Real-time Cyberattack Detection with Collaborative Learning for Blockchain Networks

TL;DR

This paper introduces a real-time collaborative learning model for blockchain networks that detects cyberattacks with high accuracy by sharing knowledge among nodes without revealing private data.

Contribution

It presents a novel collaborative detection model and a new dataset for training and testing blockchain attack detection methods.

Findings

Detection accuracy up to 97%

Effective real-time attack identification

Enhanced network security through collaborative learning

Abstract

With the ever-increasing popularity of blockchain applications, securing blockchain networks plays a critical role in these cyber systems. In this paper, we first study cyberattacks (e.g., flooding of transactions, brute pass) in blockchain networks and then propose an efficient collaborative cyberattack detection model to protect blockchain networks. Specifically, we deploy a blockchain network in our laboratory to build a new dataset including both normal and attack traffic data. The main aim of this dataset is to generate actual attack data from different nodes in the blockchain network that can be used to train and test blockchain attack detection models. We then propose a real-time collaborative learning model that enables nodes in the network to share learning knowledge without disclosing their private data, thereby significantly enhancing system performance for the whole network. The extensive simulation and real-time experimental results show that our proposed detection model can detect attacks in the blockchain network with an accuracy of up to 97%.