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Securing VANETs: Multi-Objective Intrusion Detection With variational autoencoders

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3867-3874页

作者： Nissar, Nabil Naja, Najib Jamali, Abdellah Inst Natl Postes Telecommun RAISS Lab Rabat 10000 Morocco Inst Natl Postes Telecommun Rabat 10000 Morocco Hassan I Univ Settat 26002 Morocco

The rising prevalence of connected vehicles in Vehicular Ad-hoc Networks (VANETs) within Intelligent Transportation Systems (ITS) has introduced a heightened susceptibility to various cyber threats, particularly zero-day attacks. The magnitude of intercommunicating vehicles compounds the difficulty of identifying dynamic and spatially-distant anomalies that elude static rules. Existing solutions, often rule-bound and inflexible, face challenges in dealing with novel threats. Hence, the diverse and evolving nature of potential threats underscores the necessity for more adaptive and robust detection frameworks, especially when considering the end consumer's safety and privacy *** paper introduces an innovative anomaly detection framework designed for VANETs. It employs a variational autoencoder (VAE) and optimizes multiple objectives: divergence, KL-divergence, and reconstruction error, using the AGE-MOEA and R-NSGA-III algorithms. Deployable within Roadside Units, it captures and analyzes broadcast vehicular data sequences, classifying messages as anomalous or normal to address not only the technological intricacies but also the paramount concern of consumer safety within the vehicular ecosystem. Experiments involve exploring various hyperparameters, with performance assessed using key metrics. The proposed framework undergoes comprehensive benchmarking against prior research, considering accuracy, precision, recall, and ROC-AUC. This underscores the efficacy of fully unsupervised learning and multi-objective optimization in enhancing VANETs security against emerging threats.

关键词： Consumer electronics VANETs security intrusion detection systems anomaly detection NSL-KDD artificial intelligence deep learning unsupervised learning variational autoencoder AGE-MOEA R-NSGA-III

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Machine Learning-Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using variational autoencoders

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JOURNAL OF POLYMER SCIENCE 2025年第5期63卷 1095-1107页

作者： Teimouri, Amir Li, Guoqiang Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

The discovery of high-performance shape memory polymers (SMPs) with enhanced glass transition temperatures (Tg) is of paramount importance in fields such as geothermal energy, oil and gas, aerospace, and other high-temperature applications, where materials are required to exhibit shape memory effect at extremely high-temperature conditions. Here, we employ a novel machine learning framework that integrates transfer learning with variational autoencoders (VAE) to efficiently explore the chemical design space of SMPs and identify new candidates with high Tg values. We systematically investigate the effect of different latent space dimensions on the VAE model performance. Several machine learning models are then trained to predict Tg. We find that the SVM model demonstrates the highest predictive accuracy, with R2 values exceeding 0.87 and a mean absolute percentage error as low as 6.43% on the test set. Through systematic molar ratio adjustments and VAE-based fingerprinting, we discover novel SMP candidates with Tg values between 190 degrees C and 200 degrees C, suitable for high-temperature applications. These findings underscore the effectiveness of combining VAEs and SVM for SMP discovery, offering a scalable and efficient method for identifying polymers with tailored thermal properties.

关键词： latent space optimization polymer discovery shape memory polymer transfer learning variational autoencoder

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Mitigating the Impact of Temperature Variations on Ultrasonic Guided Wave-Based Structural Health Monitoring through variational autoencoders

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SENSORS 2024年第5期24卷 1494页

作者： Junges, Rafael Lomazzi, Luca Miele, Lorenzo Giglio, Marco Cadini, Francesco Politecn Milan Dept Mech Engn Via Masa n1 I-20156 Milan Italy

Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting and acquiring ultrasonic-guided waves through a permanently installed sensor network. A baseline is registered when the structure is healthy, and newly acquired signals are compared to it to detect, localize, and quantify damage. To this purpose, the performance of traditional methods has been overcome by data-driven approaches, which allow processing a larger amount of data without losing diagnostic information. However, to date, no diagnostic method can deal with varying environmental and operational conditions (EOCs). This work aims to present a proof-of-concept that state-of-the-art machine learning methods can be used for reducing the impact of EOCs on the performance of damage diagnosis methods. Generative artificial intelligence was leveraged to mitigate the impact of temperature variations on ultrasonic guided wave-based SHM. Specifically, variational autoencoders and singular value decomposition were combined to learn the influence of temperature on guided waves. After training, the generative part of the algorithm was used to reconstruct signals at new unseen temperatures. Moreover, a refined version of the algorithm called forced variational autoencoder was introduced to further improve the reconstruction capabilities. The accuracy of the proposed framework was demonstrated against real measurements on a composite plate.

