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Anomaly-Based Intrusion Detection From Network Flow Features Using variational autoencoder

IEEE ACCESS 2020年 8卷 108346-108358页

作者： Zavrak, Sultan Iskefiyeli, Murat Sakarya Univ Dept Comp & Informat Engn TR-54187 Sakarya Turkey Duzce Univ Dept Comp Engn TR-81620 Duzce Turkey Sakarya Univ Dept Comp Engn TR-54187 Sakarya Turkey

The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, autoencoder and variational autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that variational autoencoder performs, for the most part, better than autoencoder and One-Class Support Vector Machine.

关键词： Intrusion detection Feature extraction Telecommunication traffic Deep learning Support vector machines Anomaly detection Computer hacking Flow anomaly detection intrusion detection deep learning variational autoencoder semi-supervised learning

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Text feature extraction based on stacked variational autoencoder

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MICROPROCESSORS AND MICROSYSTEMS 2020年 76卷

作者： Che, Lei Yang, Xiaoping Wang, Liang Renmin Univ China Sch Informat Beijing 100872 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat Management Beijing 100192 Peoples R China

This paper presents a text feature extraction model based on stacked variational autoencoder (SVAE). A noise reduction mechanism is designed for variational autoencoder in input layer of text feature extraction to reduce noise interference and improve robustness and feature discrimination of the model. Three kinds of deep SVAE network architectures are constructed to improve ability of representing learning to mine feature intension in depth. Experiments are carried out in several aspects, including comparative analysis of text feature extraction model, sparse performance, parameter selection and stacking. Results show that text feature extraction model of SVAE has good performance and effect. The highest accuracy of SVAE models of Fudan and Reuters datasets is 13.50% and 8.96% higher than that of PCA, respectively. (C) 2020 Elsevier B.V. All rights reserved.

关键词： variational autoencoder Text feature extraction Deep stack Noise reduction

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3D landmark-based face restoration for recognition using variational autoencoder and triplet loss

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IET BIOMETRICS 2021年第1期10卷 87-98页

作者： Sharma, Sahil Kumar, Vijay Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India Natl Inst Technol Comp Sci & Engn Dept Hamirpur India

Restoration of a 3D face from the mesh image is highly demanded in computer vision applications. 3D face restoration is a challenging task due to the variation of expression, poses, intrinsic geometries, and textures. The proposed technique consists of two main components, namely face restoration and recognition. A novel three-dimensional (3D) landmark-based face restoration method is proposed. 3D facial landmarks are used in the face recognition technique. It uses the principle of reflection and mid-face plane for the restoration of facial landmarks. By using the restored 3D face, a deep learning-based face recognition system is developed. It utilizes the concept of deep features from variational autoencoders. Further, these deep feature embeddings are trained using triplet loss training to increase the distance between embeddings of different persons and decreasing the distance between embeddings of the same person. These trained embeddings are used in support vector machine for prediction. The proposed framework is compared with recently developed face recognition techniques in terms of computational time. The proposed technique is able to recognize the person's face with better accuracy than the existing methods. Further, ablation studies are conducted to test the robustness of the proposed technique.

关键词： face recognition techniques computer vision applications Image recognition three-dimensional landmark-based face restoration method facial landmarks reflection deep learning-based face recognition system triplet loss training mid-face plane variational autoencoder 3D face restoration face recognition restored 3D face computer vision Computer vision and image processing techniques trained embeddings deep feature embeddings image representation variational autoencoders 3D landmark-based face restoration person learning (artificial intelligence)

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Predicting the quality of a machined workpiece with a variational autoencoder approach

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第2期34卷 719-737页

作者： Proteau, Antoine Tahan, Antoine Zemouri, Ryad Thomas, Marc Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada HESAM Univ CEDRIC Lab Conservatoire Natl Arts & Metiers CNAM F-750141 Paris 03 France

In this article, it is shown that a machine learning approach based only on data from sensors (vibration and current consumption) can be used to predict the geometric dimensioning and tolerancing quality measurement values of machined workpieces in an industrial context. First, a methodology based on a variational autoencoder approach is used, and then a metric based on the concept of Euclidean distance and the 2D latent space produced by the variational autoencoder is proposed. The proposed variational autoencoder regression model is shown capable of predicting the quality measurement values, with a mean square error of 5.2573 x 10(-4) mm. The proposed measurement system also displays a confidence interval of +/- 0.05 mm. Moreover, the resulting 2D latent space is capable of distributing and structuring data based on the quality level and of providing a quick visual support. Compared to the t-SNE method, this latent space displays a better structure. Furthermore, the proposed Euclidean distance metric is correlated to the quality level in both the predicted and observed subsets. This work is also based on an industrial dataset, thus increasing its potential for technological transfer;that in turn allows a better monitoring of the machining process, as well as the prediction of the workpiece quality.

