An Erebus Attack Detection Method Oriented to Blockchain Network Layer

【Author】 Dai, Qianyi; Zhang, Bin; Xu, Kaiyong; Dong, Shuqin

【Source】CMC-COMPUTERS MATERIALS & CONTINUA

【影响因子】3.860

【Abstract】Recently, the Erebus attack has proved to be a security threat to the blockchain network layer, and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer. The cloud -based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks. This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer. The goal is to improve the performance of Erebus attack detection methods, by combining the traffic behavior with the routing status based on multimodal deep feature learning. The traffic behavior and routing status were first defined and used to describe the attack characteristics at diverse stages of s leak monitoring, hidden traffic overlay, and transaction identity forgery. The goal is to clarify how an Erebus attack affects the routing transfer and traffic state on the blockchain network layer. Consequently, detecting objects is expected to become more relevant and sensitive. A two-stage feature selection algorithm was designed based on ReliefF and weighted maximum relevance minimum redundancy (ReliefF_WMRmR) to alleviate the overfitting of the training model caused by redundant information and noise in multiple source features of the routing status and traffic behavior. The ReliefF algorithm was introduced to select strong correlations and highly informative features of the labeled data. According to WMRmR, a feature selection framework was defined to eliminate weakly correlated features, eliminate redundant information, and reduce the detection overhead of the model. A multimodal deep learning model was constructed based on the multilayer perceptron (MLP) to settle the high false alarm rates incurred by multisource data. Using this model, isolated inputs and deep learning were conducted on the selected routing status and traffic behavior. Redundant intermodal information was removed because of the complementarity of the multimodal network, which was followed by feature fusion and output feature representation to boost classification detection precision. The experimental results demonstrate that the proposed method can detect features, such as traffic data, at key link nodes and route messages in a real blockchain network environment. Additionally, the model can detect Erebus attacks effectively. This study provides novelty to the existing Erebus attack detection by increasing the accuracy detection by 1.05%, the recall rate by 2.01%, and the F1-score by 2.43%.

【Keywords】Blockchain network; Erebus attack; attack detection; machine learning

【发表时间】2023

【收录时间】2023-06-24

【文献类型】实证数据

【主题类别】

区块链治理-技术治理-区块链安全