Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Intrusion detection systems play an important role in preventing security threats and protecting networks from attacks. However, with the emergence of unknown attacks and imbalanced samples, traditional machine learning methods suffer from lower detection rates and higher false positive rates. We pr...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Yanqing, Zheng, Kangfeng, Wu, Chunhua, Yang, Yixian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603523/
https://www.ncbi.nlm.nih.gov/pubmed/31159512
http://dx.doi.org/10.3390/s19112528

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603523/
https://www.ncbi.nlm.nih.gov/pubmed/31159512
http://dx.doi.org/10.3390/s19112528

Ejemplares similares