An Efficient Data Analysis Framework for Online Security Processing
Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the pr...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2021/9290853 |
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| _version_ | 1832563286229385216 |
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| author | Jun Li Yanzhao Liu |
| author_facet | Jun Li Yanzhao Liu |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. LCN-Index uses the MapReduce computing paradigm to deploy large scale online data analysis rules: in the mapping stage, it divides each attribute into a batch of analysis predicate sets which are then deployed onto a mapping node using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multiattribute hash index. By doing so, a stream tuple can be efficiently evaluated by going over the LCN-Index framework. Experiments demonstrate the utility of the proposed method. |
| format | Article |
| id | doaj-art-48b6088cc607460ca1a871b3e50ecdbe |
| institution | Kabale University |
| issn | 2090-7141 2090-715X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-48b6088cc607460ca1a871b3e50ecdbe2025-02-03T01:20:32ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2021-01-01202110.1155/2021/92908539290853An Efficient Data Analysis Framework for Online Security ProcessingJun Li0Yanzhao Liu1Information Security Department, Beijing Information Science and Technology University, Beijing 100192, ChinaChina Information Technology Security Evaluation Center, Beijing, ChinaIndustrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. LCN-Index uses the MapReduce computing paradigm to deploy large scale online data analysis rules: in the mapping stage, it divides each attribute into a batch of analysis predicate sets which are then deployed onto a mapping node using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multiattribute hash index. By doing so, a stream tuple can be efficiently evaluated by going over the LCN-Index framework. Experiments demonstrate the utility of the proposed method.http://dx.doi.org/10.1155/2021/9290853 |
| spellingShingle | Jun Li Yanzhao Liu An Efficient Data Analysis Framework for Online Security Processing Journal of Computer Networks and Communications |
| title | An Efficient Data Analysis Framework for Online Security Processing |
| title_full | An Efficient Data Analysis Framework for Online Security Processing |
| title_fullStr | An Efficient Data Analysis Framework for Online Security Processing |
| title_full_unstemmed | An Efficient Data Analysis Framework for Online Security Processing |
| title_short | An Efficient Data Analysis Framework for Online Security Processing |
| title_sort | efficient data analysis framework for online security processing |
| url | http://dx.doi.org/10.1155/2021/9290853 |
| work_keys_str_mv | AT junli anefficientdataanalysisframeworkforonlinesecurityprocessing AT yanzhaoliu anefficientdataanalysisframeworkforonlinesecurityprocessing AT junli efficientdataanalysisframeworkforonlinesecurityprocessing AT yanzhaoliu efficientdataanalysisframeworkforonlinesecurityprocessing |