Abstract
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, 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 an 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.
Funder
H2020 Industrial Leadership
Reference54 articles.
1. Computer security threat monitoring and surveillance;Anderson,1980
2. Decision tree based algorithm for intrusion detection;Rai;Int. J. Adv. Netw. Appl.,2016
3. SVM-DT-based adaptive and collaborative intrusion detection
4. Bayesian Networks for Network Intrusion Detectionhttps://intechopen.com/books/bayesian-network/bayesian-networks-for-network-intrusion-detection
Cited by
47 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献