关键词： variational autoencoder Geometric dimensioning and tolerancing 2D-visualization Prognostic Machining process

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Remote sensing image captioning via variational autoencoder and Reinforcement Learning

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KNOWLEDGE-BASED SYSTEMS 2020年 203卷

作者： Shen, Xiangqing Liu, Bing Zhou, Yong Zhao, Jiaqi Liu, Mingming China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Beijing Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Jiangsu Vocat Inst Architectural Technol Sch Intelligent Mfg Xuzhou 221008 Jiangsu Peoples R China Jiangsu Normal Univ Sch Mechatron Engn Xuzhou 221008 Jiangsu Peoples R China

Image captioning, i.e., generating the natural semantic descriptions of given image, is an essential task for machines to understand the content of the image. Remote sensing image captioning is a part of the field. Most of the current remote sensing image captioning models suffered the overfitting problem and failed to utilize the semantic information in images. To this end, we propose a variational autoencoder and Reinforcement Learning based Two-stage Multi-task Learning Model (VRTMM) for the remote sensing image captioning task. In the first stage, we finetune the CNN jointly with the variational autoencoder. In the second stage, the Transformer generates the text description using both spatial and semantic features. Reinforcement Learning is then applied to enhance the quality of the generated sentences. Our model surpasses the previous state of the art records by a large margin on all seven scores on Remote Sensing Image Caption Dataset. The experiment result indicates our model is effective on remote sensing image captioning and achieves the new state-of-the-art result. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Transformer variational autoencoder Transfer learning Remote sensing image captioning Self-attention mechanisms Convolutional neural network Reinforcement learning

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Non-Invasive Detection of Rotor Inter-Turn Short Circuit of a Hydrogenerator Using AI-Based variational autoencoder

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第1期60卷 28-37页

作者： Ibrahim, Rony Zemouri, Ryad Kedjar, Bachir Merkhouf, Arezki Tahan, Antoine Al-Haddad, Kamal Lafleur, Francois Ecole Technol Super Montreal PQ H3C 1K3 Canada Ctr Rech Hydroquebec Varennes PQ J3X 1S1 Canada

This article presents a non-intrusive technique for detecting the rotor inter-turn short circuit (RITSC) of a hydrogenerator using an artificial intelligence (AI) based variational autoencoder (VAE). The technique is applied to a large hydrogenerator of 74 MVA and 76 poles, to test its health monitoring and classification potential. The model is trained and validated based on the acquisition of real vibratory data collected in situ from a healthy machine. The frequency pattern of the fault in the vibration signal is obtained based on finite element methods (FEM). Then, to test the sensitivity of the model in early fault detection, the signature is injected into another set of real healthy vibration signals, and the results are compared to those obtained using the traditional vibration monitoring technique. Furthermore, clustering in the latent space of the model is explored. The obtained results prove the ability of this technique and its potential in detecting anomalies at earlier stages as well as its capacity to cluster different degrees of severity of the fault in a 3D user-friendly space.

关键词： Vibrations Circuit faults Monitoring Finite element analysis Stator windings Integrated circuit modeling Atmospheric modeling Diagnosis fault detection hydrogenerators monitoring non-invasive rotor inter-turn short circuit variational autoencoder

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Data-driven nonlinear parametric model order reduction framework using deep hierarchical variational autoencoder

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ENGINEERING WITH COMPUTERS 2024年第4期40卷 2385-2400页

作者： Lee, Sihun Lee, Sangmin Jang, Kijoo Cho, Haeseong Shin, Sangjoon Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea Jeonbuk Natl Univ Dept Aerosp Engn Jeonju 54896 South Korea Seoul Natl Univ Inst Adv Aerosp Technol Seoul 08226 South Korea

A data-driven parametric model order reduction (MOR) method using a deep artificial neural network is proposed. The present network, a least-squares hierarchical variational autoencoder (LSH-VAE), is capable of performing nonlinear MOR for the parametric interpolation of a nonlinear dynamic system with a significant number of degrees of freedom. LSH-VAE differs from existing networks in two major respects: a deep hierarchical structure and a hybrid weighted, probabilistic loss function. The enhancements result in significantly improved accuracy and stability compared with conventional nonlinear MOR methods, autoencoders, and variational autoencoders. In LSH-VAE, the parametric MOR framework is based on the spherically linear interpolation of the latent manifold. The present framework is validated and evaluated on three nonlinear and multiphysics dynamic systems. First, the present framework is evaluated on the fluid-structure interaction benchmark problem to assess its efficiency and accuracy. Then, a highly nonlinear aeroelastic phenomenon, limit cycle oscillation, is analyzed. Finally, the framework is applied to three-dimensional fluid flow to demonstrate its capability to efficiently analyze a significantly large number of degrees of freedom. The superior performance of LSH-VAE is emphasized by comparing its results against those of widely used nonlinear MOR methods, a convolutional autoencoder, and beta-VAE. The proposed framework exhibits significantly enhanced accuracy compared with that of conventional methods, while the computational efficiency continues to remain significantly higher.

