Intrusion Detection System for NSL-KDD Dataset Using Convolutional Neural Networks

Abstract

With the increment of cyber traffic, there is a growing demand for cyber security. How to accurately detect cyber intrusions is the hotspot of recent research. Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. In this paper, we build an IDS model with deep learning methodology. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Therefore, we propose to train an IDS model based on Convolution Neural Networks (CNN), a typical deep learning method, using entire NSL-KDD dataset. We study the performance of the model using multi class classification to compare with the performance of traditional machine learning methods including Random Forest (RF) and Support Vector Machine (SVM), and deep learning methods including Deep Belief Network (DBN) and Long Short Term Memory (LSTM). The experimental results show that the performance of our IDS model is superior to the performance of models based on traditional machine learning methods and novel deep learning methods in multi-class classification. Our model improves the accuracy of the intrusion detection and provides a new research direction for intrusion detection.