  In recent years, various international security events have occurred
frequently and interacted between real society and cyberspace. Traditional
traffic monitoring mainly focuses on the local anomalous status of events due
to a large amount of data. BGP-based event monitoring makes it possible to
perform differential analysis of international events. For many existing
traffic anomaly detection methods, we have observed that the window-based noise
reduction strategy effectively improves the success rate of time series anomaly
detection. Motivated by this observation, we propose an unsupervised anomaly
detection model, MAD-MulW, which incorporates a multi-window serial framework.
Firstly, we design the W-GAT module to adaptively update the sample weights
within the window and retain the updated information of the trailing sample,
which not only reduces the outlier samples' noise but also avoids the space
consumption of data scale expansion. Then, the W-LAT module based on predictive
reconstruction both captures the trend of sample fluctuations over a certain
period of time and increases the interclass variation through the
reconstruction of the predictive sample. Our model has been experimentally
validated on multiple BGP anomalous events with an average F1 score of over
90\%, which demonstrates the significant improvement effect of the stage
windows and adaptive strategy on the efficiency and stability of the timing
model.
