Construction of a Security Defense Model for the University's Cyberspace Based on Machine Learning

Abstract

In order to ensure the security of university teachers and students using cyberspace, a machine learning based university cyberspace security defense model is constructed. Adopting a compression perception based data collection method for university cyberspace, the data information collection of university cyberspace is completed through sparse representation, compression measurement, and recovery reconstruction. Combining the advantages of Convolutional Neural Network (CNN) model in spatial feature extraction of data and Long Short Term Memory (LSTM) model in sequential feature extraction of data, extract the features of university network spatial data. After completing the multi feature dimensionality reduction processing of university network data based on the non-negative matrix decomposition algorithm, the feature dimensionality reduction processing results are input into the ConvLSTM-CNN model. After convolution calculation and integration, the security threat detection results of university network space are output. Based on the results of security threat detection, corresponding network attack defense measures are selected to ensure the security of the university's cyberspace. The experimental results show that the average attack interception rate of the model after application can reach 97.6%. It has been proven that building a model can accurately detect security threats to the university's cyberspace and achieve defense against various network attacks in different environments.