  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%.
