FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918158727643136 |
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| author | Zehra, Sehar Syed, Hassan Jamil Faseeha, Ummay |
| author_facet | Zehra, Sehar Syed, Hassan Jamil Faseeha, Ummay |
| contents | Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kernel-level telemetry with federated learning (FL) for cross-cluster anomaly detection. Lightweight eBPF agents capture syscalls and network events, extract local statistical and sequence features, and share only model updates with a global server. A hybrid detection engine combining Variational Autoencoders (VAEs) with Isolation Forests enables both temporal pattern modeling and outlier detection. Deployed across three Kubernetes clusters, FedMon achieves 94% precision, 91% recall, and an F1-score of 0.92, while cutting bandwidth usage by 60% relative to centralized baselines. Results demonstrate that FedMon enhances accuracy, scalability, and privacy, providing an effective defense for large-scale, multi-tenant cloud-native environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments Zehra, Sehar Syed, Hassan Jamil Faseeha, Ummay Distributed, Parallel, and Cluster Computing F.2.2, I.2.7 Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kernel-level telemetry with federated learning (FL) for cross-cluster anomaly detection. Lightweight eBPF agents capture syscalls and network events, extract local statistical and sequence features, and share only model updates with a global server. A hybrid detection engine combining Variational Autoencoders (VAEs) with Isolation Forests enables both temporal pattern modeling and outlier detection. Deployed across three Kubernetes clusters, FedMon achieves 94% precision, 91% recall, and an F1-score of 0.92, while cutting bandwidth usage by 60% relative to centralized baselines. Results demonstrate that FedMon enhances accuracy, scalability, and privacy, providing an effective defense for large-scale, multi-tenant cloud-native environments. |
| title | FedMon: Federated eBPF Monitoring for Distributed Anomaly Detection in Multi-Cluster Cloud Environments |
| topic | Distributed, Parallel, and Cluster Computing F.2.2, I.2.7 |
| url | https://arxiv.org/abs/2510.10126 |