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A Combining Encryption and Decryption Algorithm Based on Neural Networks, Cross-variation and FFT

机译:基于神经网络,交叉变量和FFT的组合加密和解密算法

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摘要

Diversity and aperiodicity of populations generated by genetic algorithm and high nonlinearity of fast Fourier transformation (FFT) are absorbed to create a new method of encryption and decryption of important data based on neural networks, cross-variation and FFT algorithm. Through the cross-variation operation of key sequence, the model improves nonlinear relationship between key and initial random seeds; transformation of part of ciphertext avoids the ciphertext-only attack efficiently. The whole model enhances periodicity of key sequence and complexity of the algorithm. Because of realizing the genuine "one-time pad", it can ensure that the encryption system has a very high security standard.
机译:吸收了遗传算法产生的种群的多样性和非周期性以及快速傅立叶变换(FFT)的高度非线性,从而创建了一种基于神经网络,交叉变量和FFT算法的重要数据加密和解密的新方法。通过密钥序列的交叉变异运算,该模型改善了密钥与初始随机种子之间的非线性关系。部分密文的转换有效地避免了仅密文攻击。整个模型提高了密钥序列的周期性和算法的复杂性。由于实现了真正的“一次性密码”,因此可以确保加密系统具有很高的安全性标准。

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