An Effective Intrusion Detection Classifier Using Long Short-Term Memory with Gradient Descent Optimization

Abstract

Intrusion Detection System (IDS) is one of the important issues in network security. IDSs are built to detect both known and unknown malicious attacks. Several machine learning algorithms are used widely in IDS such as neural network, SVM, KNN etc. However, these algorithms have still some limitations such as high false positive and false alarm rate. In this paper, our contribution is to build a classifier of IDS following deep learning approach. We find the most suitable optimizer among six optimizes for Long Short-Term Memory Recurrent Neural Network (LSTM RNN) model applied in IDS. Through our experiments, we found that LSTM RNN model with Nadam optimizer outperforms to previous works. We demonstrate our approach is really efficiency to intrusion detection with accuracy is 97.54%, detection rate is 98.95%, and false alarm rate is reasonable with 9.98%.