CAJ | 학술논문

提出了一种将新型的神经网络互学习模型和常见的多混沌系统融合互扰的复合流密码方案.首先利用三个Logistics混沌映射产生的随机序列作为神经网络互学习模型中三个隐含层神经元的随机输入,神经网络交互学习达到内部权值同步后,再将同步权值映射为随机序列并与三个Logistics序列复合产生最终的密钥流.实验表明,产生的密钥流具有更好的随机性,混沌流加密应用效果好.
제출료일충장신형적신경망락호학습모형화상견적다혼돈계통융합호우적복합류밀마방안.수선이용삼개Logistics혼돈영사산생적수궤서렬작위신경망락호학습모형중삼개은함층신경원적수궤수입,신경망락교호학습체도내부권치동보후,재장동보권치영사위수궤서렬병여삼개Logistics서렬복합산생최종적밀약류.실험표명,산생적밀약류구유경호적수궤성,혼돈류가밀응용효과호.
A hybrid stream cipher scheme is proposed based on the novel interacting neural networks and the multiple chaotic systems. At first, random sequences generated by 3 independent logistics functions respectively are taken as dynamic inputs to 3 hidden layers of the interacting neural networks model. Then two inner weights of the two structures of neural networks will be synchronized through some steps of interacting learning, and the random key stream can be finally identified by combining the random sequence extracted from the aforementioned synchronized weight and 3 Logistics sequences. The comparison shows that the generated key stream performs the better randomness than others. As a good example, the proposed novel chaos-based stream cipher works perfectly on digital image encryption.