Ethereum Eclipse Attack Detection based on BG-XGBoost

Abstract

Abstract: Eclipse attacks pose a serious threat to blockchain networks. Research has proven that Ethereum is more vulnerable to the Eclipse attack than the Bitcoin peer-to-peer network. Therefore, related research on Eclipse attacks on Ethereum is of great value. This paper proposes an improved XGBoost algorithm based on Bagging. It simulates a variety of random situations through the Bagging method, introduces randomness, reduces the risk of high errors, reduces the variance of the XGBoost model output, and improves the generalization ability of the model. It further enhances the model performance on binary classification problems and achieves efficient identification of Eclipse attack traffic and normal traffic.