Abstract
Convolutional Neural Network (CNN) is a Deep Neural Network (DNN) that is well-suited to local feature extraction. DNN learns features useful for a particular learning problem from raw input data, thus reducing reliance on domain experts. However, automatic learning of features via DNN comes at a cost, as it requires relatively high computing resources. An approach to reduce DNN’s resource requirement is to use additional hand-crafted features as input, providing a compromise between automation and resource demand. In this paper, we present a hybrid DNN with mixed-mode features, and demonstrate its application in intrusion detection using UNSWNB15 dataset.
Original language | English |
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Title of host publication | Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022 |
Publisher | University of Hertfordshire |
Pages | 75-77 |
DOIs | |
Publication status | Published - 4 Nov 2022 |
Event | School of Physics, Engineering and Computer Science Research Conference - Online - hosted by the School of Physics, Engineering and Computer Science Duration: 12 Apr 2022 → … |
Conference
Conference | School of Physics, Engineering and Computer Science Research Conference |
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Abbreviated title | SPECS 2022 |
Period | 12/04/22 → … |