Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity

TL;DR

This paper develops a federated learning testbed for cybersecurity, demonstrating its capabilities and vulnerabilities against poisoning attacks, and providing a platform for further robustness research.

Contribution

It introduces a practical FL testbed using Raspberry Pi and Nvidia Jetson hardware for cybersecurity applications, enabling evaluation of performance and poisoning resilience.

Findings

The testbed effectively detects anomalies in intrusion detection scenarios.

Federated learning improves data privacy in cybersecurity applications.

Poisoning attacks significantly impact model robustness, highlighting the need for mitigation strategies.

Abstract

This paper presents the design and implementation of a Federated Learning (FL) testbed, focusing on its application in cybersecurity and evaluating its resilience against poisoning attacks. Federated Learning allows multiple clients to collaboratively train a global model while keeping their data decentralized, addressing critical needs for data privacy and security, particularly in sensitive fields like cybersecurity. Our testbed, built using Raspberry Pi and Nvidia Jetson hardware by running the Flower framework, facilitates experimentation with various FL frameworks, assessing their performance, scalability, and ease of integration. Through a case study on federated intrusion detection systems, the testbed's capabilities are shown in detecting anomalies and securing critical infrastructure without exposing sensitive network data. Comprehensive poisoning tests, targeting both model and data integrity, evaluate the system's robustness under adversarial conditions. The results show that while federated learning enhances data privacy and distributed learning, it remains vulnerable to poisoning attacks, which must be mitigated to ensure its reliability in real-world applications.