Generative Adversarial Networks (GAN) based Anomaly Detection in Industrial Software Systems

This article explores the application of Generative Adversarial Networks (GANs) in improving subsurface rock analysis. GANs generate realistic images of rock features, enhancing the accuracy and efficiency of estimating rock properties. The objective is to advance the field of subsurface rock analysis, offering insights into the potential of GAN techniques. The article reviews recent developments in data-driven rock reconstruction technology, specifically focusing on GANs. It highlights GANs’ ability to generate and estimate rock features like porosity and permeability. These GAN methods are complemented by discriminators to select or reject features, making them integral to the reconstruction model. The article provides a framework for engineers and researchers to effectively utilize these techniques for rock reconstruction, offering an assessment of their strengths and limitations. Digital rock reconstruction, aided by GANs, has promising implications for site selection and recovery. GANs enhance image resolution, improving the field of view and dynamism of parameters in rock analysis. However, GANs heavily rely on high-quality data, which poses a limitation. To address this, conditional GANs have been proposed. The article comprehensively reviews the latest developments in GAN methods for digital rock reconstruction, offering valuable insights for engineers and researchers using GANs for accurate subsurface rock analysis. It also proposes ways to enhance these methods and advance the field. The novelty of this article lies in its exploration of Generative Adversarial Networks (GANs) in the context of subsurface rock analysis. It emphasizes the potential of GAN techniques to generate realistic rock images and estimate properties like porosity and permeability. Additionally, the article discusses the integration of GANs into the digital rock concept, highlighting their role in improving predictive models and supporting decision-making in the oil and gas industry's resource extraction and production strategies.

Generative adversarial network (GAN) has been extensively applied in many fields since it was first proposed in 2014. There have been ongoing attempts at improving the model and developing its extensions. The basic idea behind GAN stems from the two-player zero-sum game. Composed of a generator and a discriminator, GAN is trained through a “battle” between the two networks, where the generator tries to fool the discriminator. In contrast, the discriminator tries not to be deceived. The purpose is to estimate the potential distribution of data samples and generate new data samples. GAN is currently being studied in computer vision, language processing, and information security and is still far from reaching its potential. Since there are limited existing resources on GAN reviews, this paper provides a comprehensive review of a few of the most important GAN variants. We divided the variants into three categories: foundation, extension, and advanced, gave an overview of each architecture, and analyzed their advantages and disadvantages. We also summarized some of the most popular applications of GAN, and finally, we brought up the problems of GAN-based architectures that have yet to be solved and pointed out the future direction of GAN research and development.

Applications of generative adversarial networks in neuroimaging and clinical neuroscience

Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical equipment. With the rapid development of deep learning technology, the methods based on big data-driven provide a new perspective for the fault diagnosis of machinery. However, mechanical equipment operates in the normal condition most of the time, resulting in the collected data being imbalanced, which affects the performance of mechanical fault diagnosis. As a new approach for generating data, generative adversarial network (GAN) can effectively address the issues of limited data and imbalanced data in practical engineering applications. This paper provides a comprehensive review of GAN for mechanical fault diagnosis. Firstly, the development of GAN-based mechanical fault diagnosis, the basic theory of GAN and various GAN variants (GANs) are briefly introduced. Subsequently, GANs are summarized and categorized from the perspective of labels and models, and the corresponding applications are outlined. Lastly, the limitations of current research, future challenges, future trends and selecting the GAN in the practical application are discussed.

As a promising generative modeling method, Generative Adversarial Networks are a deep-learning-based generative model, in which two networks, namely the generative network and the discriminant network, contest with each other in a game during which the generator “learns” and get trained to be able to fool the discriminator of believing a specific image is (superficially) authentic. It is widely used to generate pictures based on existing data. Based on the MNIST data set, this paper studies and analyzes the application of Generative Adversarial Networks in the generation of handwritten digital images. In addition, by studying the Deep Convolutional Generative Adversarial Network and the Conditional Generation Adversarial Network model, we compared the connections and differences between these three models in training the MNIST dataset to generate handwritten digits algorithms, and gave an optimization method for generating handwritten digits.

