Time-Related Network Intrusion Detection Model: A Deep Learning Method

Network Intrusion Detection Systems (NIDS) have become a strong tool to alarm attacks in computer and communication systems. Machine learning, especially deep learning, has made huge success in fields of industry and academic. Network intrusion activity can be a time series event. In this paper, we adopt a time-related deep learning approach to detect network intrusions. A stacked sparse autoencoder (SSAE) is first built to extract the features with the greedy layer-wise strategy. And then, we propose a time- related intrusion detection system based on the variants of Recurrent Neural Network (RNN). We study the performance of proposed approach on the binary classification with a benchmark dataset UNSW- NB15. Based on the study of parameter time steps, it is proved that our time- related model is effective for intrusion detection. The experiment results show that the accuracy of the proposed approach reaches over 98% and the false alarm rate is as low as 1.8%. The performance of our model is superior to that of the standard RNN- based approach and approaches based on Deep Neural Network and shallow machine learning.

I. Introduction

With the increase of network penetration rate and the rapid development of network application, cyber security has become one of the biggest challenges that network developers and managers have to face [1]. Activities that attempt to bypass security mechanisms are usually considered as intrusions. A large amount of network intrusion incidents are reported each year. Network Intrusion Detection System is regarded as a very important foundation and prerequisite for identifying malicious activities and handling cyber attacks in network traffic. NIDS monitors the network traffic and scans the system for suspicious activities and alerts the alarm.