A Review of Tabular Data Synthesis Using GANs on an IDS Dataset

Bourou, Stavroula; Saer, A. El; Velivassaki, Terpsichori-Helen; Voulkidis, Artemis; Zahariadis, Theodore

doi:10.3390/info12090375

Cited by 71 publications

(60 citation statements)

References 20 publications

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“…One is leveraged for producing regression and labeled as a generator (G), while the second is labeled as a discriminator (D). Usually, the purpose of the generator is to take random noise (V) as input, transform it using the NNs, and create false instances, whereas the aim of the discriminator is to use a NN to separate the infected data generated via the generator from the actual one [31,32]. When the process reaches equilibrium, the discriminator is unable to recognize between real and bogus data.…”

Section: Gan-based Defense Methodologymentioning

confidence: 99%

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

et al. 2022

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Intrusion detection systems (IDS) are a very vital part of network security, as they can be used to protect the network from illegal intrusions and communications. To detect malicious network traffic, several IDS based on machine learning (ML) methods have been developed in the literature. Machine learning models, on the other hand, have recently been proved to be effective, since they are vulnerable to adversarial perturbations, which allows the opponent to crash the system while performing network queries. This motivated us to present a defensive model that uses adversarial training based on generative adversarial networks (GANs) as a defense strategy to offer better protection for the system against adversarial perturbations. The experiment was carried out using random forest as a classifier. In addition, both principal component analysis (PCA) and recursive features elimination (Rfe) techniques were leveraged as a feature selection to diminish the dimensionality of the dataset, and this led to enhancing the performance of the model significantly. The proposal was tested on a realistic and recent public network dataset: CSE-CICIDS2018. The simulation results showed that GAN-based adversarial training enhanced the resilience of the IDS model and mitigated the severity of the black-box attack.

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Section: Gan-based Defense Methodologymentioning

confidence: 99%

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

et al. 2022

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“…A disadvantage of this option is that it may become computationally challenging and prone to multicollinearity in the presence of variables with a high cardinality, i.e. with a large number of classes, since each possible class creates a new variable [55]. Interestingly, there is -to the best of our knowledge -little comprehensive, comparative and conclusive scientific evidence on the properties and performance of different categorical encoding schemes.…”

Section: Fitting Gaussian Copulas To Survey Attributesmentioning

confidence: 99%

Releasing survey microdata with exact cluster locations and additional privacy safeguards

Koebe¹,

Arias-Salazar²

2022

Preprint

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Household survey programs around the world publish finegranular georeferenced microdata to support research on the interdependence of human livelihoods and their surrounding environment. To safeguard the respondents' privacy, micro-level survey data is usually (pseudo)-anonymized through deletion or perturbation procedures such as obfuscating the true location of data collection. This, however, poses a challenge to emerging approaches that augment survey data with auxiliary information on a local level. Here, we propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards through synthetically generated data using generative models. We back our proposal with experiments using data from the 2011 Costa Rican census and satellite-derived auxiliary information. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under reidentification attempts.

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“…Apart from images, GANs have also been applied in the generation of tabular data, as is evident from various GAN approaches focusing on tabular data synthesis [ 10 , 11 ]. A detailed review of different tabular data synthesis approaches based on GANs is presented in [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification

Maqsood

Mumtaz

Haq

et al. 2021

Sensors

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Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

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A Review of Tabular Data Synthesis Using GANs on an IDS Dataset

Cited by 71 publications

References 20 publications

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

Mitigation of Black-Box Attacks on Intrusion Detection Systems-Based ML

Releasing survey microdata with exact cluster locations and additional privacy safeguards

Super Resolution Generative Adversarial Network (SRGANs) for Wheat Stripe Rust Classification

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