Research on the Validity of Network Security Model Based on WOA-BP Neural Network

In the field of network security, using machine learning to detect network anomalies and attacks is becoming increasingly popular. However, the availability of a security model based on machine learning may vary depending on the dataset and the setting of security features. In this article, we consider introducing a binary classification model for anomaly detection and a multi category classification model for various types of network attacks. Machine learning algorithms used to implement the models above include Naive Bayes, Random Forest, and BP artificial neural network security models, as well as WOA optimized by BP neural network. Firstly, we conducted a series of comparative experiments by using UNSW-NB15, the most popular security data sets. On this basis, we investigated the effectiveness of the network security model. Finally, conclusions about the effectiveness of network anomaly detection are drawn, which can provide certain model ideas and data support for current network security analysis.