Traffic Prediction and Attack Detection Approach Based on PSO Optimized Elman Neural Network

In view of the characteristics of non-linearity, multi-variable and time-varying of network system, PSO algorithm is proposed to optimize Elman neural network and obtain the optimal network parameters to improve the prediction accuracy and adaptability of the algorithm. Then, the anomalous traffic detected by the anomalous traffic detection model is used as input, and the samples projected into the high-dimensional feature space are classified by KNN algorithm. An attack detection classifier is established to identify the types of network attacks. The experimental results show that, compared with traditional attack detection methods, the proposed network attack detection model based on abnormal traffic analysis improves the accuracy of intrusion detection, which can effectively identify the types of network attacks and ensure the security of information systems.