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Security analysis and new models on the intelligent symmetric key encryption

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成果类型:
期刊论文
作者:
Zhou, Lu;Chen, Jiageng*;Zhang, Yidan;Su, Chunhua;James, Marino Anthony
通讯作者:
Chen, Jiageng
作者机构:
[Zhou, Lu; Su, Chunhua] Univ Aizu, Aizu Wakamatsu, Fukushima, Japan.
[Zhang, Yidan; James, Marino Anthony; Chen, Jiageng] Cent China Normal Univ, Wuhan, Hubei, Peoples R China.
通讯机构:
[Chen, Jiageng] C
Cent China Normal Univ, Wuhan, Hubei, Peoples R China.
语种:
英文
关键词:
Generative adversarial network;Neural network;Security models;Statistical analysis;Tensorflow
期刊:
Computers & Security
ISSN:
0167-4048
年:
2019
卷:
80
页码:
14-24
基金类别:
This work has been partly supported by the National Natural Science Foundation of China under Grant No. 61702212 and the research funds of CCNU from colleges basic research and operation of MOE under Grand No. CCNU16A05040.
机构署名:
本校为通讯机构
摘要:
Data protection is achieved in modern cryptography by using encryption. Symmetric key cryptography is mainly responsible for the actual user data protection in various network protocols such as SSL/TLS and so on. The design of such encryption algorithms have always been one of the most important research targets, where heavy cryptanalysis works have been performed to evaluate the security margin. As a result, the research community is busy with fixing the security flaws based on the cryptanalysis results. Recently, the idea of building the auto...

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