Network Security State Identification Based on Neural Network Optimized by Ant Colony Algorithm

Abstract

Intrusion detection is the key technology to ensure network security. In order to solve the problem of parameter optimization in the application of neural network in intrusion detection, a network intrusion detection model based on ant colony algorithm is proposed in this paper. Firstly, this paper describes the relationship between ant colony algorithm and neural network parameters, and establishes the objective function of neural network parameter selection. Then the ant colony algorithm is used to search the optimal solution of the objective function and determine the optimal parameters of the neural network. Finally, this paper realizes the construction of intrusion detection classifier through neural network self-organizing learning. The results show that the model solves the problem of parameter optimization of neural network in intrusion detection. The classification results and classification speed have significant advantages over the typical model.