  In the contemporary digital landscape, the continuous generation of extensive
streaming data across diverse domains has become pervasive. Yet, a significant
portion of this data remains unlabeled, posing a challenge in identifying
infrequent events such as anomalies. This challenge is further amplified in
non-stationary environments, where the performance of models can degrade over
time due to concept drift. To address these challenges, this paper introduces a
new method referred to as VAE4AS (Variational Autoencoder for Anomalous
Sequences). VAE4AS integrates incremental learning with dual drift detection
mechanisms, employing both a statistical test and a distance-based test. The
anomaly detection is facilitated by a Variational Autoencoder. To gauge the
effectiveness of VAE4AS, a comprehensive experimental study is conducted using
real-world and synthetic datasets characterized by anomalous rates below 10\%
and recurrent drift. The results show that the proposed method surpasses both
robust baselines and state-of-the-art techniques, providing compelling evidence
for their efficacy in effectively addressing some of the challenges associated
with anomalous sequence detection in non-stationary streaming data.