ABSTRACTData mining and natural language processing (NLP) are fundamental fields that interact in many ways. Text mining shares many topics, such as sentiment analysis and content understanding. Combining these two fields enables more efficient mining of text data and the extraction of valuable information. In particular, the GAN (Generative Adversarial Network) architecture has achieved success in image generation and has started to be used on text data. However, training GANs is fraught with difficulties due to the complexity of text data. Linguistic studies show important differences between languages. Language is characterized by fluidity, ambiguity, and context‐sensitive interpretations, and text‐generating GAN models can struggle to deal with these complexities. The interaction between data quality, language structure, and complex interpretation can lead to inconsistency and ambiguity in the text production of GAN models. These problems are particularly pronounced when complexities such as semantic subtleties, idiomatic expressions, and context‐dependent usages come into play. Text generation is an area of GAN models used in NLP to generate language and enrich text‐based applications. Work in this area can contribute to analyzing, classifying, and processing text data. Many methods and techniques have been proposed to improve the performance of text GANs. However, some problems may be encountered in the optimization of these methods. Therefore, it is essential to use optimized methods. In conclusion, GANs can be an important tool to improve text generation in NLP. Still, they require continuous research and innovation to deal with factors such as language complexity and data quality.

With the increasing rate and types of cyber attacks against information systems and communication infrastructures, many tools are needed to detect and mitigate against such attacks, for example, Intrusion Detection Systems (IDSs). Unfortunately, traditional Signature‐based IDSs (SIDSs) perform poorly against previously unseen adversarial attacks. Anomaly‐based IDSs (AIDSs) use Machine Learning (ML) and Deep Learning (DL) approaches to overcome these limitations. However, AIDS performance can be poor when trained on imbalanced datasets. To address the challenge of AIDS performance caused by these unbalanced training datasets, generative adversarial models are proposed to obtain adversarial attacks from one side and analyse their quality from another. According to extensive usage and reliability criteria for generative adversarial models in different disciplines, Generative Adversarial Networks (GANs) , Bidirectional GAN (BiGAN) , and Wasserstein GAN (WGAN) are employed to serve AIDS. The authors have extensively assessed their abilities and robustness to deliver high‐quality attacks for AIDS. AIDSs are constructed, trained, and tuned based on these models to measure their impacts. The authors have employed two datasets: NSL‐KDD and CICIDS‐2017 for generalisation purposes, where ML and DL approaches are utilised to implement AIDSs. Their results show that the WGAN model outperformed GANs and BiGAN models in binary and multiclass classifications for both datasets.

In recent years, anomaly detection techniques in time-series data have been widely used in manufacturing, cybersecurity, and other fields. Meanwhile, various anomaly detection models based on generative adversarial networks (GAN) are gradually used in time-series anomaly detection tasks. However, there are problems of unstable generator training, missed detection of anomalous data, and inconsistency between the discriminator's discriminant and the anomaly detection target in GAN networks. Aiming at the above problems, the paper proposes a DUAL-ADGAN (Dual Anomaly Detection Generative Adversarial Networks) model for the detection of anomalous data in time series. First, the Wasserstein distance satisfying the Lipschitz constraint is used as the loss function of the data reconstruction module, which improves the stability of the traditional GAN network training. Second, by adding a data prediction module to the DUAL-ADGAN model, the distinction between abnormal and normal samples is increased, and the rate of missing abnormal data in the model is reduced. Third, by introducing the Fence-GAN loss function, the discriminator is aligned with the anomaly detection target, which effectively reduces the anomaly data false detection rate of the DUAL-ADGAN model. Finally, anomaly scores derived from the DUAL-ADGAN model are compared with dynamic thresholds to detect anomalies. The experimental results show that the average F1 of the DUAL-ADGAN model is 0.881, which is better than the other nine baseline models. The conclusions demonstrate that the DUAL-ADGAN model proposed in the paper is more stable in training while effectively solving the problems of anomaly miss detection and discriminator inconsistency with the anomaly detection target in the anomaly detection task.

In recent years, researchers have introduced various methods in many domains into medical image processing so that its effectiveness and efficiency can be improved to some extent. The applications of generative adversarial networks (GAN) in medical image processing are evolving very fast. In this paper, the state of the art in this area has been reviewed. Firstly, the basic concepts of the GAN were introduced. And then, from the perspectives of the medical image denoising, detection, segmentation, synthesis, reconstruction and classification, the applications of the GAN were summarized. Finally, prospects for further research in this area were presented.

