Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks.

ABSTRACT: Due to the growing rise of cyber attacks in the Internet, the demand of accurate intrusion detection systems (IDS) to prevent these vulnerabilities is increasing. To this aim, Machine Learning (ML) components have been proposed as an efficient and effective solution. However, its applicability scope is limited by two important issues: (i) the shortage of network traffic data datasets for attack analysis, and (ii) the data privacy constraints of the data to be used. To overcome these problems, Generative Adversarial Networks (GANs) have been proposed for synthetic flow-based network traffic generation. However, due to the ill-convergence of the GAN training, none of the existing solutions can generate high-quality fully synthetic data that can totally substitute real data in the training of

SUBMITTER: Mozo A 

PROVIDER: S-EPMC8825844 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature