ThreatFedChainAI:一种适应性边缘区块链架构,用于物联网网络中的大数据驱动威胁分析
N Ashwini1, Srinivas Dava2, A Rakesh Phanindra3
1Department of CSE, BMS Institute of Technology and Management, Bengaluru, Karnataka, India. ashwinilaxman@bmsit.in.
Scientific reports
|December 5, 2025
概括
本研究介绍了ThreatFedChainAI,这是一种用于实时物联网威胁检测的新型架构. 它使用联合学习和区块链增强安全性和隐私,实现卓越的准确性和F1分数,以实现强大的物联网安全性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 来自物联网设备的爆炸性数据增长在威胁预测,数据隐私和边缘计算资源方面带来了挑战.
- 现有的入侵检测系统 (IDS) 和静态机器学习模型在扩展性,适应新威胁和数据保密性方面扎.
- 联合学习 (FL) 往往缺乏对模型聚合和可解释性的信任,因此需要安全和可解释的物联网威胁分析.
研究的目的:
- 提出ThreatFedChainAI,一个用于实时物联网威胁预测和检测的架构.
- 解决当前物联网安全解决方案在可扩展性,适应性,隐私,信任和可解释性方面的局限性.
- 为物联网威胁分析开发一个安全,防改和保护隐私的系统.
主要方法:
- 集成边缘区块链架构,用于安全的数据管理和模型更新.
- 使用量子启发的粒子群优化算法,以实现高效的特征选择和维度减少.
- 实施适应式联合学习方法,并与区块链智能合约验证相结合.
- 应用基于SHAP的技术来提高威胁检测模型的可解释性.
主要成果:
- 与基线模型相比,ThreatFedChainAI表现出更高的性能,精度提高了高达5.3%.
- 在CICIDS2017和TON_IoT数据集上始终取得F1分数超过97%.
- 废弃性研究验证了该系统的有效性,可视化和表格支持这些发现.
结论:
- ThreatFedChainAI为实时物联网威胁检测提供了一个可扩展,安全和可解释的解决方案.
- 拟议的系统有效地解决了隐私,信任和物联网安全中的适应性等关键问题.
- 该架构非常适合在关键任务物联网网络中进行大规模部署.
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