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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Summary: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 function to arrive at a better performance. Results based on the KDDCUP'99 dataset show that our approach provides significant performance improvements over other deep sparse autoencoder Network Intrusion Detection Systems.
ISSN:2771-3075
DOI:10.1109/MCSoC57363.2022.00047