Application of Autoencoder Duets in Anomaly and Intrusion Detection

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

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Date Uploaded: 05/15/2020
Date Modified: 05/15/2020

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.

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