Data Anomaly Detection in Wireless Sensor Networks Using $\beta$ -Variational Autoencoder

Anomaly detection is the process of identifying data instances that drastically deviate from the majority of data instances. Anomaly detection is a key challenge to ensure the security in Wireless Sensor Networks. The detection of such anomalous data is required to reduce false alarms. The data that is generated from wireless sensor networks have several imbalances. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers. Data imbalance is the major challenge in machine learning models that is resolved in the proposed model using deep learning technique. Deep learning technique proposed is beta variational autoencoders in which a parameter
$\boldsymbol{\beta}$
is included to the KL divergence term of the Variational Autoencoder (VAE)'s loss function. The introduction of the parameter beta provides disentangled representation of data. The proposed VAE model uses multivariate normal distribution instead of normal distribution.