Abstract
Anomaly detection is a very critical and significant data analysis mission given the raft of cyber-attacks these days. Used to identify thought-provoking and emerging patterns, predispositions, and irregularities in the data, it is an important tool to perceive abnormalities in many different domains, including security, finance, power automation, health, computer network intrusion detection, etc. Deep learning-based AutoEncoders have shown great potential in identifying anomalies. However, state-of-the-art anomaly scores are still based on reconstruction errors, which do not take advantage of the available anomalous data samples during the training phase. Towards this direction, we demonstrate a novel extension to the AutoEncoder that not only maintains the AutoEncoder’s ability to discover non-linear features of non-anomalies but also uses the existing anomalous samples to assist in learning the features of the data better. Since the model architecture is designed to have two encoders and one decoder network, we name our model as Bifold-Encoder Uni-Decoder (BEUD) network. In this paper, we discuss two different ways of using the BEUD model to predict the anomalies in the data. BEUD is conceptually analogous to AutoEncoder but empirically more powerful. The experimental results of this architecture demonstrated a fairly good performance compared to the standard AutoEncoder architecture evaluated for the anomaly detection task.
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Rajesh, M., Kulkarni, C., Shylaja, S.S. (2022). BEUD: Bifold-Encoder Uni-Decoder Based Network for Anomaly Detection. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_3
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DOI: https://doi.org/10.1007/978-3-031-08974-9_3
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