Blockchain security is becoming increasingly relevant in today's cyberspace as it extends its influence in many industries. This paper focuses on protecting the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate and share information. The P2P network layer may be vulnerable to several families of attacks, such as Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. This layer is prone to threats inherited from traditional P2P networks, and it must be analyzed and understood by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain P2P network, at an early stage. Tikuna employs an unsupervised Long Short-Term Memory (LSTM) method based on Recurrent Neural Network (RNN) to detect attacks and alert users. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy. Our research findings demonstrate that Tikuna is a valuable security tool for assisting operators to efficiently monitor and safeguard the status of Ethereum validators and the wider P2P network

翻译：随着区块链技术在众多行业中的影响力不断扩大，其在当今网络空间中的安全性日益重要。本文聚焦于区块链最底层——特别是实现节点间通信和信息共享的P2P网络层的保护。该P2P网络层易受多种攻击族的威胁，包括分布式拒绝服务攻击、日蚀攻击或女巫攻击。由于继承了传统P2P网络的脆弱性，必须通过收集数据并从网络行为中提取洞察来分析和理解这一风险层。我们提出Tikuna——一款用于早期监测和检测以太坊区块链P2P网络潜在攻击的开源工具。Tikuna采用基于循环神经网络的无监督长短期记忆方法检测攻击并向用户发出警报。实证结果表明，该方法显著提升了检测性能，能以高准确率检测并分类针对以太坊区块链P2P网络层的日蚀攻击、隐蔽闪电攻击及其他攻击。研究证明，Tikuna作为一款有效安全工具，可帮助运维人员高效监测并维护以太坊验证者及更广泛P2P网络的安全状态。