Abstract:
Anomaly detection is crucial in various applications (e.g., cybersecurity, manufacturing, finance, IoT), and an
automatic and reliable anomaly detection tool is necessary for
accurate prediction. The proposed method in this paper focuses
on using deep LSTM Autoencoder on time-series data from IoT
water level sensors deployed on a water catchment. The method
uses unsupervised anomaly detection with deviation methods,
which involves lower-dimensional embeddings and reconstruction
error. The LSTM Autoencoder model includes feature selection
by keeping vital features, and learns the time series’ encoded
representation. The LSTM model is trained for prediction with
three hidden layers based on the encoder’s latent layer output.
Afterwards, given the output from the prediction model if the
reconstruction loss of a data point is greater than reconstruction
error threshold the value will be labeled anomaly. We also
propose and compare an unconventional method of calculating
reconstruction error of each sequence with an aim of reducing
false positives and false negatives then compare it with frequently
used method. The results show that the LSTM Autoencoder
performs well on noisy and real-world datasets for detecting
anomalies and also the proposed unconventional method of
calculating reconstruction loss increases the models accuracy in
identifying anomalies.
