Published

A Review of Tabular Data Synthesis using GANs on an IDS Dataset. Information. 2021

St. Bourou, A. El Saer, T.-H. Velivassaki, A. Voulkidis, Th. Zahariadis

Recent technological innovations along with the vast amount of available data worldwide, have led to the rise of cyberattacks against network systems. Intrusion Detection Systems (IDS) play a crucial role as a defense mechanism in networks, against adversarial attackers. Machine Learning methods provide various cybersecurity tools. However, these methods require plenty of data to be trained efficiently. Data which may be hard to collect or to use due to privacy reasons. One of the most notable Machine Learning tools is the Generative Adversarial Network (GAN) and it has great potential for Tabular data synthesis. In this work, we start by briefly presenting the most popular GAN architectures, VanillaGAN, WGAN and WGAN-GP. Focusing on tabular data generation, CTGAN, CopulaGAN and TableGAN models are used for the creation of synthetic IDS data. Specifically, the models are trained and evaluated on NSL-KDD dataset, considering the limitations and requirements that this procedure needs. Finally, based on certain quantitative and qualitative methods we argue and evaluate the most prominent GANs for tabular network data synthesis.

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Cricket: A virtualization layer for distributed execution of CUDA applications with checkpoint/restart support. Concurrency and computation. 2021

N. Eiling, J. Baude, S. Lankes, A. Monti

In high-performance computing and cloud computing the introduction of heterogeneous computing resources, such as GPU accelerator have led to a dramatic increase in performance and efficiency. While the benefits of virtualization features in these environments are well researched, GPUs do not offer virtualization support that enables fine-grained control, increased flexibility, and fault tolerance. In this article, we present Cricket: A transparent and low-overhead solution to GPU virtualization that enables future research into other virtualization techniques, due to its open-source nature. Cricket supports remote execution and checkpoint/restart of CUDA applications. Both features enable the distribution of GPU tasks dynamically and flexibly across computing nodes and the multi-tenant usage of GPU resources, thereby improving flexibility and utilization for high-performance and cloud computing.

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