  Deep learning (DL) has emerged as a crucial tool in network anomaly detection
(NAD) for cybersecurity. While DL models for anomaly detection excel at
extracting features and learning patterns from data, they are vulnerable to
data contamination -- the inadvertent inclusion of attack-related data in
training sets presumed benign. This study evaluates the robustness of six
unsupervised DL algorithms against data contamination using our proposed
evaluation protocol. Results demonstrate significant performance degradation in
state-of-the-art anomaly detection algorithms when exposed to contaminated
data, highlighting the critical need for self-protection mechanisms in DL-based
NAD models. To mitigate this vulnerability, we propose an enhanced auto-encoder
with a constrained latent representation, allowing normal data to cluster more
densely around a learnable center in the latent space. Our evaluation reveals
that this approach exhibits improved resistance to data contamination compared
to existing methods, offering a promising direction for more robust NAD
systems.
