A Systematic Approach for the Application of Restricted Boltzmann Machines in Network Intrusion Detection

1. Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cogn. Sci. 9(1), 147–169 (1985)

   Article  Google Scholar 

2. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

   Article  MathSciNet  MATH  Google Scholar 

3. Bengio, Y., LeCun, Y.: Scaling learning algorithms towards AI. In: Bottou, L., Chapelle, O., DeCoste, D., Weston, J. (eds.) Large-Scale Kernel Machines. MIT Press, Cambridge (2007)

   Google Scholar 

4. Chen, H., Murray, A.F.: Continuous restricted Boltzmann machine with an implementable training algorithm. IEEE Proc. Vis. Image Sig. Process. 150(3), 153–158 (2003)

   Article  Google Scholar 

5. Fiore, U., Palmieri, F., Castiglione, A., De Santis, A.: Network anomaly detection with the restricted Boltzmann machine. Neurocomputing 122, 13–23 (2013)

   Article  Google Scholar 

6. Hinton, G.E.: A practical guide to training restricted Boltzmann machines. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 599–619. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35289-8_32

   Chapter  Google Scholar 

7. Hinton, G., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

   Article  MathSciNet  MATH  Google Scholar 

8. Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

   Article  MathSciNet  MATH  Google Scholar 

9. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors, July 2012. arXiv:1207.0580 [cs.NE]

10. Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)

    Article  MATH  Google Scholar 

11. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

12. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

13. Ising, E.: Beitrag zur theorie des ferromagnetismus. Z. Phys. 31(1), 253–258 (1925)

    Article  Google Scholar 

14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates (2012)

    Google Scholar 

15. Movellan, J.R., Mineiro, P., Williams, R.J.: A Monte Carlo EM approach for partially observable diffusion processes: theory and applications to neural networks. Neural Comput. 14, 1507–1544 (2002)

    Article  MATH  Google Scholar 

16. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Frnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning, pp. 807–814. Omnipress (2010)

    Google Scholar 

17. Onsager, L.: Crystal statistics. I. A two-dimensional model with an order-disorder transition. Phys. Rev. 65, 117–149 (1944)

    Article  MathSciNet  MATH  Google Scholar 

18. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958)

    Article  Google Scholar 

19. Salakhutdinov, R.: Learning in Markov random fields using tempered transitions. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) NIPS, pp. 1598–1606. Curran Associates (2009)

    Google Scholar 

20. Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357–374 (2012)

    Article  Google Scholar 

21. Smith, L.N., Hand, E.M., Doster, T.: Gradual DropIn of layers to train very deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4763–4771 (2016)

    Google Scholar 

22. Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: foundations, pp. 194–281. MIT Press (1986)

    Google Scholar 

23. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: Proceedings of the Second IEEE International Conference on Computational Intelligence for Security and Defense Applications, CISDA 2009, pp. 53–58 (2009)

    Google Scholar 

24. Teh, Y.W., Titterington, D.M. (eds.): Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (2010)

    Google Scholar 

25. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

26. Welling, M., Teh, Y.W.: Linear response algorithms for approximate inference in graphical models. Neural Comput. 16, 197–221 (2004)

    Article  MATH  Google Scholar 

27. Zhao, X., Hou, Y., Yu, Q., Song, D., Li, W.: Understanding deep learning by revisiting Boltzmann machines: an information geometry approach (2013). CoRR, abs/1302.3931

    Google Scholar 

Download references