Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling

We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learn...

Descripción completa

Detalles Bibliográficos
Autores principales: Raghuram, Jayaram, Miller, David J., Kesidis, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2014
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4294760/
https://www.ncbi.nlm.nih.gov/pubmed/25685511
http://dx.doi.org/10.1016/j.jare.2014.01.001

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4294760/
https://www.ncbi.nlm.nih.gov/pubmed/25685511
http://dx.doi.org/10.1016/j.jare.2014.01.001

Ejemplares similares