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

Quan, NV, Nguyen, VH, Cao, VL, Le-Khac, N-A and Shone, N (2022) A Robust PCA Feature Selection to Assist Deep Clustering Autoencoder-Based Network Anomaly Detection. In: 20218th NAFOSTED Conference on Information and Computer Science (NICS) . (2021 8th NAFOSTED Conference on Information and Computer Science (NICS) (NICS’21), 21 December 2021 - 22 December 2021, Hanoi, Vietnam).

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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

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 12 Jan 2022 12:26
Last Modified: 13 Apr 2022 15:18
URI: https://researchonline.ljmu.ac.uk/id/eprint/16038
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