Design of automatic monitoring system for network information security in cloud computing environment

Author(s):  
Jing NIU
2016 ◽  
Vol 3 (1) ◽  
pp. 42
Author(s):  
Quanhui Ren ◽  
Hui Gao

<span style="color: black; line-height: 115%; font-family: 'Calibri','sans-serif'; font-size: 12pt; mso-fareast-font-family: Calibri; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">In order to adapt to rapid development of network information technology, the application of cloud computing technology is increasingly widespread. However, the security problem in the cloud computing environment has not been effectively resolved. Currently, the research on this problem is getting more attention from the industry. In order to further investigate the information security issues of cloud computing environment, this article not only discusses the basic concept, characteristics and service model of cloud computing, but also focuses on the cloud computing security reference model and cloud cube model. In this paper, the information security problems and concrete solutions in the former cloud computing environment are discussed from different aspects.</span>


2018 ◽  
Vol 24 (1) ◽  
pp. 739-743 ◽  
Author(s):  
Ganthan Narayana Samy ◽  
Bharanidharan Shanmugam ◽  
Nurazean Maarop ◽  
Pritheega Magalingam ◽  
Sundresan Perumal ◽  
...  

2014 ◽  
Vol 513-517 ◽  
pp. 2315-2319
Author(s):  
Qian Wang

The network information in cloud computing environment is in the format of massive data with high dimension features. In order to increase the accuracy of massive data automatic classification, this paper proposes a relevant massive data classification algorithm. This algorithm first transfers the features of the massive data in the cloud computing environment into one multi-objective optimization problem. In the specific mining area, the algorithm associatively classifies the massive data information and the selective standard is the high accuracy. The simulation and experiment test the performance of the algorithm which shows the algorithm can effectively classify the features of the massive data in the cloud computing. The algorithm can increase the accuracy and efficiency of the automatic classification which is an effective method for massive data.


2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Yanqing Chen

At present, many companies have many problems such as high financial costs, low financial management capabilities, and redundant frameworks; at the same time, the SASAC requires that the enterprise’s financial strategy transfer from “profit-driven” to “value-driven”, finance separate from accounting to improve the operational efficiency of the company. Under this background, more and more enterprise respond to the call of the SASAC; in order to achieve the goals of corporate financial cost savings and financial management efficiency improved, we began to provide services through financial sharing. The research of information fusion theory involves many basic theories, which can be roughly divided into two large categories from the algorithmic point of view: probabilistic statistical method and artificial intelligence method. The main task of artificial intelligence is to realize the computer for some learning, thinking process, and wisdom formation of simulation, and an important goal of information integration is the human brain comprehensive processing ability simulation, so artificial intelligence method will have broad application prospects in the field of information fusion; the common methods have D-S evidence reasoning, fuzzy theory, neural network, genetic algorithm, rough set, and other information fusion methods. The purpose of this paper is to proceed from the internal financial situation of the enterprise, analyze data security issues in the operation of financial shared services, and find a breakthrough in solving problems. But, with constantly expanding of enterprise group financial sharing service scale, the urgent problem to be solved is how to ensure the financial sharing services provided by enterprises in the cloud computing environment. This paper combines financial sharing service theory and information security theory and provides reference for building financial sharing information security for similar enterprises. For some enterprise that have not established a financial shared service center yet, they can learn from the establishment of the financial sharing information security system in this paper and provide a reference for enterprise to avoid the same types of risks and problems. For enterprise that has established and has begun to practice a financial shared information security system, appropriate risk aversion measures combined with actual situation of the enterprise with four dimensions related to information security system optimization was formulated and described in this paper. In summary, in the background of cloud computing, financial sharing services have highly simplified operational applications, and data storage capabilities and computational analysis capabilities have been improved greatly. Not only can it improve the quality of accounting information but also provide technical support for the financial sharing service center of the enterprise group, perform financial functions better, and enhance decision support and strategic driving force, with dual practical significance and theoretical significance.


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