DDoS Attack Detection Method Based on Multi-scale Convolutional Neural Network
In recent years,network security is facing increasing challenges,among which Distributed Denial of Service(DDoS)attack is a common form of network threats.In order to deal with this challenge,this paper proposes a DDoS attack detection method based on Multi-scale Convolutional Neural Network(MSCNN).The model is trained on the CICDDoS2019day1 dataset,and the model detection performance is tested on the CICDDoS2019day2 dataset.By using MSCNN to predict and classify network traffic,DDoS attacks can be effectively identified and false positive rate can be reduced.Experiments show that the MSCNN method is superior to DNN,CNN and LSTM in terms of accuracy,recall and F1 score performance metrics.
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