Federated Learning-based Robust Intrusion Detection System for Public Data Authorized Operations
[1]WEI Lin,CHEN Chao,HAN Ding,et al.Federated Learning-based Robust Intrusion Detection System for Public Data Authorized Operations[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.[doi:10.13705/j.issn.1671-6833.2027.01.008]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
48
Number of periods:
2027 XX
Page number:
1-9
Column:
Public date:
2027-12-10
- Title:
- Federated Learning-based Robust Intrusion Detection System for Public Data Authorized Operations
- Keywords:
- clustered federated learning; public data; intrusion detection; poisoning attack defense; data heteroge; neity; class imbalance
- CLC:
- TP18 TP391
- Abstract:
- While public data authorized operation enables multi-party data sharing and reuse, it confronts malicious intrusion threats targeting public data, making reliable intrusion detection technologies imperative to guarantee datasecurity and privacy. Traditional federated learning-based intrusion detection technologies have three major limitations: severe class imbalance of intrusion detection data leading to high false positive rates for minority intrusionsamples; data heterogeneity across nodes exacerbating deviations between local and global models and slowing down model convergence; and poisoning attacks from malicious nodes undermining model training and degrading the detection performance of benign nodes. A federated learning-based robust intrusion detection system ( FL-RIDS) for public data authorized operation was proposed. Firstly, an effective sample number weighted loss function ( ESLoss) for intrusion detection was designed to dynamically adjust class weights and significantly improve the model′s sensitivity to few-sample intrusion categories. Secondly, a model performance evaluation-based clustering method( SEC) was proposed. It evaluated the performance of local models of all participant nodes on the cloud server and clusters nodes with similar data features, effectively mitigating performance degradation and slow convergence caused by data heterogeneity. Finally, a lightweight poisoning detection algorithm ( LPD) was proposed to enhance system robustness against malicious attacks. It dynamically identified malicious nodes launching poisoning attacks during training by monitoring the similarity variation of update directions between local node models and cluster global model classifiers. Experimental results showed that in CICIDS2017 and CAN-intrusion-dataset, ESLoss improved the f1score by 18. 9 and 3. 2 percentage points respectively at the highest level compared to the cross-entropy loss function. The SEC method improved the f1score by 4. 9 and 5. 9 percentage points respectively at the highest level compared to FedAvg with excellent anti-poisoning performance. In addition, LPD improved the f1score by 54. 2 and 68. 4 percentage points respectively at the highest level compared to FedAvg, which could effectivelyresist poisoning attacks launched by malicious nodes in the middle of model training.
- References:
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Last Update:
2026-10-30
