The growing complexity and heterogeneity of modern network traffic pose a signif- icant challenge to anomaly detection in cybersecurity. Traditional models often fail to generalize across datasets with differing distributions and feature spaces, resulting in limited robustness when applied to unseen environments. This thesis proposes a unified framework for network anomaly detection that leverages multiple datasets to build a generalizable classification model. The proposed approach utilizes AutoEncoders (AEs) to transform multiple datasets into a common feature space, thereby enabling their integration. We train an independent AE on each dataset to learn a compact, latent representation of its specific traffic patterns (both normal and anomalous).