关键词： generative artificial intelligence variational autoencoder temperature ultrasonic guided wave

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Leveraging variational autoencoders for Parameterized MMSE Estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3731-3744页

作者： Baur, Michael Fesl, Benedikt Utschick, Wolfgang Tech Univ Munich TUM Sch Computat Informat & Technol D-80333 Munich Germany

In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation. The derived estimator is shown to approximate the minimum mean squared error estimator by utilizing the variational autoencoder as a generative prior for the estimation problem. We propose three estimator variants that differ in their access to ground-truth data during the training and estimation phases. The proposed estimator variant trained solely on noisy observations is particularly noteworthy as it does not require access to ground-truth data during training or estimation. We conduct a rigorous analysis by bounding the difference between the proposed and the minimum mean squared error estimator, connecting the training objective and the resulting estimation performance. Furthermore, the resulting bound reveals that the proposed estimator entails a bias-variance tradeoff, which is well-known in the estimation literature. As an example application, we portray channel estimation, allowing for a structured covariance matrix parameterization and low-complexity implementation. Nevertheless, the proposed framework is not limited to channel estimation but can be applied to a broad class of estimation problems. Extensive numerical simulations first validate the theoretical analysis of the proposed variational autoencoder-based estimators and then demonstrate excellent estimation performance compared to related classical and machine learning-based state-of-the-art estimators.

关键词： Estimation Training Inverse problems Data models Bayes methods Noise measurement Vectors Parameter estimation variational autoencoder conditional mean estimator generative model inverse problem

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Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation

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JOURNAL OF COMPUTATIONAL PHYSICS 2024年 515卷

作者： Glyn-Davies, Alex Duffin, Connor Akyildiz, O. Deniz Girolami, Mark Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England Imperial Coll London Dept Math Exhibit Rd London SW7 2AZ England Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England

Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model- space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder (Phi-DVAE) to embed diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard, possibly nonlinear, filter for the latent state-space model and a VAE, to assimilate the unstructured data into the latent dynamical system. Unstructured data, in our example systems, comes in the form of video data and velocity field measurements, however the methodology is suitably generic to allow for arbitrary unknown observation operators. A variational Bayesian framework is used for the joint estimation of the encoding, latent states, and unknown system parameters. To demonstrate the method, we provide case studies with the Lorenz-63 ordinary differential equation, and the advection and Korteweg-de Vries partial differential equations. Our results, with synthetic data, show that Phi-DVAE provides a data efficient dynamics encoding methodology which is competitive with standard approaches. Unknown parameters are recovered with uncertainty quantification, and unseen data are accurately predicted.

关键词： Data assimilation Nonlinear filtering variational autoencoder Inverse problem variational Bayes

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Multi-Objective Design of DNA-Stabilized Nanoclusters Using variational autoencoders With Automatic Feature Extraction

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ACS NANO 2024年第39期18卷 26997-27008页

作者： Sadeghi, Elham Mastracco, Peter Gonzalez-Rosell, Anna Copp, Stacy M. Bogdanov, Petko Univ Albany SUNY Dept Comp Sci Albany NY 12222 USA Univ Calif Irvine Dept Mat Sci & Engn Irvine CA 92697 USA Univ Calif Irvine Dept Chem Dept Chem & Biomol Engn Irvine CA 92697 USA Univ Calif Irvine Dept Phys & Astron Irvine CA 92697 USA

