SELID: Selective Event Labeling for Intrusion Detection Datasets

A large volume of security events, generally collected by distributed monitoring sensors, overwhelms human analysts at security operations centers and raises an alert fatigue problem. Machine learning is expected to mitigate this problem by automatically distinguishing between true alerts, or attack...

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Detalles Bibliográficos
Autores principales: Jang, Woohyuk, Kim, Hyunmin, Seo, Hyungbin, Kim, Minsong, Yoon, Myungkeun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347169/
https://www.ncbi.nlm.nih.gov/pubmed/37447954
http://dx.doi.org/10.3390/s23136105

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347169/
https://www.ncbi.nlm.nih.gov/pubmed/37447954
http://dx.doi.org/10.3390/s23136105

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