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VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection

SENSORS 2024年第18期24卷 6035页

作者： Tian, Wuxin Shen, Yanping Guo, Na Yuan, Jing Yang, Yanqing Inst Disaster Prevent Sch Informat Engn Beijing 101601 Peoples R China Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China

To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This model extends the variational autoencoder Generative Adversarial Network (VAEGAN) by integrating key features from the Auxiliary Classifier Generative Adversarial Network (ACGAN) and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). These enhancements significantly improve both the quality of generated samples and the stability of the training process. By utilizing the VAE-WACGAN model to oversample anomalous data, more realistic synthetic anomalies that closely mirror the actual network traffic distribution can be generated. This approach effectively balances the network traffic dataset and enhances the overall performance of the intrusion detection model. Experimental validation was conducted using two widely utilized intrusion detection datasets, UNSW-NB15 and CIC-IDS2017. The results demonstrate that the VAE-WACGAN method effectively enhances the performance metrics of the intrusion detection model. Furthermore, the VAE-WACGAN-based intrusion detection approach surpasses several other advanced methods, underscoring its effectiveness in tackling network security challenges.

关键词： network security network intrusion detection system (IDS) generative adversarial network variational autoencoder dataset balancing deep learning

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Business Process Anomaly Detection and Root Cause Analysis Using BLSTM-VAE With Attention

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IEEE ACCESS 2024年 12卷 101390-101406页

作者： El-Aziz, Eman Abd Fathalla, Radwa Ismail, Yasser Shaheen, Mohamed Arab Acad Sci Technol & Maritime Transport Coll Comp & IT Alexandria 21625 Egypt Southern Univ & A&M Coll Elect Engn Dept Baton Rouge LA 70807 USA

Detecting anomalous executions in business process data is crucial for safeguarding the efficiency and success of an organization. Unsupervised approaches are commonly used for business process anomaly detection because of the scarcity of labeled anomaly data. However, these approaches often encounter a notable decline in performance because they lack prior knowledge about the anomalies. Additionally, most of them do not perform root cause analysis on the detected anomalies. This study proposes a variational autoencoder-based approach to overcome the performance limitations of existing unsupervised methods and determine the root causes of the detected anomalies. The learning of the variational autoencoder from unlabeled business process data is enhanced in the proposed approach by leveraging different architectural components, namely, the entity embedding technique, the bidirectional long short-term memory network, and the self-attention mechanism. Combining these architectural components in the variational autoencoder architecture leads to learning high-level representations from the business process data and thus improving the reconstruction capability of the variational autoencoder. Furthermore, this study suggests feeding the reconstruction error provided by the variational autoencoder into the logistic regression classifier to improve the accuracy of anomaly detection. The performance of the proposed model was evaluated on real-life and synthetic datasets. The experimental findings indicate that the proposed model outperforms six existing anomaly detection models in terms of precision, recall, and F1-score metrics.

关键词： Anomaly detection Root cause analysis Data models Analytical models Long short term memory Information systems Business process management bidirectional long short-term memory business process root cause analysis entity embedding logistic regression process-aware information systems self-attention variational autoencoder

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Intra-pulse modulation discrimination using a self-supervised attention-driven CNN-BiLSTM-VAE combination

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PHYSICAL COMMUNICATION 2024年 67卷

作者： Sharma, Purabi Sarma, Kandarpa Kumar Gauhati Univ Dept Elect & Commun Engn Gauhati Assam India

Identification of intra-pulse modulation (IPM) of radar signals is a crucial part of contemporary electronic support systems and electronic intelligence reconnaissance. Artificial intelligence (AI)-based methods can be very effective in recognising the IPM of radar signals. In this direction, an automatic method is proposed for recognising a few IPMs of radar signals based on continuous wavelet transform (CWT) and a hybrid model of self-attention (SA)-aided convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Firstly, time-frequency attributes of different radar signals are obtained using CWT, and thereafter CNN-SA-BiLSTM is utilised for feature extraction from the 2D scalograms formed by the time-frequency components. The CNN extracts features from the scalograms, SA enhances the discriminative power of the feature map, and BiLSTM detects radar signals based on these features. Additionally, the study addresses real-world data imbalance issues by incorporating a generative AI model, namely the variational autoencoder (VAE). The VAE-based approach effectively mitigates challenges arising from data imbalance situations. This method is tested at varying noise levels to give a proper representation of the actual electronic warfare environment. The simulation results demonstrate that the best overall recognition accuracy of the proposed method is 98.4%, even at low signal-to-noise ratios (SNR).

