Hybrid network attack prediction with Savitzky–Golay filter-assisted informer

Abstract

Due to the emergence of new network attack technologies, cloud manufacturing platforms may be subject to various network attacks at any time. Traditional network attack prediction aims to predict the upcoming types of network attacks with monitored data characteristics, and this prediction method cannot accurately learn the network attack traffic that the cloud manufacturing platform may suffer in the future. Network attack traffic prediction means the future network attack traffic prediction by using past data. Traditional machine learning approaches cannot investigate complex nonlinear features. Deep learning methods can investigate nonlinear characteristics, yet they suffer from the problem of overfitting. In addition to this, deep learning approaches suffer from gradient disappearance and explosion. To solve them, we design a network attack traffic method called S-Informer, which integrates the filter of Savitzky–Golay, the self-attention of ProbSparse in a generative decoder and an encoder for eliminating noise, reducing the network scale and improving the speed of prediction. Real-life dataset-based experimental results show that S-Informer achieves higher prediction accuracy than several commonly-used algorithms.