Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

Edge computing is difficult to deploy a complete and reliable security strategy due to its dis-tributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be huge. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion de-tection model based on multi-algorithm fusion is proposed. Kernel Principal Component Analysis (KPCA) is used to extract data dimensions and simplify data representations. Then Subtractive Clustering algorithm (SCM) and Grey Wolf algorithm (GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge com-puting platform with weak computing ability and bearing capacity. The experimental results of BATADAL dataset and Gas dataset show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL dataset. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection.