DES、Chaskey和PRESENT差分神经元判别模型的改进
Improving Differential-Neural Distinguisher Model For DES, Chaskey, and PRESENT
摘要：
Gohr在2019年的论文中提出了一种新的使用机器学习算法的密码分析策略。Gohr将差分神经网络与差分路径相结合，并结合高级密钥恢复过程，实现了对Speck 32/64的12轮密钥恢复攻击。Chen和Yu改进了差分神经网络的预测精度，考虑了从多个密文对而不是单个密文对导出的特征。通过修改初始卷积层的核大小以捕获更多的维数信息，可以提高差分神经网络对三种约简对称密码的预测精度。对于DES，我们改进了（5-6）轮差分神经网络的预测精度，并训练了一个新的7轮差分神经网络。对于Chaskey，我们改进了（3-4）轮差分神经网络的预测精度。对于PRESENT，我们改进了（6-7）轮差分神经网络的预测精度。源代码可在https://drive.google.com/drive/folders/1i0RciZlGZsEpCyW-wQAy7zzJeOLJNWqL?上获得USP=共享。
英文摘要：
In CRYPTO'19, Gohr proposed a new cryptanalysis strategy using machine learning algorithms. Combining the differential-neural distinguisher with a differential path and integrating the advanced key recovery procedure, Gohr achieved a 12-round key recovery attack on Speck32/64. Chen and Yu improved prediction accuracy of differential-neural distinguisher considering derived features from multiple-ciphertext pairs instead of single-ciphertext pairs. By modifying the kernel size of initial convolutional layer to capture more dimensional information, the prediction accuracy of differential-neural distinguisher can be improved for for three reduced symmetric ciphers. For DES, we improve the prediction accuracy of (5-6)-round differential-neural distinguisher and train a new 7-round differential-neural distinguisher. For Chaskey, we improve the prediction accuracy of (3-4)-round differential-neural distinguisher. For PRESENT, we improve the prediction accuracy of (6-7)-round differential-neural distinguisher. The source codes are available in https://drive.google.com/drive/folders/1i0RciZlGZsEpCyW-wQAy7zzJeOLJNWqL?usp=sharing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
