Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset

This work is a deep sparse autoencoder network intrusion detection system which addresses the issue of interpretability of L2 regularization technique used in other works. The proposed model was trained using a mini-batch gradient descent technique, L1 regularization technique and ReLU activation fu...

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Bibliographic Details

Published in:	Proceedings (IEEE International Symposium on Embedded Multicore/Manycore SoCs. Online) pp. 251 - 255
Main Authors:	Tanimu, Jesse Jeremiah, Hamada, Mohamed, Robert, Patience, Mahendran, Anand
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2022
Subjects:	Activation function Autoencoder Deep learning Intrusion detection Multicore processing Network intrusion detection Regularization Softmax and Sparse Autoencoder
ISSN:	2771-3075
Online Access:	Get full text
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