Effective Intrusion Detection and Prevention System of Botnet attack in Blockchain Technology using Recurrent Neural Network

Abstract

Intrusion Detection and prevention System are most common in cyber security. The aim of this undertaking is to delve into blockchain security and specifically apply machine learning techniques in building an Intrusion Detection and Prevention System (IDPS). Given the increasing popularity of blockchain technology, it has become more important to ensure the integrity, privacy and security of distributed ledgers. Our study intends to counteract this by examining new methods for data analysis which can help identify and prevent various forms of threats on blockchain networks. The IDPS will detect any malicious activity or suspicious moves through streaming real-time information about block chain. In this research paper we have evaluated effectiveness and efficacy of our proposed methodology in protecting blockchain networks from such types of attacks as well as intrusions.