DNA-stabilized silver nanoclusters (Ag-N-DNAs) have sequence-tuned compositions and fluorescence colors. High-throughput experiments together with supervised machine learning models have recently enabled design of DNA templates that select for Ag-N-DNA properties, including near-infrared (NIR) emission that holds promise for deep tissue bioimaging. However, these existing models do not enable simultaneous selection of multiple Ag-N-DNA properties, and require significant expert input for feature engineering and class definitions. This work presents a model for multiobjective, continuous-property design of Ag-N-DNAs with automatic feature extraction, based on variational autoencoders (VAEs). This model is generative, i.e., it learns both the forward mapping from DNA sequence to Ag-N-DNA properties and the inverse mapping from properties to sequence, and is trained on an experimental data set of DNA sequences paired with Ag-N-DNA fluorescence properties. Experimental testing shows that the model enables effective design of Ag-N-DNA emission, including bright NIR Ag-N-DNAs with 4-fold greater abundance compared to training data. In addition, Shapley analysis is employed to discern learned nucleobase patterns that correspond to fluorescence color and brightness. This generative model can be adapted for a range of biomolecular systems with sequence-dependent properties, enabling precise design of emerging biomolecular nanomaterials.

关键词： variational autoencoder silver nanocluster DNA interpretable machinelearning fluorescence near-infrared

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Convolutional variational autoencoders and Resampling Techniques with Generative Adversarial Network for Enhancing Internet of Thing Security

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第3期34卷 562-569页

作者： Dong, Huiyao Kotenko, I. V. Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

The Internet of Things is a pivotal constituent of the contemporary technological revolution and has experienced expeditious expansion in recent times. The proliferation of Internet of Things devices has led to enhanced convenience and automation. However, the extensive deployment of Internet of Things devices has also engendered concerns regarding data privacy and security. Among various detection and prevention methodologies, deep learning is emerging as a prominent trend. This paper ultilizes convolutional variational autoencoders and resampling techniques for network attacks detection. The proposed methodology employs a hybrid data resampling technique to tackle the issue of imbalanced classes, followed by the implementation of a convolutional variational autoencoder classification model with a weighted loss function. The experiments demonstrate that the light-weighted convolutional variational autoencoder outperforms the baseline models. Therefore, it possesses the capability to effectively detect intrusive activities in real-world settings and strengthen the Internet of Things security.

关键词： Internet of Things cyber-attacks variational autoencoder

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Aspect-level item recommendation based on user reviews with variational autoencoders

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INFORMATION SCIENCES 2024年 671卷

作者： Ou, Wei Huynh, Van-Nam Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci Nomi Japan

In this paper we propose an aspect-based recommendation model based on variational autoencoders, that provides not only coarse predictions about what items users may like, but also finer-grained predictions of specific aspects users may be interested in of the recommended items. The proposed model first employs convolution operations to learn probability distributions of aspect-level embeddings for users and items from user aspect-level sentiments on seen items. Then it samples from these distributions and feeds the samples into transpose convolution operations to 'reconstruct' missing aspect-level sentiments for unseen items, thereby serving as signals for generating recommendations. To prevent overfitting, we impose a prior on the representation distributions and penalize the model if the learned distributions diverge from the prior. Based on the output of the proposed model, we propose a two-stage ranking scheme that combines aspect-level and overall sentiment signals to rank items. Experiment results show that the proposed model outperforms state -of -the -art aspect-based recommendation models, and the two-stage ranking scheme improves the traditional ranking by overall sentiment predictions.

关键词： Aspect-based recommender systems variational autoencoder Item ranking Aspect-level sentiment prediction

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A Survey on variational autoencoders in Recommender Systems

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ACM COMPUTING SURVEYS 2024年第10期56卷 1页

作者： Liang, Shangsong Pan, Zhou Liu, Wei Yin, Jian De Rijke, Maarten Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai Peoples R China Univ Amsterdam Amsterdam Noord Holland Netherlands

Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these, variational autoencoder (VAE)-based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.

关键词： variational autoencoder recommender systems deep learning Bayesian network

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Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 198页

作者： Farhadyar, Kiana Bonofiglio, Federico Hackenberg, Maren Behrens, Max Zoeller, Daniela Binder, Harald Univ Freiburg Inst Med Biometry & Stat Freiburg Germany Univ Freiburg Freiburg Ctr Data Anal & Modeling Freiburg Germany Natl Res Council Italy ISMAR Lerici Italy

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.

关键词： Synthetic data Complex distribution Propensity score Deep generative model variational autoencoder

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