Application of Autoencoder Duets in Anomaly and Intrusion Detection Open Access Deposited
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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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- IT Research Symposium’20
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