Simulation of automatic intrusion detection in university networks by using neural network algorithms

• It is necessary to develop an automatic detection method • This article collects a large amount of network intrusion data from universities • The security of university networks can be improved With the rapid development of computers and the Internet, university networks are facing more and more network intrusion threats, and it is necessary to develop an automatic detection method based on neural network algorithms. This article collects a large amount of network intrusion data from universities and obtains data on normal network traffic for comparison. Preprocess the collected data and use neural network algorithms to design appropriate network structures and parameters. Train the neural network model through a training set to learn the characteristics of network intrusion behavior. Using appropriate training methods and optimization algorithms, train and optimize the neural network model to achieve high accuracy and robustness. Finally, the trained neural network model was applied to the actual network environment of universities, achieving automatic detection and warning functions, and timely discovering potential network intrusion behaviors. By establishing a neural network model and implementing automatic detection and warning, the security of university networks can be improved and the normal operation of the network can be protected.