Application Layer DDoS Attack Detection Method Based on Multimodal Neural Network Traffic Characteristics
The increasing connectivity of agricultural equipment,sensors and monitoring systems to the network poses new cybersecurity challenges to rural distribution grids.Among them,distributed denial-of-service(DDoS)attacks are a common cyber threat that poses a serious threat to the security of rural power distribution networks.This study is dedicated to propose a network application layer DDoS attack detection method based on multimodal neural network traffic features for the special needs of rural power distribution networks.By formulating the web application layer traffic packet capture process and constructing a multimodal neural network model,the features of web application layer DDoS attack traffic are successfully extracted and analyzed.After loading the abnormal traffic features in the context of DDoS attack,the correlation coefficient is calculated and the corresponding DDoS attack detection rules are designed to achieve effective detection of DDoS attack.After experimental analysis,the proposed method performs well in extracting DDoS attack related features,with a maximum extraction completeness of up to 95%,which is significantly better than that of the DDoS attack detection methods based on EEMD-LSTM and those based on conditional entropy and decision tree in the comparison experiments.
rural power distribution networkstraffic feature extractionDDoS attacksnetwork application layermultimodal neural networkattack behavior detection
万方数据
维普
