Index Catalog // Scholar@UC

Borrar filtros

Filtrado por: Creador Lee, Kijung Departamento - School of Information Technology

Application of Autoencoder Duets in Anomaly and Intrusion Detection

Type:

Article

Descripción/Resumen:

Signature-based intrusion detection methods report high accuracy with a low false alarm rate. However, they do not perform well when faced with new or emerging threats. This work focuses on anomaly-based data driven methods to identify potential zero-day-attacks using a specific class of neural networks known as the autoencoder.

Creador/Autor:

Li, Chengcheng; Lee, Kijung; Mathur, Nitin, and Gonen, Bilal

Peticionario:

Jess Kropczynski

Fecha modificada:

05/15/2020

Fecha modificada:

05/15/2020

Fecha de creacion:

2020-04-14

Licencia:

All rights reserved