# FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT

arXiv: https://arxiv.org/abs/2012.06150 · HTML: https://mlsanity.com/paper/2012.06150/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions](https://mlsanity.com/paper/2106.09527/) (2021)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning](https://mlsanity.com/paper/2307.00541/) (2023)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection](https://mlsanity.com/paper/2306.13029/) (2023)
- [Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection](https://mlsanity.com/paper/2503.14618/) (2025)
- [RepuNet: A Reputation System for Mitigating Malicious Clients in DFL](https://mlsanity.com/paper/2506.19892/) (2025)
- [CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community](https://mlsanity.com/paper/2407.06485/) (2024)
- [Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems](https://mlsanity.com/paper/2409.08237/) (2024)
- [OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT](https://mlsanity.com/paper/2510.05180/) (2025)
- [Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks](https://mlsanity.com/paper/2510.23019/) (2025)
- [Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks](https://mlsanity.com/paper/2608.17352/) (2026)

## 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.