  The significant rise of security concerns in conventional centralized
learning has promoted federated learning (FL) adoption in building intelligent
applications without privacy breaches. In cybersecurity, the sensitive data
along with the contextual information and high-quality labeling in each
enterprise organization play an essential role in constructing high-performance
machine learning (ML) models for detecting cyber threats. Nonetheless, the
risks coming from poisoning internal adversaries against FL systems have raised
discussions about designing robust anti-poisoning frameworks. Whereas defensive
mechanisms in the past were based on outlier detection, recent approaches tend
to be more concerned with latent space representation. In this paper, we
investigate a novel robust aggregation method for FL, namely Fed-LSAE, which
takes advantage of latent space representation via the penultimate layer and
Autoencoder to exclude malicious clients from the training process. The
experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the
feasibility of our defensive mechanism against cutting-edge poisoning attacks
for developing a robust FL-based threat detector in the context of IoT. More
specifically, the FL evaluation witnesses an upward trend of approximately 98%
across all metrics when integrating with our Fed-LSAE defense.