The need for detection, identification and prediction of anomalies emerged just a short time ago. This task brings multiple challenges that are mainly associated with recording and detection of novel attacks and influences. It draws attention of experts in network security and diagnostics of information and industrial systems. Detection of anomalies in dynamic dataflows largely determines the efficiency of management of computer network information security within information and industrial systems. At the same time, the technology of dynamic dataflow prediction is also very important for building systems intended to detect anomalies in data protection within industrial control systems. Any automated systems are based on available computer capabilities and advances in management theory, mathematical modeling and optimizations methods. Processes that occur in industrial automated systems and that are reflected in dataflows being observed constitute complex multi-component processes, thus making it more complicated to predict such processes. In this case, complex multi-component time series data should be predicted at different (short-term, medium-term and long-term) time scales. A neural network architecture matching the structure of the multi-component time series being predicted should be built for generating a multi-component prediction. It is proposed to decompose the complex multi-component time series into several basic components using the digital signal processing technology, i. e. to perform a preliminary structural analysis of multi-component time series within the observed range of all time series that reflect operation of the industrial control system. Separate predictions with different time horizon are formed for each basic component of the multi-component time series using the available neural network architecture and machine learning taking into account dynamic characteristics of the above components. Anomalies in the observed range of multiple time series that reflect operation of the industrial control system are detected (identified) through component-wise comparison of each component (resulted from the above preliminary digital processing) of any time series within the observed range of all time series, with each prediction of the relevant component of the above time series within the observed range of all time series. This approach that implies component-wise comparison will allow to detect anomalies within the range of observed time series of the industrial control system separately by their different dynamic characteristics, and thus will improve the efficiency of management of information security within information and industrial systems.

This research proposes an application of generative adversarial networks (GANs) to solve the class imbalance problem in the fault detection and classification study of a plasma etching process. Small changes in the equipment part condition of the plasma equipment may cause an equipment fault, resulting in a process anomaly. Thus, fault detection in the semiconductor process is essential for success in advanced process control. Two datasets that assume faults of the mass flow controller (MFC) in equipment components were acquired using optical emission spectroscopy (OES) in the plasma etching process of a silicon trench: The abnormal process changed by the MFC is assumed to be faults, and the minority class of Case 1 is the normal class, and that of Case 2 is the abnormal class. In each case, additional minority class data were generated using GANs to compensate for the degradation of model training due to class-imbalanced data. Comparisons of five existing fault detection algorithms with the augmented datasets showed improved modeling performances. Generating a dataset for the minority group using GANs is beneficial for class imbalance problems of OES datasets in fault detection for the semiconductor plasma equipment.

