在联邦学习中加噪声，平衡区块链攻击检测的隐私与准确
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks
- 用差分隐私原理在子模型加噪再融合，保护数据隐私
- 测试高斯、拉普拉斯等噪声，发现对检测精度和收敛时间有影响
- 给出实用建议，适合关注区块链安全的工程师参考
本文提出一种新型协同攻击检测（CCD）系统，旨在提升基于区块链的数据共享网络安全性，解决联邦学习中添加噪声带来的挑战。基于差分隐私理论，该方法在传输前向训练好的子模型注入噪声以重构全局模型。系统性研究了高斯、拉普拉斯及动量计数器三种噪声类型对关键性能指标的影响，包括攻击检测准确率、深度学习模型收敛时间以及全局模型生成总耗时。结果揭示了数据隐私保障与系统性能之间的复杂权衡，为不同CCD环境下的参数优化提供了宝贵洞见。通过大量仿真，提出了实现数据保护与系统效率最佳平衡的实际建议，推动安全可靠的区块链网络发展。
原文摘要 · Abstract (English)
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.
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