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筛选: 学 Education, Criminal Justice, and Human Services 创建者 Lee, Kijung

Type:: Article
摘抄:: 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.
作者:: Li, Chengcheng; Lee, Kijung; Mathur, Nitin, and Gonen, Bilal
提交者:: Jess Kropczynski
上传日期:: 05/15/2020
更改日期:: 05/15/2020
创建:: 2020-04-14

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