| Title | Classification of Network Traffic Using Generative Adversarial Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Myakotin, Dmitriy, Varkentin, Vitalii |
| Conference Name | 2021 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS) |
| Date Published | sep |
| Keywords | Complexity theory, cybersecurity, denial-of-service attack, Generative Adversarial Learning, generative adversarial networks, machine learning, Metrics, Network security, network traffic, pubcrawl, resilience, Resiliency, Scalability, security, telecommunication traffic, Training |
| Abstract | Currently, the increasing complexity of DDoS attacks makes it difficult for modern security systems to track them. Machine learning techniques are increasingly being used in such systems as they are well established. However, a new problem arose: the creation of informative datasets. Generative adversarial networks can help create large, high-quality datasets for machine learning training. The article discusses the issue of using generative adversarial networks to generate new patterns of network attacks for the purpose of their further use in training. |
| DOI | 10.1109/ITQMIS53292.2021.9642895 |
| Citation Key | myakotin_classification_2021 |
Classification of Network Traffic Using Generative Adversarial Networks