Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity
This work addresses security concerns for cybersecurity applications using Federated Learning, but it is incremental as it focuses on testbed design and evaluation without introducing new mitigation methods.
This paper tackled the problem of evaluating Federated Learning's resilience to poisoning attacks in cybersecurity by designing a testbed using Raspberry Pi and Nvidia Jetson hardware, and found that while FL enhances data privacy, it remains vulnerable to such attacks.
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.