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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint (2016). arXiv:1603.04467
Aggarwal, P., Sharma, S.K.: Analysis of KDD dataset attributes-class wise for intrusion detection. Procedia Comput. Sci. 57, 842–851 (2015)
Costa, Y.M., Oliveira, L.S., Silla, C.N.: An evaluation of convolutional neural networks for music classification using spectrograms. Appl. Soft Comput. 52, 28–38 (2017)
Denning, D.E.: An intrusion-detection model. IEEE Trans. softw. Eng. 2, 222–232 (1987)
Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recogn. 58, 121–134 (2016)
Gao, N., Gao, L., Gao, Q., Wang, H.: An intrusion detection model based on deep belief networks. In: 2014 Second International Conference on Advanced Cloud and Big Data (CBD), pp. 247–252. IEEE (2014)
Kim, J., Kim, J., Thu, H.L.T., Kim, H.: Long short term memory recurrent neural network classifier for intrusion detection. In: 2016 International Conference on Platform Technology and Service (PlatCon), pp. 1–5. IEEE (2016)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint (2014). arXiv:1408.5882
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. pp. 1097–1105 (2012)
Kumar, S., Spafford, E.H.: A pattern matching model for misuse intrusion detection (1994)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lee, W., Stolfo, S.J., Mok, K.W.: A data mining framework for building intrusion detection models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 120–132. IEEE (1999)
Lee, W., Stolfo, S.J., et al.: Data mining approaches for intrusion detection. In: USENIX Security Symposium, pp. 79–93. San Antonio, TX (1998)
Li, Y., Ma, R., Jiao, R.: A hybrid malicious code detection method based on deep learning. Methods 9(5), (2015)
Lippmann, R., Cunningham, R.K., Fried, D.J., Graf, I., Kendall, K.R., Webster, S.E., Zissman, M.A.: Results of the DARPA 1998 offline intrusion detection evaluation. In: Recent Advances in Intrusion Detection, vol. 99, pp. 829–835 (1999)
Niyaz, Q., Sun, W., Javaid, A.Y., Alam, M.: A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (Formerly BIONETICS), BICT-15, vol. 15, pp. 21–26 (2015)
Özgür, A., Erdem, H.: A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015. PeerJ PrePrints 4, e1954v1 (2016)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD cup 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, pp. 1–6. IEEE (2009)
Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN), pp. 712–717. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-70139-4_87
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70138-7
Online ISBN: 978-3-319-70139-4
eBook Packages: Computer ScienceComputer Science (R0)