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筛选: 学 Education, Criminal Justice, and Human Services 创建者 Mathur, Nitin

Application of Autoencoder Duets in Anomaly and Intrusion Detection

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

证书:

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