Predictive Analytics Service for Security of Blockchain and Peer-to-Peer Payment Solutions

Abstract

The blockchain and Peer-To-Peer Payment solutions become adopted by financial institutions. While these changes bring significant service benefits they also increase the risks and vulnerabilities of the financial services. In this paper, we investigate, develop, and evaluate machine learning (ML) algorithms for predicting attacks on blockchain nodes and a Peer to Peer payment system. We have evaluated a set of machine learning algorithms that include classification ML algorithms from the scikit-learn library. We demonstrate that the proposed solution is able to predict cyber-physical attacks close to 100% accuracy. We have implemented a service prototype as a proof of concept. The prediction is done based on the collected data of the blockchain and peer-to-peer payment nodes. For the evaluation of the algorithms, a set of highly reputable classification metrics has been selected and applied.