In the traditional public key cryptosystem based on certificates, the issuance and management of user certificates are realized through the authoritative certificate center, but amount of time is spent in the transmission and verification of user public key certificates. After a malicious user obtaining legitimate users' private keys, he can select a secret value and signature process to generate the final private key, public key and signature. And he will announce that he is the legal user, while others are unable to distinguish this process. This is the defect of traditional digital signature scheme without certificate. Therefore, this paper proposes a certificateless short signature scheme based on integrated neural networks and elliptic curve cryptography for secure data fusion analysis. The security of the solution is based on Inv-CDH problem. The complete security proof is given under the stochastic predictor model. It is proved that the new model can resist existence forgery in adaptive selective message attack with new adversary. Experiment results show that the calculation amount of our proposed certificateless short signature scheme is small and the efficiency is high compared with other state-of-the-art schemes.

The network learning is one new study way, the learning behavior need the real-time monitoring and the effective instruction to through the evaluation in the learning process. The BP algorithm model of evaluating E-Learning behavior selects the learning behavior which affects the study effect in network learning process, has established the network learning behavior evaluating indicator system, and takes the data-in by the second-level target, carries on the network training using MATLAB. In the network training process, the global error assumes the declining trend basically, the restraining effect is good. Through the test indicated that this model may use in evaluating the network learning behavior, and obtains the expectation effect.

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