Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System
Abstract
Classification features are crucial for an intrusion detection system (IDS), and the detection performance of IDS will change dramatically when providing different input features. Moreover, the large number of network traffic and their high-dimensional features will result in a very lengthy classification process. Recently, there is an increasing interest in the application of deep learning approaches for classification and learn feature...
Paper Details
Title
Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System
Published Date
Jul 23, 2018
Journal
Volume
6
Pages
41238 - 41248
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