Network Data Generation using IP2Vec Embedding

Minji Kim, Migyeong Kang, Eunil Park, Sangheon Pack, Jinyoung Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Network data generation is crucial in applying deep learning techniques for predicting and managing 5G/6G networks. In this paper, we propose to incorporate IP2Vec in generative adversarial networks (GAN)-based network data generation models. Our evaluation demonstrates that utilizing IP2Vec can effectively enhance the performance of GANs in generating network data.

Original languageEnglish
Title of host publicationICTC 2023 - 14th International Conference on Information and Communication Technology Convergence
Subtitle of host publicationExploring the Frontiers of ICT Innovation
PublisherIEEE Computer Society
Pages872-874
Number of pages3
ISBN (Electronic)9798350313277
DOIs
Publication statusPublished - 2023
Event14th International Conference on Information and Communication Technology Convergence, ICTC 2023 - Jeju Island, Korea, Republic of
Duration: 2023 Oct 112023 Oct 13

Publication series

NameInternational Conference on ICT Convergence
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference14th International Conference on Information and Communication Technology Convergence, ICTC 2023
Country/TerritoryKorea, Republic of
CityJeju Island
Period23/10/1123/10/13

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • 5G/6G networks
  • generative adversarial networks
  • IP2Vec
  • network data generation

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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