摘要
加密算法的识别对于密码分析研究有着重要的意义,目前学者们已经在此领域展开了一些研究并取得了一定的进展。然而在针对哈希函数的识别方面,所展开的理论研究较少。本文对随机性检测特征进一步挖掘,利用欧氏距离筛选出对哈希函数最有区分度的3个检测项,基于选出的检测项的核心关注点重新构建特征生成方法,并结合随机森林模型,提出了一种基于组合随机性特征的哈希函数识别方案。通过实验分析,该识别方案明显优于传统的基于随机性检测特征的识别方案。
The recognition of encryption algorithms is of great significance to the research of cryptographic analysis.At present,scholars have carried out some research and made some progress in this field.However,there are few theoretical studies on Hash function recognition.In this paper,the randomness detection features are further mined.The Euclidean distance is used to screen out the three detection items that have the most distinguishing degree to the Hash function.Based on the core concerns of the selected detection items,the feature generation method is reconstructed.Combined with the random forest model,a Hash function recognition scheme based on the combined randomness features is proposed.Through experimental analysis,the recognition scheme is obviously superior to the traditional recognition scheme based on random detection features.
作者
王徐来
向广利
李蓓蕾
李祯鹏
张涛
WANG Xulai;XIANG Guangli;LI Beilei;LI Zhenpeng;ZHANG Tao(School of Computer Science and Artificial Intelligence,Wuhan University of Technology,Wuhan 430070,Hubei,China;China Construction Third Engineering Bureau Installation Engineering Co.,Ltd.(Intelligent Business Department of China Construction Third Engineering Bureau),Wuhan 430060,Hubei,China)
出处
《武汉大学学报（理学版）》
CAS
CSCD
北大核心
2023年第2期215-222,共8页
Journal of Wuhan University:Natural Science Edition
基金
湖北省重点新产品计划(2021BAA030)
关键词
密码分析
哈希函数
特征提取
随机性检测
cryptanalysis
Hash function
feature extraction
randomness test
作者简介
王徐来,男,硕士生,现从事信息安全研究。E-mail:409852467@qq.com;通信联系人:向广利,E-mail:glxiang@whut.edu.cn
