  Despite achieving good performance and wide adoption, machine learning based
security detection models (e.g., malware classifiers) are subject to concept
drift and evasive evolution of attackers, which renders up-to-date threat data
as a necessity. However, due to enforcement of various privacy protection
regulations (e.g., GDPR), it is becoming increasingly challenging or even
prohibitive for security vendors to collect individual-relevant and
privacy-sensitive threat datasets, e.g., SMS spam/non-spam messages from mobile
devices. To address such obstacles, this study systematically profiles the
(in)feasibility of federated learning for privacy-preserving cyber threat
detection in terms of effectiveness, byzantine resilience, and efficiency. This
is made possible by the build-up of multiple threat datasets and threat
detection models, and more importantly, the design of realistic and
security-specific experiments.
  We evaluate FL on two representative threat detection tasks, namely SMS spam
detection and Android malware detection. It shows that FL-trained detection
models can achieve a performance that is comparable to centrally trained
counterparts. Also, most non-IID data distributions have either minor or
negligible impact on the model performance, while a label-based non-IID
distribution of a high extent can incur non-negligible fluctuation and delay in
FL training. Then, under a realistic threat model, FL turns out to be
adversary-resistant to attacks of both data poisoning and model poisoning.
Particularly, the attacking impact of a practical data poisoning attack is no
more than 0.14\% loss in model accuracy. Regarding FL efficiency, a
bootstrapping strategy turns out to be effective to mitigate the training delay
as observed in label-based non-IID scenarios.
