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Modelling the Mimic Defence Technology for Multimedia Cloud Servers.

Authors :
Feng, Feng
Zhou, Xiabing
Li, Bin
Zhou, Qinglei
Source :
Security & Communication Networks; 11/28/2020, p1-22, 22p
Publication Year :
2020

Abstract

A current research trend is to combine multimedia data with artificial intelligence and process them on cloud servers. In this context, ensuring the security of multimedia cloud servers is critical, and the cyber mimic defence (CMD) technology is a promising approach to this end. CMD, which is an innovative active defence technology developed in China, can be applied in many scenarios. However, although the mathematical model is a key component of CMD, a universally acceptable mathematical model for theoretical CMD has not been established yet. In this work, the attack problems and modelling difficulties were extensively examined, and a comprehensive modelling theory and concepts were clarified. By decoupling the model from the input and output of the specific system scene, the modelling difficulties were effectively avoided, and the mathematical expression of the CMD mechanism was enhanced. Furthermore, the process characteristics of the attack behaviour were identified by using a specific mathematical mapping method. Finally, based on the decomposition problem of large prime factors and convolution operations, an intuitive and exclusive CMD mathematical model was proposed. The proposed model could clearly express the CMD mechanism and transform the problems of attack and defence in the CMD domain into corresponding mathematical problems. These aspects were considered to qualitatively assess the CMD security, and it was noted that a high level of security can be realized. Furthermore, the overhead of CMD was analyzed. Moreover, the proposed model can be directly programmed. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19390114
Database :
Complementary Index
Journal :
Security & Communication Networks
Publication Type :
Academic Journal
Accession number :
147265117
Full Text :
https://doi.org/10.1155/2020/8819958