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http://dx.doi.org/10.33778/kcsa.2022.22.1.067

Topic Modeling to Identify Cloud Security Trends using news Data Before and After the COVID-19 Pandemic  

Soun U Lee (중앙대학교/융합보안학과)
Jaewoo Lee (중앙대학교/산업보안학과)
Publication Information
Convergence Security Journal / v.22, no.2, 2022 , pp. 67-75 More about this Journal
Abstract
Due to the COVID-19 pandemic, many companies have introduced remote work. However, the introduction of remote work has increased attacks on companies to access sensitive information, and many companies have begun to use cloud services to respond to security threats. This study used LDA topic modeling techniques by collecting news data with the keyword 'cloud security' to analyze changes in domestic cloud security trends before and after the COVID-19 pandemic. Before the COVID-19 pandemic, interest in domestic cloud security was low, so representation or association could not be found in the extracted topics. However, it was analyzed that the introduction of cloud is necessary for high computing performance for AI, IoT, and blockchain, which are IT technologies that are currently being studied. On the other hand, looking at topics extracted after the COVID-19 pandemic, it was confirmed that interest in the cloud increased in Korea, and accordingly, interest in cloud security improved. Therefore, security measures should be established to prepare for the ever-increasing usage of cloud services.
Keywords
Cloud security; LDA Topic Modeling; Big data; Trend Analysis;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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