# Federated Learning for Malware Detection in IoT Devices

arXiv: https://arxiv.org/abs/2104.09994 · HTML: https://mlsanity.com/paper/2104.09994/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)
- [A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets](https://mlsanity.com/paper/2008.03343/) (2020)
- [EDIMA: Early Detection of IoT Malware Network Activity Using Machine Learning Techniques](https://mlsanity.com/paper/1906.09715/) (2019)
- [Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning](https://mlsanity.com/paper/2007.13054/) (2020)

## Built on by (most cited first, 30 of 36)

- [Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions](https://mlsanity.com/paper/2106.09527/) (2021)
- [FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation](https://mlsanity.com/paper/2210.08285/) (2022)
- [Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection](https://mlsanity.com/paper/2106.07976/) (2021)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [Federated Learning for 5G Base Station Traffic Forecasting](https://mlsanity.com/paper/2211.15220/) (2022)
- [Demystifying the Transferability of Adversarial Attacks in Computer Networks](https://mlsanity.com/paper/2110.04488/) (2021)
- [Active Learning for Network Traffic Classification: A Technical Study](https://mlsanity.com/paper/2106.06933/) (2021)
- [Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors](https://mlsanity.com/paper/2202.00137/) (2022)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Anomaly Detection via Federated Learning](https://mlsanity.com/paper/2210.06614/) (2022)
- [FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2402.10082/) (2024)
- [FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data](https://mlsanity.com/paper/2408.04442/) (2024)
- [Robust Federated Learning for execution time-based device model identification under label-flipping attack](https://mlsanity.com/paper/2111.14434/) (2021)
- [Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks](https://mlsanity.com/paper/2112.00988/) (2021)
- [Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks](https://mlsanity.com/paper/2209.12195/) (2022)
- [Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario](https://mlsanity.com/paper/2210.11061/) (2022)
- [Federated Deep Learning for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2306.02715/) (2023)
- [A Survey of Anomaly Detection in In-Vehicle Networks](https://mlsanity.com/paper/2409.07505/) (2024)
- [FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection](https://mlsanity.com/paper/2410.14121/) (2024)
- [FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing](https://mlsanity.com/paper/2205.11519/) (2022)
- [CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation](https://mlsanity.com/paper/2308.05978/) (2023)
- [FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning](https://mlsanity.com/paper/2507.07258/) (2025)
- [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026)
- [CADeSH: Collaborative Anomaly Detection for Smart Homes](https://mlsanity.com/paper/2303.01021/) (2023)
- [Unsupervised anomalies detection in IIoT edge devices networks using federated learning](https://mlsanity.com/paper/2308.12175/) (2023)
- [Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation](https://mlsanity.com/paper/2404.03088/) (2024)
- [Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach](https://mlsanity.com/paper/2405.14528/) (2024)
- [WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems](https://mlsanity.com/paper/2504.04367/) (2025)
- [Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks](https://mlsanity.com/paper/2510.23019/) (2025)
- [Split Learning-Enabled Framework for Secure and Light-weight Internet of Medical Things Systems](https://mlsanity.com/paper/2511.00336/) (2025)

## Abstract (arXiv)

This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is presented. N-BaIoT, a dataset modeling network traffic of several real IoT devices while affected by malware, has been used to evaluate the proposed framework. Both supervised and unsupervised federated models (multi-layer perceptron and autoencoder) able to detect malware affecting seen and unseen IoT devices of N-BaIoT have been trained and evaluated. Furthermore, their performance has been compared to two traditional approaches. The first one lets each participant locally train a model using only its own data, while the second consists of making the participants share their data with a central entity in charge of training a global model. This comparison has shown that the use of more diverse and large data, as done in the federated and centralized methods, has a considerable positive impact on the model performance. Besides, the federated models, while preserving the participant's privacy, show similar results as the centralized ones. As an additional contribution and to measure the robustness of the federated approach, an adversarial setup with several malicious participants poisoning the federated model has been considered. The baseline model aggregation averaging step used in most federated learning algorithms appears highly vulnerable to different attacks, even with a single adversary. The performance of other model aggregation functions acting as countermeasures is thus evaluated under the same attack scenarios. These functions provide a significant improvement against malicious participants, but more efforts are still needed to make federated approaches robust.