Three-dimensional microstructure reconstruction is indispensable for understanding the performance and reliability of SOFC electrodes. Not only the microstructure parameters, but also SOFC electrochemical characteristics such as overpotentials can be calculated when combined with numerical simulations. The most common method to analyze 3-D structures with a feature size of 0.01 – 10 μm is a focused ion beam - scanning electron microscope (FIB-SEM). However, there’s a tradeoff between reconstruction volume size and resolution. Besides, in order to obtain high quality reconstruction, accumulation of know-hows for pre and post-processing is required. Therefore, fast screening of the microstructures is not easy for FIB-SEM due to the difficulty of measurement. On the other hand, a single SEM image can be obtained very easily. An arising question is if it is possible to extract properties of microstructures and numerically synthesize 3-D model directly from a single 2-D SEM image.In recent years, machine learning techniques have become a powerful tool for image processing. Nevertheless, applications of machine learning in the field of porous materials, in particular fuel cells and batteries are still limited. The previous works concentrated on relatively simple artificial neural networks (ANN), but the class of neural networks that can effectively analyze visual images is the convolutional neural network (CNN). Gayon-Lombardo et al. (1) demonstrated a successful application of generative adversarial network (GAN) based on CNN layers to fabricate synthetic 3-D SOFC microstructures. It is indicated that the GAN-based approach can be very effective, but the algorithm requires 3-D teaching data (GAN3Dto3D).In the scope of this research, a novel method for fabrication of synthetic 3-D microstructure directly from a 2-D SEM image is proposed (Fig. 1A). The GAN2Dto3D network consists of a 3-D microstructure generator and a discriminator which operate on 2-D data. Each microstructure fabricated by the generator is treated as a set of individual cross-sectional 2-D images. The discriminator checks the validity of each 2-D slice in the generated microstructure in all spatial directions (height, width and thickness). Patches extracted from a real 2-D cross-section image are used as training data. In each training iteration, new random patches are extracted to increase the number of available training data. Additionally, the training images are augmented by random horizontal and vertical flipping. As all of the cross-section of the newly generated microstructure has to be statistically equivalent to the real 2-D cross-section, the generated 3-D microstructure is smooth in all spatial directions.The GAN2Dto3D approach is tested on the dataset of real SOFC microstructures. For the real microstructure, a set of nickel (Ni) – gadolinium doped ceria (GDC) microstructures was fabricated. All composite anode samples were prepared with NiO (AGC Seimi, Japan) and GDC (Gd0.1Ce0.9O ShinEtsu, Japan) powders mixed in a designated proportion and sintered at 1350 oC. Differences in the structure are due to the initial anode slurry composition and packing density. The solid phase fractions were controlled by GDC share in the composite in the range of 30 – 70 vol.%, and their porosity was controlled by isostatic pressing (2). 3-D microstructures were reconstructed by FIB-SEM and used as the ground truth data for the validation of GAN2Dto3D model. The results of the GAN2Dto3D algorithm applied to generate two synthetic microstructures with different GDC share and the porosity are shown in Fig. 1B and 1C. The size of the generated microstructures is 64 × 64 × 64 voxels, which is equivalent to 6.4 × 6.4 × 6.4 µm. The real microstructures reconstructed with FIB-SEM are shown for comparison. The proposed GAN algorithm produces realistic microstructures, which indicates that GAN has a great potential to enable fast evaluation of various samples without carrying out complex FIB-SEM measurements.1. Gayon-Lombardo, L. Mosser, N. P. Brandon, and S. J. Cooper, npj Comput. Mater., 6, 1–11 (2020).2. Komatsu, A. Sciazko, and N. Shikazono, J. Power Sources, 485, 229317 (2021). Figure 1

In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multi-channel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images.

We propose a deep-learning approach based on generative adversarial networks (GANs) to reduce noise in weak lensing mass maps under realistic conditions. We apply image-to-image translation using conditional GANs to the mass map obtained from the first-year data of Subaru Hyper Suprime-Cam (HSC) Survey. We train the conditional GANs by using 25 000 mock HSC catalogues that directly incorporate a variety of observational effects. We study the non-Gaussian information in denoised maps using one-point probability distribution functions (PDFs) and also perform matching analysis for positive peaks and massive clusters. An ensemble learning technique with our GANs is successfully applied to reproduce the PDFs of the lensing convergence. About $60{{\ \rm per\ cent}}$ of the peaks in the denoised maps with height greater than 5σ have counterparts of massive clusters within a separation of 6 arcmin. We show that PDFs in the denoised maps are not compromised by details of multiplicative biases and photometric redshift distributions, nor by shape measurement errors, and that the PDFs show stronger cosmological dependence compared to the noisy counterpart. We apply our denoising method to a part of the first-year HSC data to show that the observed mass distribution is statistically consistent with the prediction from the standard ΛCDM model.

Recent advancements in deep learning have led to significant improvements in pneumoconiosis diagnosis from chest X-rays (CXR). However, these models typically require large training datasets, which are challenging to collect due to the rarity of the disease and strict data-sharing limitations. In addition, the process of annotating medical images is labor-intensive, requiring highly skilled radiologists, which further limits data availability. To address this data scarcity, we propose a novel approach to generate synthetic pathology in CXR images, thus augmenting existing datasets and improving model training efficiency. While bidirectional generative adversarial networks (GAN), such as CycleGAN, can perform image translation between domains without paired samples, these methods often struggle to maintain structural and pathological consistency, especially with fine details. This study introduces a multiscale attention-based GAN (MSA-GAN) model that enhances CycleGAN with a multiscale attention generator, local-global discriminators and structural similarity (SSIM) loss, ensuring greater fidelity in preserving structural and pathological details during translation. We utilise MSA-GAN-generated synthetic CXR images to train CNN models for pneumoconiosis classification and lung segmentation tasks. Experimental results indicate that CNN trained on MSA-GAN-generated images outperforms existing CNN-based methods, showing improved accuracy and consistency in both qualitative and quantitative assessments across classification and segmentation tasks.