A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Vaccari, Ivan; Orani, Vanessa; Paglialonga, Alessia; Cambiaso, Enrico; Mongelli, Maurizio

doi:10.3390/s21113726

Cited by 39 publications

(22 citation statements)

References 42 publications

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“…The recipient domains of generative data augmentation methods thus far have primarily been cybersecurity and financial fraud detection. Datasets pertaining to IoT applications have also leveraged GANs to enrich training data, but in many scenarios, this is not done explicitly in the context of class imbalance, but rather to create synthetic data to replace original data which is presumed inaccurate [86]. There are number of unique challenges in applying GANs to tabular data, challenges not previously addressed by counterpart problems in visual domains.…”

Section: Discussionmentioning

confidence: 99%

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Sauber-Cole

Khoshgoftaar

2022

J Big Data

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The existence of class imbalance in a dataset can greatly bias the classifier towards majority classification. This discrepancy can pose a serious problem for deep learning models, which require copious and diverse amounts of data to learn patterns and output classifications. Traditionally, data-level and algorithm-level techniques have been instrumental in mitigating the adverse effect of class imbalance. With the recent development and proliferation of Generative Adversarial Networks (GANs), researchers across a variety of disciplines have adapted the architecture of GANs and implemented them on imbalanced datasets to generate instances of the underrepresented class(es). Though the bulk of research has been centered on the application of this methodology in computer vision tasks, GANs are likewise being appropriated for use in tabular data, or data consisting of rows and columns with traditional structured data types. In this survey paper, we assess the methodology and efficacy of these modifications on tabular datasets, across domains such network traffic classification and financial transactions over the past seven years. We examine what methodologies and experimental factors have resulted in the greatest machine learning efficacy, as well as the research works and frameworks which have proven most influential in the development of the application of GANs in tabular data settings. Specifically, we note the prevalence of the CGAN architecture, the optimality of novel methods with CNN learners and minority-class sensitive measures such as F1 score, the popularity of SMOTE as a baseline technique, and the improved performance in the year-over-year use of GANs in imbalanced tabular datasets.

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Section: Discussionmentioning

confidence: 99%

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Sauber-Cole

Khoshgoftaar

2022

J Big Data

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“…As far as future work is concerned, we plan to evaluate the proposed approach in the image field where adversarial machine learning is mainly known and tested. Moreover, the study may extend the testing through deeper cross-validation in the presence of a large amount of data, including the adoption of explainable data augmentation [2]. The characterization of the placement of the adversarial points, as through rules or other means, deserves further study to understand the behaviour of the attack and profile personalized counterattacks [60].…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning (ML) has become an increasingly used technology in every aspect of our lives. It is adopted for image classification [1], to prevent health diseases [2], in cyber-security to detect cyber-attacks [3], [4], in the new industrial era (called industry 4.0) [5] or in other fields. It has a significant impact on daily activities and the use of these algorithms aims to improve daily life by offering services and applications capable of making optimal autonomous decisions.…”

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confidence: 99%

eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics

et al. 2022

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Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise. An important concept to analyze when defining ML algorithms concerns adversarial machine learning attacks. These attacks aim to create manipulated datasets to mislead ML algorithm decisions. In this work, we propose new approaches able to detect and mitigate malicious adversarial machine learning attacks against a ML system. In particular, we investigate the Carlini-Wagner (CW), the fast gradient sign method (FGSM) and the Jacobian based saliency map (JSMA) attacks. The aim of this work is to exploit detection algorithms as countermeasures to these attacks. Initially, we performed some tests by using canonical ML algorithms with a hyperparameters optimization to improve metrics. Then, we adopt original reliable AI algorithms, either based on eXplainable AI (Logic Learning Machine) or Support Vector Data Description (SVDD). The obtained results show how the classical algorithms may fail to identify an adversarial attack, while the reliable AI methodologies are more prone to correctly detect a possible adversarial machine learning attack. The evaluation of the proposed methodology was carried out in terms of good balance between FPR and FNR on real world application datasets: Domain Name System (DNS) tunneling, Vehicle Platooning and Remaining Useful Life (RUL). In addition, a statistical analysis was performed to improve the robustness of the trained models, including evaluating their performance in terms of runtime and memory consumption. INDEX TERMS machine learning, detection algorithms, adversarial machine learning, reliable I. INTRODUCTION A. BACKGROUND VOLUME 4, 2016

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“…Provided that the generator is sufficiently trained, it generates artificial data that mimics the real ones, and can eventually fool the discriminator, i.e., the generated data are recognized as real by the discriminator. GANs have also attracted a lot of interest in the medical field to generate synthetic data for various applications, such as data augmentation for patient data collected from Internet of Medical Things devices, as the process of data collection can run into trouble for various reasons and cause problems for patient monitoring, and ultimately for clinical decision-making systems [28]. GAN-based models have also been used for the fast MRI (magnetic resonance imaging) reconstruction of blurry scans [29], as well as the segmentation of meibomian glands from infrared images, i.e., automatically identifying the area of meibomian glands [30], and style transfer from UBM (Ultrasound Biomicroscopy) to AS-OCT (Anterior Segment Optical Coherence Tomography) in ophthalmology image domains [31].…”

Section: Introductionmentioning

confidence: 99%

GAN-Based Approaches for Generating Structured Data in the Medical Domain

et al. 2022

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Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

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A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Cited by 39 publications

References 42 publications

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics

GAN-Based Approaches for Generating Structured Data in the Medical Domain

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