| Title | A novel network intrusion detection algorithm based on Fast Fourier Transformation |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Liu, Weiyou, Liu, Xu, Di, Xiaoqiang, Qi, Hui |
| Conference Name | 2019 1st International Conference on Industrial Artificial Intelligence (IAI) |
| Date Published | jul |
| Keywords | CNN, CNN model, composability, computer network security, convolutional neural nets, convolutional neural networks, Deep Learning, deep learning techniques, Discrete Fourier transforms, fast Fourier transformation, fast Fourier transforms, FFT, image classification, Image coding, Intrusion detection, intrusion detection problem, learning (artificial intelligence), machine learning algorithms, Matrix converters, Metrics, network intrusion detection, network intrusion detection algorithm, network traffic, NSL-KDD, NSL-KDD dataset, pubcrawl, representation learning, Resiliency |
| Abstract | Deep learning techniques have been widely used in intrusion detection, but their application on convolutional neural networks (CNN) is still immature. The main challenge is how to represent the network traffic to improve performance of the CNN model. In this paper, we propose a network intrusion detection algorithm based on representation learning using Fast Fourier Transformation (FFT), which is first exploration that converts traffic to image by FFT to the best of our knowledge. Each traffic is converted to an image and then the intrusion detection problem is turned to image classification. The experiment results on NSL-KDD dataset show that the classification performence of the algorithm in the CNN model has obvious advantages compared with other algorithms. |
| DOI | 10.1109/ICIAI.2019.8850770 |
| Citation Key | liu_novel_2019 |
A novel network intrusion detection algorithm based on Fast Fourier Transformation