Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks

TL;DR

This paper introduces a federated learning-based cyberattack detection system for blockchain networks that balances privacy and accuracy by analyzing the impact of different noise types on system performance.

Contribution

It proposes a novel CCD framework that integrates differential privacy noise into federated models and systematically evaluates its effects on detection accuracy and efficiency.

Findings

Gaussian noise offers a good balance between privacy and accuracy.

Laplace noise impacts convergence time more significantly.

Optimal noise parameters depend on specific security and performance requirements.

Abstract

This paper presents a novel Collaborative Cyberattack Detection (CCD) system aimed at enhancing the security of blockchain-based data-sharing networks by addressing the complex challenges associated with noise addition in federated learning models. Leveraging the theoretical principles of differential privacy, our approach strategically integrates noise into trained sub-models before reconstructing the global model through transmission. We systematically explore the effects of various noise types, i.e., Gaussian, Laplace, and Moment Accountant, on key performance metrics, including attack detection accuracy, deep learning model convergence time, and the overall runtime of global model generation. Our findings reveal the intricate trade-offs between ensuring data privacy and maintaining system performance, offering valuable insights into optimizing these parameters for diverse CCD environments. Through extensive simulations, we provide actionable recommendations for achieving an optimal balance between data protection and system efficiency, contributing to the advancement of secure and reliable blockchain networks.