A Network Intrusion Detection Method Based on Stacked Autoencoder and LSTM

Abstract

Nowadays, network intrusions have brought greater impact in a large scale. Intrusion Detection Systems (IDS) have been a recent research hotspot for both the industry and the academic. However, due to the dynamic characteristics of network traffic, it is challenging to extract significant features and identify the traffic types. This paper focuses on applying deep learning methods to feature extraction. Specifically, an IDS model is proposed based on autoencoder and long short-term memory (LSTM) cell. The overall architecture of the intrusion detection model includes a feature extractor, a classifier, and an evaluation block. Different structures of the feature extraction model have been discussed and researched. Experiments conducted on the UNSW-NB15 dataset produce satisfactory result. A number of selected metrics such as accuracy and false alarm rate are adopted to evaluate the detection performance. Simulation results indicate that our model works better than competing machine learning methods and achieves accuracy of over 92%.