Table of Contents Author Guidelines Submit a Manuscript
Security and Communication Networks
Volume 2017 (2017), Article ID 4184196, 10 pages
https://doi.org/10.1155/2017/4184196
Research Article

Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

College of Computer, National University of Defense Technology, Changsha, Hunan 410073, China

Correspondence should be addressed to Zhiping Cai

Received 28 July 2017; Accepted 22 October 2017; Published 16 November 2017

Academic Editor: Wojciech Mazurczyk

Copyright © 2017 Yang Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Z. Cai, Z. Wang, K. Zheng, and J. Cao, “A Distributed TCAM coprocessor architecture for integrated longest prefix matching, policy filtering, and content filtering,” IEEE Transactions on Computers, vol. 62, no. 3, pp. 417–427, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. K. Zheng, Z. Cai, X. Zhang, Z. Wang, and B. Yang, “Algorithms to speedup pattern matching for network intrusion detection systems,” Computer Communications, vol. 62, pp. 47–58, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Yu, J. Long, F. Liu, and Z. Cai, “Machine learning combining with visualization for intrusion detection: a survey,” in Modeling Decisions for Artificial Intelligence, vol. 9880 of Lecture Notes in Comput. Sci., pp. 239–249, Springer, Cham, Germany, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  4. R. Sommer and V. Paxson, “Outside the closed world: on using machine learning for network intrusion detection,” in Proceedings of the IEEE Symposium on Security and Privacy, pp. 305–316, IEEE Computer Society, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, “Why does unsupervised pre-training help deep learning?” Journal of Machine Learning Research, vol. 11, pp. 625–660, 2010. View at Google Scholar · View at MathSciNet
  6. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, “Self-taught learning: transfer learning from unlabeled data,” in Proceedings of the 24th International Conference on Machine Learning (ICML '07), pp. 759–766, ACM, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. U. Fiore, F. Palmieri, A. Castiglione, and A. de Santis, “Network anomaly detection with the restricted Boltzmann machine,” Neurocomputing, vol. 122, pp. 13–23, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “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), New York, NY, USA, December 2015. View at Publisher · View at Google Scholar
  10. S. Revathi and A. Malathi, A Detailed Analysis on nsl-kdd Dataset Using Various Machine Learning Techniques for Intrusion Detection, 2013.
  11. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in Proceedings of the 2nd IEEE Symposium on Computational Intelligence for Security and Defence Applications, pp. 1–6, IEEE, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning,” Pattern Recognition, vol. 58, pp. 121–134, 2016. View at Publisher · View at Google Scholar
  13. Z. Wang, The Applications of Deep Learning on Traffic Identification, BlackHat, 2015.
  14. W. Wang, M. Zhu, X. Zeng et al., “Malware traffic classification using convolutional neural network for representation learning,” in Proceedings of the 2017 International Conference on Information Networking (ICOIN), pp. 712–717, Da Nang, Vietnam, January 2017. View at Publisher · View at Google Scholar
  15. Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–27, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Fisher and V. Koltun, Multi-scale context aggregation by dilated convolutions, 2015, https://arxiv.org/abs/1511.07122.
  17. V. Dumoulin and V. Francesco, A guide to convolution arithmetic for deep learning, https://arxiv.org/abs/1603.07285.
  18. X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256, 2010.
  19. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Proceedings of the 20th Annual Conference on Neural Information Processing Systems (NIPS '06), pp. 153–160, Cambridge, Mass, USA, December 2006. View at Scopus
  20. “The ctu-13 dataset,” https://stratosphereips.org/category/dataset.html.
  21. “The unb iscx 2012 intrusion detection evaluation dataset,” http://www.unb.ca/cic/research/datasets/ids.html.
  22. A. Shiravi, H. Shiravi, M. Tavallaee, and A. A. Ghorbani, “Toward developing a systematic approach to generate benchmark datasets for intrusion detection,” Computers & Security, vol. 31, no. 3, pp. 357–374, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. “Threatglass by barracuda labs,” http://threatglass.com/.
  24. “Contagio malware dump,” http://contagiodump.blogspot.kr/2013/08/deepend-research-list-of-malware-pcaps.html.
  25. Y. Yu, J. Long, and Z. Cai, “Session-Based Network Intrusion Detection Using a Deep Learning Architecture,” in Modeling Decisions for Artificial Intelligence, vol. 10571 of Lecture Notes in Computer Science, pp. 144–155, Springer International Publishing, Cham, Germany, 2017. View at Publisher · View at Google Scholar
  26. B. James, B. Olivier, F. Bastien et al., “Theano: A cpu and gpu math compiler in python,” in Proceedings of the 9th Python in Science Conference, pp. 1–7, 2010.
  27. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), pp. 1097–1105, Lake Tahoe, Nev, USA, December 2012. View at Scopus