An Effective Network Attack Detection Method Based on Kernel PCA and LSTM-RNN

Abstract

Intrusion Detection System (IDS) is 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. To overcome above challenge, we propose a novel effective attack detection method based on kernel principal component analysis (PCA) and long short-term memory recurrent neural network (LSTM-RNN). To achieve high accurate detection rate, data preprocessing, feature extraction, attack detection is seamlessly integrated into an end-to-end detection method. To assess the method, the well-known NSL-KDD dataset has been used. The results of experimental show that the proposed attack detection strategy greatly outperforms several attack detection strategy that use SVM, neural network or Bayesian methods.