in the system, but not all parts of the image are needed. Therefore, it is very necessary to accurately segment the effective area of the image to store it so as to achieve the purpose of data compression. This paper proposes the Attention U-Net framework, which combines the traditional semantic segmentation network U-Net with the Attention module to focus on the region of interest in the image, emphasize foreground information, and suppress background information. The results show that compared with U-Net, the accuracy is improved by 1.77% and after the segmentation is completed, each image saves an average of 2MB of storage space.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"1 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":"116797978","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/SmartCloud55982.2022.00037
Michael S. MacFadden, Meikang Qiu
The HTML5 Web Storage API provides the ability for web applications to store data on client machines. This storage is commonly used for caching, local state tracking, and offline support that allows web applications to work when the web server cannot be contacted. The HTML5 Web Storage is becoming increasingly popular with the majority of new web applications using at least some features provided by this standard. Unfortunately, the local storage provided by HTML5 Web Storage is not entirely secure and does not sufficiently ensure the confidentiality of the user’s data. Encrypting data prior to storage is a common approach to protecting local user data. However, as browser-based applications become more complex and demanding the impact of data encryption may adversely impact application performance. Furthermore, the average web developer is generally not proficient in cryptographic best practices in web applications. First, we provide a simple design approach for encryption of local storage that supports offline web applications. Second, we analyze the impact of various symmetric encryption algorithms and implementations on the performance of the HTML Web Storage API. We show that there are several viable options that will increase the confidentiality and privacy of user data within local storage without imposing significant performance penalties.
HTML5 Web Storage API为Web应用程序提供了在客户机上存储数据的能力。这种存储通常用于缓存、本地状态跟踪和离线支持，允许web应用程序在无法联系web服务器时工作。HTML5 Web Storage正变得越来越流行，因为大多数新的Web应用程序至少使用了这个标准提供的一些特性。不幸的是，HTML5 Web storage提供的本地存储并不完全安全，不能充分保证用户数据的机密性。在存储之前对数据进行加密是保护本地用户数据的常用方法。然而，随着基于浏览器的应用程序变得越来越复杂和苛刻，数据加密的影响可能会对应用程序的性能产生不利影响。此外，一般的web开发人员通常并不精通web应用程序中的加密最佳实践。首先，我们为支持离线web应用程序的本地存储加密提供了一种简单的设计方法。其次，我们分析了各种对称加密算法和实现对HTML Web存储API性能的影响。我们展示了几个可行的选项，可以在不造成显著性能损失的情况下增加本地存储中用户数据的机密性和隐私性。
{"title":"Performance Impacts of JavaScript-Based Encryption of HTML5 Web Storage for Enhanced Privacy","authors":"Michael S. MacFadden, Meikang Qiu","doi":"10.1109/SmartCloud55982.2022.00037","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00037","url":null,"abstract":"The HTML5 Web Storage API provides the ability for web applications to store data on client machines. This storage is commonly used for caching, local state tracking, and offline support that allows web applications to work when the web server cannot be contacted. The HTML5 Web Storage is becoming increasingly popular with the majority of new web applications using at least some features provided by this standard. Unfortunately, the local storage provided by HTML5 Web Storage is not entirely secure and does not sufficiently ensure the confidentiality of the user’s data. Encrypting data prior to storage is a common approach to protecting local user data. However, as browser-based applications become more complex and demanding the impact of data encryption may adversely impact application performance. Furthermore, the average web developer is generally not proficient in cryptographic best practices in web applications. First, we provide a simple design approach for encryption of local storage that supports offline web applications. Second, we analyze the impact of various symmetric encryption algorithms and implementations on the performance of the HTML Web Storage API. We show that there are several viable options that will increase the confidentiality and privacy of user data within local storage without imposing significant performance penalties.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"21 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":"114854855","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/SmartCloud55982.2022.00023
Yueying Zhou, Xinping Ren, Xiaoying Zheng, Yongxin Zhu, Kang Xu, Shijin Song, Li Tian
Hepatic fibrosis is an important prognostic factor as severe liver fibrosis may lead to liver cancer or even death. To grade liver fibrosis, ultrasound gray-scale images and ultrasound elastic images are commonly used in clinical diagnosis to judge the severity of liver fibrosis. However, these two diagnoses methods are often vulnerable to disturbances, such as personal experience or instrument differences. Moreover, these individual differences usually lead to conflicting stand-alone machine learning diagnosis models at each hospital whose medical data are not allowed to share in public due to data privacy. To handle the conflicts among diagnosis models, we propose a federated learning based hierarchical diagnosis method of liver fibrosis by utilizing shear wave elasticity pictures of multiple users across hospitals without sharing the original data. Our method is validated with authentic shear wave elasticity pictures of hepatic fibrosis patients in Shanghai, China. Experimental results show that our method is able to preprocess these shear wave elasticity pictures, train local diagnosis models at each hospital and securely consolidate into a shared global diagnosis model whose accuracy is over 70% with only a small dataset containing a few hundreds of labeled pictures. Our method is expected to further improve in its accuracy with more training samples. Our method would be the first practice based on federated learning in liver fibrosis diagnosis.
