CFL-IDS: An Effective Clustered Federated Learning Framework for Industrial Internet of Things Intrusion Detection
发布时间：2023-10-17 点击次数：
姚羽（教授）
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- 博士生导师 硕士生导师
- 电子邮箱：
- 职务：复杂网络系统安全保障技术教育部工程研究中心主任
- 学历：博士研究生毕业
- 性别：男
- 联系方式：yaoyu@mail.neu.edu.cn
- 学位：博士
- 毕业院校：东北大学
- 所属院系：计算机科学与工程学院
- 学科：
计算机应用技术
计算机软件与理论
计算机系统结构
- 发表刊物：IEEE Internet of Things Journal.
- 影响因子：10.6
- 摘要：The Industrial Internet of Things (IIoT) offers the manufacturing sector opportunities for transformation and upgrade but also carries significant security risks. Traditional federated learning (FL) as a potential security solution is challenging in complicated application environments with heterogeneous data, imbalanced data, and poisoning attacks. To address these challenges, we construct a clustered FL Framework for IIoT intrusion detection (CFL-IDS) based on local models’ evaluation metrics (EMs). First, we designed an intrusion detection model with a dynamic focal loss (DFL) for all edge nodes (ENs). This model’s performance is enhanced under various imbalanced data partitions by dynamically altering the focus on samples during the loss minimization training process. Second, the time series of EMs of local models to reflect the data distribution of ENs implicitly, and use clustering algorithms to facilitate knowledge sharing among those ENs with similar data distribution to co-optimize a common model for them. Finally, an intelligent cooperative model aggregation mechanism (ICMAM) adaptively adjusts each local model’s weight distribution, which substantially improves the benefits of FL and alleviate subpar models’ alleviates interference from subpar models to FL. Experiments demonstrate that CFL-IDS has stronger robustness and displays superior performance under data imbalance and non-independent and identically distributed (non-IID) situations while being effective against poisoning attacks.
- 关键字：IIoT Intrusion detection, clustered federated learning, evaluation metrics, non-IID, data imbalanced, poisoning attack
- 论文类型：SCI JCR Q1
- 备注：https://ieeexplore.ieee.org/document/10285326
- 文献类型：JCR 一区
- 是否译文：否