Enhancing blockchain security through natural language processing and real-time monitoring

Abstract

We describe implementing and testing a security monitoring system for Blockchain-based applications by performing Log Analysis through natural language processing (NLP) techniques and enabling queries via REST API. The study focuses on the Hyperledger Fabric framework, a highly reliable and scalable open-source software platform for private blockchains. The real-time monitoring system aims to detect and prevent attack scenarios on the network structure and smart contracts, such as DDOS, Sybil, and Eclipse. The vulnerability analysis is extended to inspecting Docker containers to detect network-based attacks. Although the case study focuses on Hyperledger Fabric, the techniques used are general and applicable to all Blockchain-based systems and fulfill the need to provide adequate monitoring capabilities for blockchain implementations.