基于FBR特征的密码算法识别  

RECOGNITION OF CRYPTOGRAPHIC ALGORITHMS BASED ON FBR FEATURE

作　　者：向广利[1] 蒋欣 张于洁 杨立新[2] Xiang Guangli;Jiang Xin;Zhang Yujie;Yang Lixin(School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,Hubei,China;Hubei Academy of Scientific and Technical Information,Wuhan 430071,Hubei,China)

机构地区：[1]武汉理工大学计算机科学与技术学院,湖北武汉430070 [2]湖北省科技信息研究院,湖北武汉430071

出　　处：《计算机应用与软件》2024年第11期358-365,共8页Computer Applications and Software

基　　金：湖北省技术创新专项重大项目(2019ABA102)。

摘　　要：针对现有的密码算法识别存在密文特征提取不足和识别准确率低等问题,提出一种FBR密文特征提取方法。该方法结合随机性测试中的频率(Frequency)、块内频率(Block Frequency)和游程(Run)三种方法,定义出密文的码元次数统计值、游程次数统计值和块内次数统计值,基于三种统计值构造出FBR特征。实验使用支持向量机对三种混合数据集分别进行密文二分类和多分类实验。实验结果表明,该方法所提取的FBR密文特征对比已有表现良好的密文特征,其平均识别准确率得到较高的提升,充分证明了该方法的有效性。Aimed at the problems of insufficient ciphertext feature extraction and low recognition accuracy in existing cryptographic algorithm recognition,a FBR ciphertext feature extraction method is proposed.This method combined the three methods of frequency,block frequency and run in the randomness test to define the statistical value of the number of symbols in the ciphertext,the statistical value of the number of runs,and the statistical value of the number of times within the block.FBR features were constructed based on three statistical values.The experiment used support vector machines to perform ciphertext two-classification and multi-classification experiments on the three mixed data sets.The experimental results show that the FBR ciphertext features extracted by this method are compared with the ciphertext features that have performed well,and the average recognition accuracy is improved,which fully proves the effectiveness of the proposed method.

关 键 词：密码算法识别 特征提取 FBR特征 支持向量机 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]