Index Catalog // Scholar@UC

Start Over

Filtering by: Creator/Author Lee, Kijung Language English

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

Search Constraints