关键词： Intra-pulse modulation CNN Self-attention BiLSTM variational autoencoder

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Knowledge-embedding deep interpretable graph model for wear prediction: Application in pantograph-catenary systems

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 261卷

作者： Mo, Yutao Peng, Yizhen Wu, Jing Fan, Kangbo Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Rail Transit Grp Co LTD Chongqing 400042 Peoples R China

The wear modeling and life prediction of pantographs are crucial for ensuring the safety and reliability of urban rail transit systems. However, because of the complex interplay between stochastic vibrations and electrical currents, pantograph wear exhibits strong variability, and physics-based degradation predictions based solely on material parameters, environmental factors, and wear mechanisms are limited in accuracy. Purely data-driven approaches, on the other hand, are constrained by their reliance on large datasets and lack of interpretability, making them difficult to meet practical engineering needs. To address these challenges, we propose an interpretable variational model called IV-NBEATS. This study integrates the surface wear mechanism under the asperity hypothesis into the N-BEATS model using the projection principle, thereby enhancing the interpretability of the model. In addition, we introduce a method for describing the uncertainty of key wear parameters, enabling a deep network to represent the uncertainty of these parameters. Furthermore, to cope with dynamic changes in wear system parameters, we propose a dynamic updating method based on a Bayesian directed graph model that effectively overcomes the limitations of existing methods in capturing the temporal evolution of wear system parameters. Finally, the effectiveness of the proposed approach is demonstrated through the analysis of a real-world case study of pantograph wear.

关键词： Pantograph wear N -BEATS variational autoencoder Interpretability

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DiffuseVAE plus plus : Mitigating training-sampling mismatch based on additional noise for higher fidelity image generation

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NEUROCOMPUTING 2025年 633卷

作者： Yang, Xiaobao Luo, Wei Ning, Hailong Zhang, Guorui Sun, Wei Ma, Sugang Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China

Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated remarkable results in image generation. However, there exist a mismatch between the training and sampling process in current diffusion models, in addition, the U-Net denoising network based on simple residual blocks cannot predict noise information accurately, which affects the generated quality. To address these limitations, we present a novel image generation method that achieves higher fidelity. First, by additionally adding the standard Gaussian noise in the diffusion forward process, which does not disrupt the forward process, our method alleviates the mismatch. Subsequently, an important efficient denoising network based on U-Net is presented, where our proposed Simple Squeeze-Excitation and Simple GLU, combined with Depthwise Separable Convolution, enhance the ability of the model to predict real noise using the Simplified Nonlinear No Activation (SNNA) block. Furthermore, considering the structural characteristics of the baseline model, we introduce an additional cross-attention mechanism to enable DDPM to focus on VAE stage characteristics. Allowing the model to more accurately capture and learn the noise information. Finally, it is shown after extensive experiments the proposed DiffuseVAE++ obtains significant gains in FID scores, improving from 3.84 to 2.41 on CIFAR-10 and from 3.94 to 2.30 on CelebA-64. In particular, the IS scores on CIFAR-10 reaches 10.10, which is comparable to the current state-of-the-art methods competitively (e.g., U-ViT, StyleGAN2).

关键词： Image generation Generative model Diffusion model variational autoencoder Unconditional generation

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Generative-based hybrid model with semantic representations for generalized zero-shot learning

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-12页

作者： Akdemir, Emre Barisci, Necaattin Gazi Univ Grad Sch Informat Dept Comp Sci Ankara Turkiye Minist Natl Educ Directorate Gen Informat Technol Ankara Turkiye Gazi Univ Fac Technol Dept Comp Engn Ankara Turkiye

Generalized Zero-Shot Learning (GZSL) endeavors to recognize instances of seen and unseen classes using semantic information and labeled instances of only seen classes. In addressing the data imbalance problem in GZSL, generator networks produce synthetic features out of visual features and semantic features. However, the original semantic features exhibit insufficient distinctiveness. Throughout the current study, a hybrid generative-based GZSL framework that combines semantic descriptions used in fine-grained data sets with semantic attributes is proposed to increase the distinctiveness of the original semantic features. The proposed model offers semantic information enriched by the use of semantic attributes and semantic descriptions. A generalized classification is provided by using Generative Adversarial Network (GAN) and variational autoencoder (VAE) to generate visual features of unseen classes from this semantic information. The proposed method has been evaluated through extensive experiments on AWA2, CUB, FLO and SUN GZSL benchmark datasets. The experiments showed AWA2 70.96, CUB 76.71, FLO 92.12 and SUN 42.84% performance. Notably the proposed method achieved superior GZSL results, 3.41% and 3.52% on fine-grained CUB and FLO datasets respectively.

