An Improved BP Neural Network Anomaly Detection Method

The difficulty that the classical anomaly detection system based on BP neural network is that the convergence speed is too slow and the objective function falls into the local minimum problem. The established network genetic traffic detection model has low detection efficiency and accuracy. This article improves through a hybrid encoding method, while optimizing and modifying parameters such as crossover operator, mutation operator, crossover probability, and mutation probability. Based on this, this article proposes an genetically optimized BP neural network model by analyzing the structure of traditional neural network anomaly detection models. The research results show that the proposed algorithm overcomes the problem of slow training speed in traditional neural network anomaly detection and has better detection performance than traditional algorithms.