Analyzing the Effect of Attention Functions in Autoencoder-Based Network Anomaly Detection

This work designs and implements several convolutional variational autoencoder (CVAE) architectures augmented with squeeze-and-excitation and Convolutional Block attention modules placed at various depths and locations in the encoderdecoder pipeline, in order to evaluate the impact of these elements on semi-supervised network anomaly detection performance, together with their computational burden. The main goal is to understand whether these architectural modules meaningfully enhance CVAE-based anomaly detection capabilities in realistic settings and what is their most favorable combination for achieving the best results. We hope that this systematic testing effort can contribute to the advancement of practical utilization of attention functions in network security monitoring.