Abstract
This study explored the global cyberspace security issues, with the purpose of breaking the stereotype of people’s cognition of cyberspace problems, which reflects the relationship between interdependence and association. Based on the Apriori algorithm in association rules, a total of 181 strong rules were mined from 40 target websites and 56,096 web pages were associated with global cyberspace security. Moreover, this study analyzed support, confidence, promotion, leverage, and reliability to achieve comprehensive coverage of data. A total of 15,661 sites mentioned cyberspace security-related words from the total sample of 22,493 professional websites, accounting for 69.6%, while only 735 sites mentioned cyberspace security-related words from the total sample of 33,603 non-professional sites, accounting for 2%. Due to restrictions of language, the number of samples of target professional websites and non-target websites is limited. Meanwhile, the number of selections of strong rules is not satisfactory. Nowadays, the cores of global cyberspace security issues include internet sovereignty, cyberspace security, cyber attack, cyber crime, data leakage, and data protection.
Highlights
Association rules, reflecting the interdependence and correlation between one thing and others, are one of the critical research methods in the data mining of graphic patterns (Epifania et al, 2020)
This study focused on target websites of global cyberspace security, which were divided into two types, professional and non-professional, to consider the comprehensiveness of data coverage
The limitation of the work lies in the limited number of data mining samples due to different language restrictions for global professional and non-target websites, which leads to an insufficient selection of strong rules
Summary
Association rules, reflecting the interdependence and correlation between one thing and others, are one of the critical research methods in the data mining of graphic patterns (Epifania et al, 2020). The data mining technology of association rules is mostly based on the Apriori algorithm, of which the core optimization lies in finding all the frequent item-sets in the transaction database (Shashi et al, 2020). Based on the association rules of the Apriori algorithm in data mining, global cyberspace security was studied, to seek the focus of current cyberspace issues and to provide a path reference for future cyberspace governance (Johns, 2019). Based on the word segmentation results in the second stage, the Apriori algorithm for data mining of association rules is analyzed in the third stage. This study selected 89 lexicons related to cyberspace security to cover all aspects of global cyberspace security, providing high-quality data for later association rules mining.
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