{"title":"Federated-Learning-based Hierarchical Diagnosis of Liver Fibrosis","authors":"Yueying Zhou, Xinping Ren, Xiaoying Zheng, Yongxin Zhu, Kang Xu, Shijin Song, Li Tian","doi":"10.1109/SmartCloud55982.2022.00023","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00023","url":null,"abstract":"Hepatic fibrosis is an important prognostic factor as severe liver fibrosis may lead to liver cancer or even death. To grade liver fibrosis, ultrasound gray-scale images and ultrasound elastic images are commonly used in clinical diagnosis to judge the severity of liver fibrosis. However, these two diagnoses methods are often vulnerable to disturbances, such as personal experience or instrument differences. Moreover, these individual differences usually lead to conflicting stand-alone machine learning diagnosis models at each hospital whose medical data are not allowed to share in public due to data privacy. To handle the conflicts among diagnosis models, we propose a federated learning based hierarchical diagnosis method of liver fibrosis by utilizing shear wave elasticity pictures of multiple users across hospitals without sharing the original data. Our method is validated with authentic shear wave elasticity pictures of hepatic fibrosis patients in Shanghai, China. Experimental results show that our method is able to preprocess these shear wave elasticity pictures, train local diagnosis models at each hospital and securely consolidate into a shared global diagnosis model whose accuracy is over 70% with only a small dataset containing a few hundreds of labeled pictures. Our method is expected to further improve in its accuracy with more training samples. Our method would be the first practice based on federated learning in liver fibrosis diagnosis.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"53 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":"129417407","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/SmartCloud55982.2022.00026
Li Zhu, Bin Liu
When predicting the total power load of many users, the computing resources often can’t keep up with the growth rate of the total amount of data, and it is difficult to analyze effectively the data in the actual environment. This paper firstly considers clustering users, then predicts each cluster separately, and finally summarizes the results of each cluster to get the result. This paper firstly performs PCA dimension reduction on user data, and then uses the adaptive K-Means clustering method to determine the number of clusters and the initial cluster center, and then uses the determined parameters to cluster the users, and then builds a model for each cluster user and sum up the forecast results to get the total power load. In order to illustrate the effect of this method under different models, this paper establishes XGBoost, CatBoost and LightGBM models respectively and predicts the total power load of all users. From the experimental results, it can be seen that this method is consistent with the actual data trend, and the prediction effect is better than that of directly modeling all user data.
