Research on Intrusion Detection Based on Neural Network Optimized by Genetic Algorithm

Abstract

With the development of 5G and the emergence of the COVID-19 epidemic, network traffic has surged, and network security has once again become a key concern. Intrusion detection system is an important means to protect network security. It can find abnormal conditions in the early stage of cyber attack. Intrusion detection is also a kind of abnormal detection in a broad sense. To improve the performance of the intrusion detection system, a cyber-attack detection method combining Borderline SMOTE and improved BP neural network (Back-Propagation neural network) is proposed. It mainly uses one-hot encoding, Borderline SMOTE data oversampling and other technologies to preprocess the data, and uses the BP neural network improved by genetic algorithm to predict cyber attacks. Finally, the model is compared with other traditional machine learning models through the core indicator recall and auxiliary indicators precision, roc curve, etc. The results show that the hybrid detection model proposed in this study has higher recall and lower running time, and performs better in intrusion detection.