关键词： Generalized zero-shot learning Semantic representation variational autoencoder Generative adversarial network

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Uncertainty quantification for variational Bayesian dropout based deep bidirectional LSTM networks

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2025年 1-18页

作者： Sardar, Iqra Noor, Farzana Iqbal, Muhammad Javed Alsanad, Ahmed Akbar, Muhammad Azeem Int Islamic Univ Dept Math & Stat Islamabad 44000 Pakistan Univ Engn & Technol Taxila Dept Comp Sci Taxila 47050 Pakistan King Saud Univ Coll Comp & Informat Sci STCs Artificial Intelligence Chair Dept Informat Syst Riyadh 11451 Saudi Arabia Lappeenranta Lahti Univ Technol Software Engn Dept Lappeenranta 53851 Finland

Time series classification is a critical task in various domains, requiring robust models to handle inherent uncertainties in temporal data. These uncertainties, categorized as aleatoric and epistemic, pose significant challenges in achieving accurate predictions. In real-world applications, models often encounter unseen data that were not present during training process. Bayesian inference has been widely utilized for uncertainty quantification in statistics and machine learning. In this study, we proposed a Bayesian Deep Bi-LSTM model incorporating variational Bayesian dropout with a Gaussian prior and variational autoencoder (VAE). The proposed technique efficiently handles uncertainty in both the model and data while VAE reducing the dimensionality of model parameters. We apply this framework to univariate time series datasets from the UCR repository and compare its performance with four traditional machine learning methods and four sequential deep learning models. Experimental results demonstrate that the Bayesian deep Bi-LSTM model effectively improves overall classification performance. In particular, the model benefits significantly from data augmentation using SMOTE when handling imbalanced dataset. The variational Bayesian dropout model exhibits lower total uncertainty across both datasets, indicating more stable and reliable predictions compared to the VAE-based model. Future research should explore additional datasets from the UCR repository and investigate advanced uncertainty modeling techniques to further enhance performance and scalability.

关键词： Time series classification Bayesian deep Bi-LSTM Aleatoric and epistemic uncertainty variational Bayesian dropout variational autoencoder

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Dynamical mode recognition of coupled flame oscillators by supervised and unsupervised learning approaches

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KNOWLEDGE-BASED SYSTEMS 2025年 307卷

作者： Xu, Weiming Yang, Tao Zhang, Peng City Univ Hong Kong Dept Mech Engn Kowloon Tong Kowloon Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hung Hong Kowloon Hong Kong 999077 Peoples R China

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential for ensuring the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core task. To facilitate fundamental studies, the present work concerned dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators were generated through fully validated reacting flow simulations. Due to the limitations of expertise-based models, a data-driven approach was adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) was used to project the high dimensional data onto a 2-dimensional latent space. Based on phase trajectories in the latent space, both supervised and unsupervised classifiers were proposed for datasets with and without well-known labeling, respectively. For labeled datasets, we established the Wassersteindistance-based classifier (WDC) for mode recognition;for unlabeled datasets, we developed a novel unsupervised classifier (GMM-DTW) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance across seven assessment metrics for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition in complex combustion problems.

关键词： variational autoencoder Dynamical mode recognition Coupled flames Wasserstein distance Dynamic time warping

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Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split variational Adversarial Training

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2024年第4期42卷 1-28页

作者： Cheng, Jiezhu Huang, Kaizhu Zheng, Zibin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Higher Educ Mega Ctr 132 East Outer Ring Rd Guangzhou 510006 Guangdong Peoples R China Duke Kunshan Univ Data Sci Res Ctr 8 Duke Ave Suzhou 215316 Jiangsu Peoples R China Sun Yat Sen Univ Sch Software Engn Zhuhai 519082 Guangdong Peoples R China

In the stock market, a successful investment requires a good balance between profits and risks. Based on the learning to rank paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for investors. Despite the efforts to make profits, many existing recommendation approaches still have some limitations in risk control, which may lead to intolerable paper losses in practical stock investing. To effectively reduce risks, we draw inspiration from adversarial learning and propose a novel Split variational Adversarial Training (SVAT) method for risk-aware stock recommendation. Essentially, SVAT encourages the stock model to be sensitive to adversarial perturbations of risky stock examples and enhances the model's risk awareness by learning from perturbations. To generate representative adversarial examples as risk indicators, we devise a variational perturbation generator to model diverse risk factors. Particularly, the variational architecture enables our method to provide a rough risk quantification for investors, showing an additional advantage of interpretability. Experiments on several real-world stock market datasets demonstrate the superiority of our SVAT method. By lowering the volatility of the stock-recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits. All the experimental data and source code are available at https://***/drive/folders/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing

关键词： Stock recommendation risk control variational autoencoder

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An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 239卷

作者： Yao, Changqing Nagao, Masahiro Datta-Gupta, Akhil Mishra, Srikanta Texas A&M Univ College Stn TX 77843 USA Battelle Mem Inst 505 King Ave Columbus OH 43201 USA

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation;second, we use variational autoencoderdecoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration. The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatu

关键词： CO 2 plume visualization Time -of -Flight Deep learning variational autoencoder

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