{"title":"Prediction of User Electricity Consumption based on Adaptive K-Means Algorithm","authors":"Li Zhu, Bin Liu","doi":"10.1109/SmartCloud55982.2022.00026","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00026","url":null,"abstract":"When predicting the total power load of many users, the computing resources often can’t keep up with the growth rate of the total amount of data, and it is difficult to analyze effectively the data in the actual environment. This paper firstly considers clustering users, then predicts each cluster separately, and finally summarizes the results of each cluster to get the result. This paper firstly performs PCA dimension reduction on user data, and then uses the adaptive K-Means clustering method to determine the number of clusters and the initial cluster center, and then uses the determined parameters to cluster the users, and then builds a model for each cluster user and sum up the forecast results to get the total power load. In order to illustrate the effect of this method under different models, this paper establishes XGBoost, CatBoost and LightGBM models respectively and predicts the total power load of all users. From the experimental results, it can be seen that this method is consistent with the actual data trend, and the prediction effect is better than that of directly modeling all user data.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"11 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":"126351419","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/SmartCloud55982.2022.00042
Yuanhao Sun, Cheng Lv, Xi Liu, Tianyang Lei, Zhuoyi Guo, Ning Li, Hongshan Yang
Big data technology is rapidly changing IT industry. Among those technologies, Hadoop is the best-known one and keeps growing its popularity. Transwarp Data Hub (shortened to TDH) is an enterprise-level big data platform developed by Transwarp Technology (Shanghai) Co., Ltd. The last five years have witnessed rapid development in its growth, and it has gained experience from the deployment and implementation in the fields such as postal service, transportation, and finance. Moreover, the company has been engaged in the exploration of the newborn big data technology. Transwarp Data Hub provides five major products: Analytical Database (Transwarp Inceptor and Transwarp ArgoDB), Real-time Streaming Engine (Transwarp Slipstream), Knowledge Database (Transwarp Search and Transwarp StellarDB), Operational Database (Transwarp Hyperbase), and Data Science Platform (Transwarp Discover). Enterprises can leverage data to build core business systems more effectively and accelerate business innovation by deploying, installing, and using TDH.
大数据技术正在迅速改变IT行业。在这些技术中，Hadoop是最著名的一种，而且它的受欢迎程度还在不断增长。Transwarp Data Hub(简称TDH)是由Transwarp Technology (Shanghai) Co. Ltd.开发的企业级大数据平台。近五年来，该系统发展迅速，在邮政、交通、金融等领域的部署和实施中积累了丰富的经验。此外，公司一直致力于新兴的大数据技术的探索。Transwarp Data Hub提供五大产品:分析数据库(Transwarp interceptor和Transwarp ArgoDB)、实时流引擎(Transwarp Slipstream)、知识数据库(Transwarp Search和Transwarp starardb)、操作数据库(Transwarp Hyperbase)和数据科学平台(Transwarp Discover)。企业可以通过部署、安装和使用TDH来更有效地利用数据构建核心业务系统，并加速业务创新。
{"title":"TDH: An Efficient One-stop Enterprise-level Big Data Platform","authors":"Yuanhao Sun, Cheng Lv, Xi Liu, Tianyang Lei, Zhuoyi Guo, Ning Li, Hongshan Yang","doi":"10.1109/SmartCloud55982.2022.00042","DOIUrl":"https://doi.org/10.1109/SmartCloud55982.2022.00042","url":null,"abstract":"Big data technology is rapidly changing IT industry. Among those technologies, Hadoop is the best-known one and keeps growing its popularity. Transwarp Data Hub (shortened to TDH) is an enterprise-level big data platform developed by Transwarp Technology (Shanghai) Co., Ltd. The last five years have witnessed rapid development in its growth, and it has gained experience from the deployment and implementation in the fields such as postal service, transportation, and finance. Moreover, the company has been engaged in the exploration of the newborn big data technology. Transwarp Data Hub provides five major products: Analytical Database (Transwarp Inceptor and Transwarp ArgoDB), Real-time Streaming Engine (Transwarp Slipstream), Knowledge Database (Transwarp Search and Transwarp StellarDB), Operational Database (Transwarp Hyperbase), and Data Science Platform (Transwarp Discover). Enterprises can leverage data to build core business systems more effectively and accelerate business innovation by deploying, installing, and using TDH.","PeriodicalId":104366,"journal":{"name":"2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)","volume":"86 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":"115215284","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/smartcloud55982.2022.00010
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}
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