Abstract
In response to the challenges posed by the high overhead and low detection efficiency of traditional SDN, a novel approach has been proposed to detect DDoS attacks. This cooperative method leverages information entropy and deep learning techniques to divide the detection task between the data plane and control plane. An advanced CNN-BiLSTM model with batch normalization and attention mechanism is utilized to identify DDoS attack traffic. The results of experiments demonstrate that this method offers superior accuracy, detection rate, and false alarm rate compared to prior approaches. Moreover, the switch-controller collaborative detection method proposed in this research reduces the occupancy rate of CPU, in contrast to the conventional single point detection method.
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