To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learn ing are proposed. Though the detection rate of many network intrusion detection solutions can be quite high nowadays, their identification accuracy on imbalanced abnormal network traffic still remains low. Therefore, this paper proposes a ResNet &Inception-based convolutional neural network (RICNN) model to abnormal traffic classification. RICNN can learn more traffic features through the Inception unit, and the degradation problem of the network is eliminated through the direct map ping unit of ResNet, thus the improvement of the model?s generalization ability can be achievable. In addition, to simplify the network, an improved version of RICNN, which makes it possible to reduce the number of parameters that need to be learnt without degrading identification accuracy, is also proposed in this paper. The experimental results on the dataset CICIDS2017 show that RICNN not only achieves an overall accuracy of 99.386% but also has a high detection rate across different categories, especially for small samples. The comparison experiments show that the recognition rate of RICNN outperforms a variety of CNN models and RNN models, and the best detection accuracy can be achieved.

A new algorithm of pattern recognition based on RBF neural network and Monkey-King genetic algorithm (MK-RBFNN) is prsented. The algorithm includes two parts. The first parts is that Monkey-King genetic algorithm is introduced to determine the initial positions and number of the hidden layer centers in RBF network. The second part is to set up the RBF neural network to do pattern recognition by learning and training the input sample data. Finally, experiments are implemented on datasets as Iris and WINES, which shows that the proposed algorithm has higher recognition ability compared with the conventional methods.