The malicious resource consumption detection in permissioned blockchain based on traffic analysis

Qihui Zhou, Qianyun Ruan, Dongdong Huo, Penghui Lv, Yu Wang, Zhen Xu

Abstract: Blockchain is widely applied in many applications. However, it faces many security risks. Unlike external attacks, the evildoing of internal members need more attentions in permissioned blockchain. We introduce the malicious resource consumption attack which seriously degrade the performance of permissioned blockchain system. To detect malicious resource consumption attacks, we propose MRC-Dec which applies a LSTM-AE model to analyze traffic between blockchain nodes, to determine whether the attacks happen. First, we construct the traffic dataset for attack detection by implementing two types of malicious resource consumption, namely useless endorsement and storage pollution on Fabric. Next in order not to be affected by the blockchain scale which means the number of nodes in blockchain, the traffic features are extracted between the node sets instead of between nodes. After feature extraction, a sequence of traffic features in temporal order is obtained, which is input to the LSTM-AE model. The LSTM-AE model learns the characteristic of normal traffic, and a threshold is set to determine normal and attack. The evaluation results show that MRC-Dec can find the malicious resource consumption attacks with high performance and can well adopt to the blockchain scale.

External IDs:dblp:conf/cscwd/ZhouRHL0X23