Malicious Traffic Identification with Self-Supervised Contrastive Learning

As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic-identification method based on contrastive learning. The pr...

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Detalles Bibliográficos
Autores principales: Yang, Jin, Jiang, Xinyun, Liang, Gang, Li, Siyu, Ma, Zicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459182/
https://www.ncbi.nlm.nih.gov/pubmed/37631752
http://dx.doi.org/10.3390/s23167215

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459182/
https://www.ncbi.nlm.nih.gov/pubmed/37631752
http://dx.doi.org/10.3390/s23167215

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