关键词： Machine learning Nonlinear model order reduction Multiphysics analysis variational autoencoder Parametric interpolation

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Customization of latent space in semi-supervised variational autoencoder

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PATTERN RECOGNITION LETTERS 2024年 177卷 54-60页

作者： An, Seunghwan Jeon, Jong-June Univ Seoul Dept Stat 163 Seoulsiripdaero Seoul 02504 South Korea

We propose a novel semi-supervised learning method of variational autoencoder (VAE), which yields a customized latent space through our EXplainable encoder Network (EXoN). The customization involves a manual design of the interpolation and structural constraint, such as proximity, which enhances the interpretability of the latent space. To improve the classification performance, we introduce a new semi supervised classification method called SCI (Soft-label Consistency Interpolation). Combining the classification loss and the Kullback-Leibler divergence is crucial in constructing an explainable latent space. Additionally, the variability of the generated samples is determined by an active latent subspace, which effectively captures distinctive characteristics. We conduct experiments using the MNIST, SVHN, and CIFAR-10 datasets, and the results demonstrate that our approach yields an explainable latent space while significantly reducing the effort required to analyze representation patterns within the latent space.

关键词： variational autoencoder Explainable latent space Semi-supervised Customization

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Chiller fault diagnosis based on improved variational autoencoder and co-training framework: A case study of insufficient samples

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JOURNAL OF BUILDING ENGINEERING 2024年 88卷

作者： Lu, Cheng Ma, Xiang Yan, Ke China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Hunan Univ Mech & Elect Engn Changsha 410006 Peoples R China

Implementing efficient automatic fault diagnosis is critical for saving energy and minimizing financial losses in the heating ventilation air-conditioning (HVAC) systems of commercial buildings. However, the limited quantity and weak features of fault samples acquired during HVAC operations hinder the effectiveness of conventional machine learning -based fault diagnosis methodologies. This paper proposes a method based on an improved conditional variational autoencoder (MCVAE) and co -training (CT) ideology to address the issue of insufficient training samples. Initially, we employ MCVAE to synthesize an extensive dataset of chiller fault samples from the original training dataset. Subsequently, the beneficial samples for training our fault diagnostic classifier, namely high -quality samples, are selected from the generated dataset using the CT -based framework. Finally, the selected high -quality samples are merged into the original training dataset to train the ultimate fault classifiers. Experimental results demonstrate that our proposed method outperforms in effectiveness and efficiency compared to recently published methods. For instance, in the case of fault level 1 compared to the suboptimal model, our approach exhibits improvements of 2.41% when each type has 5 fault samples.

关键词： Fault diagnosis variational autoencoder Chiller Data augmentation

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Non-Parallel Whisper-to-Normal Speaking Style Conversion Using Auxiliary Classifier variational autoencoder

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IEEE ACCESS 2023年 11卷 44590-44599页

作者： Seki, Shogo Kameoka, Hirokazu Kaneko, Takuhiro Tanaka, Kou NTT Corp NTT Commun Sci Labs Atsugi Kanagawa 2430198 Japan

This paper is concerned with non-parallel whisper-to-normal speaking-style conversion (W2N-SC), which converts whispered speech into normal speech without using parallel training data. Most relevant to this task is voice conversion (VC), which converts one speaker's voice to another. However, the W2N-SC task differs from the regular VC task in three main respects. First, unlike normal speech, whispered speech contains little or no pitch information. Second, whispered speech usually has significantly less energy than normal speech and is therefore more susceptible to external noise. Third, in the actual usage scenario of W2N-SC, users may suddenly switch voice modes from whispered to normal speech, or vice versa, meaning that the speaking-style of input speech cannot be assumed in advance. To clarify whether existing VC techniques can successfully handle these task-specific concerns and how they should be modified to better address them, we consider a variational autoencoder (VAE)-based VC method as a baseline and examine what modifications to this method would be effective for the current task. Specifically, we study the effects of 1) a self-supervised training scheme called filling-in-frames (FIF);2) data augmentation (DA) using noisy speech samples;and 3) an architecture that allows for any-to-many conversions. Through experimental evaluation of the W2N-SC and speaker conversion tasks, we confirmed that, especially in the W2N-SC task, the version incorporating the above modifications works better than the baseline VC model applied as is.

关键词： Task analysis Training Decoding Speech Noise measurement Training data Generators Voice conversion whisper-to-normal speaking style conversion variational autoencoder self-supervised learning data augmentation

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