A Dual-Decoder Variational Auto-Encoder for Anomaly Detection

Anomaly detection aims to identify patterns and events that deviate from the norm. However, current methods struggle to achieve high detection accuracy due to data complexity, i.e., imbalance and particularly hidden features not directly observed in the raw data. To address this, we propose a novel...

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Vydané v:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 1 - 6
Hlavní autori: Dinh, Phai Vu, Nguyen, Diep N., Thai, Hoang Dinh, Nguyen, Quang Uy, Bao, Son Pham, Dutkiewicz, Eryk
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 24.03.2025
Predmet:
Accuracy
Analytical models
Anomaly detection
Benchmark testing
Complexity theory
Data models
Decoding
Feature extraction
generative models
neural network architecture
Neural networks
Stochastic processes
ISSN:1558-2612
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Shrnutí:Anomaly detection aims to identify patterns and events that deviate from the norm. However, current methods struggle to achieve high detection accuracy due to data complexity, i.e., imbalance and particularly hidden features not directly observed in the raw data. To address this, we propose a novel neural network architecture/model, called Dual-Decoder Variational Auto-Encoder (DDVAE) which consists of an encoder and two decoders. The encoder maps the input data into the extended latent space, where the dimensionality is greater than that of the input data, providing more room to capture the relationship among features. The data in the extended latent space of DDVAE is modelled based on the distribution of the normal samples to generate stochastic latent variables before they are fed into the first decoder to reconstruct the input data. After that, reconstructed data at the output of the first decoder are used to reconstruct the extended latent space, in which anomalies produce higher reconstruction errors than normal samples when reconstructing both the input data and the extended latent space. The Area Under the ROC Curve (AUC) obtained by DDVAE is significantly greater than that of conventional non-parametric methods and generative models on eight benchmark anomaly datasets, by up to 3.9%. DDVAE also achieves an average miss detection rate of 16.9%. We observe that DDVAE achieves high AUC in cases of a low rate of anomalies compared to normal samples,
ISSN:1558-2612
DOI:10.1109/WCNC61545.2025.10978209