Design and Implementation of Intrusion Detection System Based on Neural Network

With the continuous emergence of cyber-attacks, traditional intrusion detection methods become increasingly limited. In the field of network security, new intrusion detection methods are needed to ensure network security. To solve the problems, relevant knowledge involved was first introduced. Then, an intrusion detection system based on neural network was designed according to the general intrusion detection framework, and the design of the event collector and analyzer in the system was described in detail. Experiments were conducted with the weight initialization method of the neural network model, the selection of the activation function, and the selection of the optimizer. Finally, the most suitable hyperparameters were determined and the optimal neural network model was trained. The test results show that the application of neural network to the intrusion detection system can greatly improve the accuracy of intrusion detection, thereby improving the security of computer networks.