English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

At present, most of the security assessment works of the information system are based on the single imformation system, and its security status is evaluated according to each attribute hierarchy. There are few methods to evaluate the security level of information system for system group. According to the security requirements of information sys­ tem, a security level evaluation method based on system group was proposed. Taking into account the grey clustering a­ nalysis method of grey system theory, some evaluation objects are divided into several grades by using the whitening weight function, which is suitable for the system analysis of complex structure,high uncertainty and lack of effective in­ formation, In this paper, we used the whitening weight function to determine the level of security evaluation of informa­ tion system. Firstly, from the point of view of the information system, a fast and efficient K-mean clustering algorithm is used to determine the whitenization weight function. Secondly, the grey level grey clustering coefficient of each informa­ tion system is obtained by using the whitening weight function. Finally, we can get the security level of each information system. The results show that this method can effectively evaluate the security level of information systems group.

基于灰色聚类的系统群安全评估方法 (Method of Safety Evaluation for System Group Based on Grey Clustering).