Focused on the issue that Elman neural network has slow convergence speed and low prediction accuracy in the closing price prediction based on the network public opinion of the stock market, a prediction model combining Improved Whale Optimization Algorithm (IWOA) and Elman neural network was proposed, which is based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)algorithm. Firstly, text mining technology was used to mine and quantify the network public opinions of Shanghai Stock Exchange (SSE) 180 shares, and in order to reduce the complexity of attribute set, Boruta algorithm was used to select the important attributes. Then, CEEMDAN algorithm was used to add a certain number of white noises with specific variances in order to realize the decomposition and noise reduction of the attribute sequence. At the same time, in order to enhance the global search and local mining capabilities, adaptive weight was used to improve Whale Optimization Algorithm (WOA). Finally, the initial weights and thresholds of Elman neural network were optimized by WOA in the iterative process. The results show that, compared to Elman neural network, the proposed model has the Mean Absolute Error (MAE) reduced from 358.812 0 to 113.055 3； compared to the original dataset without CEEMDAN algorithm, the proposed model has the Mean Absolute Percentage Error (MAPE) reduced from 4.942 3% to 1.445 31%, demonstrating that the model effectively improves the prediction accuracy and provides an effective experimental method for predicting the network public opinion of stock market.

The second generation of blockchain represented by smart contract has experienced an explosive growth of its platforms and applications in recent years. However, frequent smart contract vulnerability incidents pose a serious risk to blockchain ecosystem security. Since code auditing based on expert experience is inefficient in smart contracts vulnerability mining, the significance of developing universal automated tools to mining smart contracts vulnerability was proposed. Firstly, the security threats faced by smart contracts were investigated and analyzed. Top 10 vulnerabilities, including code reentrancy, access control and integer overflow, as well as corresponding attack modes were summarized. Secondly, mainstream detection methods of smart contract vulnerabilities and related works were discussed. Thirdly, the performance of three existing tools based on symbolic execution were verified through experiments. For a single type of vulnerability, the highest false negative rate was 0.48 and the highest false positive rate was 0.38. The experimental results indicate that existing studies only support incomplete types of vulnerability with many false negatives and positives and depend on manual review. Finally, future research directions were forecasted aiming at these limitations, and a symbolic-execution-based fuzzy test framework was proposed. The framework can alleviate the problems of insufficient code coverage in fuzzy test and path explosion in symbolic execution, thus improving vulnerability mining efficiency for large and medium-sized smart contracts.

To solve the problems in traditional network protocol recognition methods, such as difficulty of manual feature extraction and low recognition accuracy, an application protocol recognition method based on Convolutional Neural Network (CNN) was proposed. Firstly, the raw network data was divided according to Transmission Control Protocol (TCP) connection or User Datagram Protocol (UDP) interaction, and the network flow was extracted. Secondly, the network flow was converted into a two-dimensional matrix through data prepocessing to facilitate the CNN analysis. Then, a CNN model was trained using the training set to extract protocol features automatically. Finally, the trained CNN model was used to recognize the application network protocols. The experimental results show that, the overall recognition accuracy of the proposed method is about 99.70%, which can effectively recognize the application protocols.

Since the protocol classification of network traffic is a prerequisite for protocol analysis and network management, the network protocol recognition techniques were researched and reviewed. Firstly, the target of network protocol recognition was described, and the general process of protocol recognition was analyzed. The practical requirements for protocol recognition were discussed, and the criteria for evaluating protocol recognition methods were given. Then, the research status of network protocol techniques was summarized from two categories:packet-based protocol recognition methods and flow-based protocol recognition methods, and the variety of techniques used for protocol recognition were analyzed and compared. Finally, with the defects of current protocol recognition methods and the practical application requirements considered, the research trend of protocol recognition techniques was forecasted.

The fairness of verifiable encrypted signature scheme is completely determined by the arbitrators neutral problem, which reduces the security of signature exchange. In order to deal with this issue, using the properties of bilinear pairings and combining with certificateless public key cryptography and verifiable encrypted signature, a blind verifiable encrypted signature was designed without certificate. The adjudicator in this scheme cannot restore the original signature directly, thereby the security of exchange signature protocols was enhanced. The proposed scheme was also provably secure in the random oracle module under Discrete Logarithm Problem (DLP) and Computational Differ-Hellman Problem (CDHP) assumption.

In cognitive radio system, cognitive engine can dynamically configure its working parameters according to the changes of communication environment and users’ requirement. Intelligent optimization algorithm of cognitive engine had been studied, and a Binary Ant Colony Simulated Annealing (BAC&SA) algorithm was proposed for parameters optimization of cognitive radio system. The new algorithm, which introduced the Simulated Annealing (SA) algorithm into the Binary Ant Colony Optimization (BACO) algorithm, combined the rapid optimization ability of BACO with probability jumping property of SA, and effectively avoided the defect of falling into local optimization result of BACO. The simulation results show that cognitive engine based on BAC&SA algorithm has considerable advantage over GA and BACO algorithm in the global search ability and average fitness.

To analyze the difficulties of detection under “soft” failures, an improved B-style grey relationship degree based on the moving state propagator for the federated filter (FF) was developed to solve above problems. The mode can obtain the highest fault tolerance quality without feedback, mutual pollution was avoided by using FF. Also, an adaptive balance coefficient and its algorithm was presented to balance the optimal information sharing approach and the fault tolerance one. According to the algorithm in this paper, the information sharing coefficients were adaptively adjusted by the failure grade, and the approximate highest fusion accuracy was ensured, the algorithm has the character of low calculation, simple configuration, high precision, and was suitable to practical application. This simulation results indicate that fusion accuracy under the largest range of failures was increased approximate 28.5%, which shows the fusion accuracy under failures was efficiently improved and the approximate highest fusion accuracy of whole process was realized.