# Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

arXiv: https://arxiv.org/abs/2202.00137 · HTML: https://mlsanity.com/paper/2202.00137/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets](https://mlsanity.com/paper/2008.03343/) (2020)
- [Electrosense: Open and Big Spectrum Data](https://mlsanity.com/paper/1703.09989/) (2017)
- [EDIMA: Early Detection of IoT Malware Network Activity Using Machine Learning Techniques](https://mlsanity.com/paper/1906.09715/) (2019)

## Built on by (most cited first, 5 of 5)

- [Secure Federated Learning for Cognitive Radio Sensing](https://mlsanity.com/paper/2304.06519/) (2023)
- [RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT](https://mlsanity.com/paper/2212.14647/) (2022)
- [Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario](https://mlsanity.com/paper/2210.11061/) (2022)
- [Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding](https://mlsanity.com/paper/2410.09284/) (2024)
- [SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks](https://mlsanity.com/paper/2409.15067/) (2024)

## Abstract (arXiv)

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train models and the privacy concerns of such scenarios limit the applicability of centralized ML/DL-based approaches. Federated learning (FL) addresses these limitations by creating federated and privacy-preserving models. However, FL is vulnerable to malicious participants, and the impact of adversarial attacks on federated models detecting spectrum sensing data falsification (SSDF) attacks on spectrum sensors has not been studied. To address this challenge, the first contribution of this work is the creation of a novel dataset suitable for FL and modeling the behavior (usage of CPU, memory, or file system, among others) of resource-constrained spectrum sensors affected by different SSDF attacks. The second contribution is a pool of experiments analyzing and comparing the robustness of federated models according to i) three families of spectrum sensors, ii) eight SSDF attacks, iii) four scenarios dealing with unsupervised (anomaly detection) and supervised (binary classification) federated models, iv) up to 33% of malicious participants implementing data and model poisoning attacks, and v) four aggregation functions acting as anti-adversarial mechanisms to increase the models robustness.