FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT
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Abstract (arXiv)
The distributed denial of service (DDoS) attack is detrimental to the industrial Internet of things (IIoT) as it triggers severe resource starvation on networked objects. Recent dynamics demonstrate that it is a highly profitable business for attackers using botnets. Current centralized mitigation solutions concentrate on detection and mitigation at a victim's side, paying inadequate attention to hacking costs and the collaboration of defenders. Thus, we propose the federated learning empowered mitigation architecture (FLEAM) to advocate joint defense, incurring a higher hacking expense. FLEAM combines FL and fog computing to reduce mitigation time and improve detection accuracy, enabling defenders to jointly combatting botnets. Our comprehensive evaluations showcase that the attacking expense incurred is 2.5 times higher, the mitigation delay is about 72% lower, and the accuracy is 47% greater on average than classic solutions.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 2; 2025: 5; 2026: 1.
12 papers.
- 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
- 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
- 2023Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learningcited by 3
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
- 2023Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detectioncited by 2
- 2025Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detectioncited by 2
- 2025RepuNet: A Reputation System for Mitigating Malicious Clients in DFLcited by 1
- 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
- 2024Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systemscited by 0
- 2025OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoTcited by 0
- 2025Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networkscited by 0
- 2026Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networkscited by 0
- 2026Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks18 Aug 2026
- 2025Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks27 Oct 2025
- 2025OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT5 Oct 2025
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks26 Aug 2025
- 2025RepuNet: A Reputation System for Mitigating Malicious Clients in DFL24 Jun 2025
- 2025Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection18 Mar 2025
- 2024Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems12 Sep 2024
- 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community9 Jul 2024
- 2023Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning2 Jul 2023
- 2023Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection22 Jun 2023
- 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges2 Aug 2021
- 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions16 Jun 2021