Clustering-Based Deep Autoencoders for Network Anomaly Detection

Nguyen, Van Du; Hung, Nguyen Viet; Le-Khac, Nhien-An; Cao, Van Loi

doi:10.1007/978-3-030-63924-2_17

Cited by 10 publications

(14 citation statements)

References 13 publications

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“…An autoencoder is a particular type of artificial neural network utilized primarily for handling tasks of unsupervised machine learning [49][50][51]. Like the works in [52][53][54][55][56], this study utilizes the autoencoders for both dimensionality reduction and detecting anomalies. Autoencoder is composed of two components: an encoder and a decoder.…”

Section: The Proposed Deep Autoencoder Neural Network (Dann) Algorithmmentioning

confidence: 99%

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Muneer¹,

Taib²,

Fati³

et al. 2022

Computers, Materials &Amp; Continua

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Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the "curse of dimensionality" that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a real-world gas turbine (GT) dataset that contains 87620 columns and 56 rows. During the experiment, two issues have been investigated and solved to enhance the results. The first is the dataset class imbalance, which solved using SMOTE technique. The second issue is the poor performance, which can be solved using one of the optimization algorithms. Several optimization algorithms have been investigated and tested, including stochastic gradient descent (SGD), RMSprop, Adam and Adamax. However, Adamax optimization algorithm showed the best results when employed to train the DANN model. The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%, F1-score of 0.9649, Area Under the Curve (AUC) rate of 0.9649, and a minimal loss function during the hybrid model training.

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Section: The Proposed Deep Autoencoder Neural Network (Dann) Algorithmmentioning

confidence: 99%

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Muneer¹,

Taib²,

Fati³

et al. 2022

Computers, Materials &Amp; Continua

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“…Deep AEs are a type of neural network, which are designed with purpose of encoding input data into latent and meaningful representations, then decoding them so that they are as similar to the input data as possible [2] [10] [13]. In this subsection, we will present the structure and the loss functions of AE [13] and CAE [23]. They are the important components of our proposed model.…”

Section: B Autoencodermentioning

confidence: 99%

“…However, AEs models are showing prominent efficiency in comparison with other architectures in many circumstances. Therefore, they are the core of most deep learning-based unsupervised models applied to the network anomaly detection problem [7] [6] [23] [11] [9] [21]. In this section, we will discuss the most current and prominent autoencoder-based methods.…”

Section: Existing Workmentioning

confidence: 99%

“…Nguyen et al . [23] introduced a hybrid solution combining clustering methods and AEs for detecting network anomalies in a semi-supervised manner. These combined models were trained using only normal samples.…”

Section: Existing Workmentioning

confidence: 99%

“…One of the widely deployed solutions is to use deep neural network autoencoders, which are trained using only normal data in a one-class training manner [23] [6] [5]. Deep learning neural network autoencoders (AE) have shown to be a very effective and efficient method in building anomaly detection models in many different domains such as network intrusion detection and IoTs Anomaly Detection [7] [28].…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

Hung

Cao

et al. 2021

2021 8th NAFOSTED Conference on Information and Computer Science (NICS)

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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 Clusteringbased 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.

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