Research on Network Intrusion Detection Model Based on Grey Wolf Strategy

The large volume and high feature latitude are the characteristics of network connection recording data presentation. On the other hand, in a network connection record data, the value range of each feature attribute is different, and some feature attribute value categories are diverse. These factors pose challenges to network intrusion detection. In order to address the drawbacks of traditional detection methods and the optimization problem of high-dimensional objective functions, an animal hunting strategy was employed to optimize the network structure of the convolutional neural network model, resulting in a more optimized network intrusion detection model. Through simulation experiments, it was found that using animal hunting optimization algorithm resulted in faster convergence speed of the model, while verifying the effectiveness and accuracy of the proposed algorithm.