Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset
Jesse Jeremiah Tanimu, Mohamed Hamada, Patience Robert, Anand Mahendran · 2022
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.