A Robust PCA Feature Selection To Assist Deep Clustering Autoencoder-Based Network Anomaly Detection

This paper presents a novel method to enhance the performance of Clustering-based Autoencoder models for network anomaly detection. Previous studies have developed regularized variants of Autoencoders to learn the latent representation of normal data in a semi-supervised manner, including Shrink Aut...

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Vydané v:2021 8th NAFOSTED Conference on Information and Computer Science (NICS) s. 335 - 341
Hlavní autori: Nguyen, Van Quan, Nguyen, Viet Hung, Cao, Van Loi, Khac, Nhien - An Le, Shone, Nathan
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 21.12.2021
Predmet:
Anomaly detection
Benchmark testing
Clustering-based Autoencoders (CAEs)
Computer science
Data models
Deep Learning
Feature extraction
Latent Representation
Principal component analysis
Principal Component Analysis (PCA)
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Shrnutí:This paper presents a novel method to enhance the performance of Clustering-based Autoencoder models for network anomaly detection. Previous studies have developed regularized variants of Autoencoders to learn the latent representation of normal data in a semi-supervised manner, including Shrink Autoencoder, Dirac Delta Variational Autoencoder and Clustering-based Autoencoder. However, there are concerns regarding the feature selection of the original data, which stronger support Autoencoders models exploring more intrinsic, meaningful and latent features at bottleneck. The method proposed involves combining Principal Component Analysis and Clustering-based Autoencoder. Specifically, PCA is used for the selection of new data representation space, aiming to better assist CAE in learning the latent, prominent features of normal data, which addresses the aforementioned concerns. The proposed method is evaluated using the standard benchmark NSL-KDD data set and four scenarios of the CTU13 datasets. The promising experimental results confirm the improvements offered by the proposed approach, in comparison to existing methods. Therefore, it suggests a strong potential application within modern network anomaly detection systems.
DOI:10.1109/NICS54270.2021.9701456