Federated deep learning algorithm for RPA network security detection

Abstract

ABSTRACT The development of Internet technology has produced a huge impact on people’s lives. More data information is generated in life or work. Research proposes a combined detection model, and combines Fed Prox, convolutional neural network, and support vector machine to obtain Fed Prox Convolutional Neural Networks Support Vector Machine (Fed PCNN). The network intrusion detection model trains the local model with the divided local data, uses the model with the near-end item as the judgment basis, analyzes the difference between the model and the global model, and then trains the local model through local iteration. Finally, the local model is aggregated to obtain the best classification prediction effect of the model effect. The research proves that the Fed PCNN model has an excellent performance in recall and accuracy through experiments. In different data sets, the recall rate is more than 90%, the accuracy rate is more than 95%, and the false alarm rate is less than 2%. Therefore, the Fed PCNN model has a good detection effect in network intrusion detection.