Real-time Cyberattack Detection with Collaborative Learning for Blockchain Networks

• Published in: 2024 IEEE Wireless Communications and Networking Conference (WCNC) pp. 1 - 6
• Main Authors: Khoa, Tran Viet, Son, Do Hai, Hoang, Dinh Thai, Trung, Nguyen Linh, Thuy Quynh, Tran Thi, Nguyen, Diep N., Ha, Nguyen Viet, Dutkiewicz, Eryk
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.04.2024
• Subjects: Accuracy; blockchain; Collaboration; Cyberattack detection; cybersecurity; Data models; Data privacy; deep learning; distributed machine learning; Federated learning; Knowledge engineering; System performance
• ISSN: 1558-2612
• DOI: 10.1109/WCNC57260.2024.10571103

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 realtime 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 realtime experimental results show that our proposed detection model can detect attacks in the blockchain network with an accuracy of up to 97%.