The Double Encryption System Combined with RSA Algorithm and Chaotic Synchronization

• Main Authors: Sheng-Yuan Lin, 林聖淵
• Other Authors: Feng-Hsiag Hsiao
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/43krbw

碩士 === 國立臺南大學 === 電機工程學系碩博士班 === 102 === A design methodology for neural-network (NN) -based secure communications in multiple time-delay chaotic (MTDC) systems is proposed in this thesis; RSA algorithm and chaotic synchronization are employed to obtain double encryption system. Chaotic synchronization has suffered from demodulating information by hackers via the public channel. Different methods have been developed to hide the contents of a message using chaotic signals. However, the attacks proposed in many studies have shown that most of these methods are not secure or have low security. The information can be stolen from hackers via the public channel, by estimating the state of the chaotic system and demodulating it by using an adaptive observer and robust controller. According to the above, this thesis combines chaotic synchronization with RSA algorithm to increase the complexity of the cryptosystem, thereby providing greater security. First, RSA algorithm and public key are used to encrypt the original message (plaintext). The encrypted message (ciphertext) is re-encrypted via chaotic synchronization. A robust fuzzy control design is then presented to overcome the effects of modeling errors between the MTDC systems and the NN models. Next, a delay-dependent exponential stability criterion is derived in terms of Lyapunov’s direct method, in order to ensure that the trajectories of the master system can approach those of the slave system. Subsequently, the stability conditions of this criterion are reformulated into linear matrix inequalities (LMIs). Genetic algorithms (GAs) have drawn great attention from researchers due to their capability for achieving near-optimal solutions in random search. The lower and upper bounds of the search space based on LMI approach can be set so that the GA will seek better feedback gains of fuzzy controllers in order to speed up the synchronization process. However, the GA has the shortcomings of premature convergence and local search. Therefore, an improved genetic algorithm (IGA) is proposed in this thesis to effectively overcome the weakness of the traditional GA. On the basis of the IGA, a fuzzy controller is synthesized to not only realize the exponential synchronization, but also achieve optimal H∞ performance by minimizing the disturbances attenuation level. Finally, a numerical example with simulations is given to illustrate the effectiveness of our approach.