A deep learning based detection scheme towards DDos Attack in permissioned blockchains

Chuang Li, Dongdong Huo, Sixiang Wang, Yaoyi Deng, Qihui Zhou, Yu Wang

Abstract: In view of the phenomenon that DDoS attacks of smart contract in permissioned blockchain cannot be well detected at present, we proposes a traffic anomaly detection method based on the two-layer GRU model. Firstly, we analyzed the traffic characteristics of DDoS attacks against permissioned blockchains, and the traffic characteristics are evaluated by random forest to obtain the importance ranking of the features, and the most important features are used to construct the dataset. Secondly, the traditional GRU model and encoder-decoder model are combined to reduce the dimension of the data through encoder, and then the data is restored through the decoding layer. Finally, experiments show that the two-layer GRU model has a high identification accuracy and accuracy rate in the detection of DDoS attacks.