According to statistics, a large part of large amount of malicious codes belong to deceptive malicious codes. They usually use icons which are similar to those icons commonly used softwares to disguise themselves and deceive users to click to achieve the purpose of communication and attack. Aiming at solving the problems of low efficiency and high cost of traditional malicious code detection methods based on code and behavior characteristics on the deceptive malicious codes, a new malicious code detection method was proposed. Firstly, Portable Executable (PE) file icon resource information was extracted and icon similarity analysis was performed by image hash algorithm. Then, the PE file import table information was extracted and a fuzzy hash algorithm was used for behavior similarity analysis. Finally, clustering and local sensitive hash algorithms were adopted to realize icon matching, designing and implementing a lightweight and rapid malicious code detection tool. The experimental results show that the designed tool has a good detection effect on malicious code.

With the arrival of big data, network public opinion data reveals the features of massive information and wide coverage. For the complicated network public opinion data, traditional single models may not efficiently predict the trend of network public opinion. To address this question, the improved combination model based on the Empirical Mode Decomposition-AutoRegression (EMD-AR) model was proposed, called EMD-ARXG (Empirical Mode Decomposition-AutoRegression based on eXtreme Gradient boosting)model. EMD-ARXG model was applied to the prediction of the trend of complex network public opinion. In this model, the Empirical Mode Decomposition (EMD) algorithm was employed to decompose the time series, and then AutoRegression (AR) model was applied to fit the decomposed time series and establish sub-models. Finally, the sub-models were reconstructed and then the modelling process was completed. In addition, in the fitting process AR model, in order to reduce the fitting error, the residual error was learned by eXtreme Gradient Boosting (XGBoost), and each sub-model was iteratively updated to improve its prediction accuracy. In order to verify the prediction performance of EMD-ARXG model, the proposed model was compared with wavelet neural network model and back propagation neural network based on EMD model. The experimental results show that the EMD-ARXG model is superior to two other models in terms of the statistical indicators including Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil Inequality Coefficient (TIC).

According to the characteristics of Ad Hoc networks, such as mobility and self-organization, an identity-based key management scheme for Ad Hoc networks was proposed. In the paper, by the method of secure distributed key generation based on threshold cryptography, the interior members of Ad Hoc networks collaborated to conduct the system private key. Compared with the existent protocol, the proposed scheme, does not require the fixed structure of service nodes, and service nodes can dynamically join and leave network. At the same time, the system key is updatable among service nodes. The analytical results show the proposed scheme is flexible and secure, and it is suitable for Mobile Ad Hoc Network (MANET).