DDoS attack detection method based on ConvLSTM and ResNet in SDN
With the continuous development of software-defined networks,security challenges are increasing,the most common of which is distributed denial of service attack.In this paper,we deeply analyze the security problems of software-defined networks,especially the lack of feature extraction of time series data in detecting distributed denial-of-service attacks.To solve this problem,this paper proposes an attack detection method based on deep learning.In this method,convolutional long and short time memory network is first introduced,and multi-head attention mechanism is combined to capture spatial and temporal characteristics of network data at the same time.Then,through the improved residual network,the problem of gradient disappearance in the training process of neural network is solved,and the ability of data feature extraction is strengthened again.Finally,the sigmoid function is used to classify the input data to determine whether it is normal traffic or malicious attacks.The experimental results show that the proposed model has excellent performance.
software-defined networkingdistributed denial of serviceconvolutional long short-term memory networkmulti-head attention mechanismresidual network
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