Abstract
The intrusion detection based on deep learning method has been widely attempted for representation learning. However, in various deep learning models for intrusion detection, there is rarely convolutional neural networks (CNN) model. In this work, we propose a image conversion method of NSL-KDD data. Convolutional neural networks automatically learn the features of graphic NSL-KDD transformation via the proposed graphic conversion technique. We evaluate the performance of the image conversion method by binary class classification experiments with NSL-KDD Test\(^+\) and Test\(^{-21}\). Different structures of CNN are testified for comparison. On the two NSL-KDD test datasets, CNN performed better than most standard classifier although the CNN did not improve state of the art completely. Results show that the CNN model is sensitive to image conversion of attack data and our proposed method can be used for intrusion detection.
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Li, Z., Qin, Z., Huang, K., Yang, X., Ye, S. (2017). Intrusion Detection Using Convolutional Neural Networks for Representation Learning. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_87
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DOI: https://doi.org/10.1007/978-3-319-70139-4_87
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