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Filtering by: Creator/Author Mathur, Nitin Publisher University of Cincinnati

Application of Autoencoder Duets in Anomaly and Intrusion Detection

Type:

Article

Description/Abstract:

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.

Creator/Author:

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

Submitter:

Jess Kropczynski

Date Uploaded:

05/15/2020

Date Modified:

05/15/2020

Date Created:

2020-04-14

License:

All rights reserved