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Erschienen in: Automatic Control and Computer Sciences 8/2018

01.12.2018

Application Model of Modern Artificial Neural Network Methods for the Analysis of Information Systems Security

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 8/2018

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Abstract

In this work considered the problem of safety analysis of control mechanisms in modern information systems, including control software systems of cyberphysical and industrial facilities, digital control systems for distributed cyber environments VANET, FANET, MARINET, industrial Internet of things and sensor networks. The representation of security violation as a property of the system described by a complex function is proposed, in which the method of finding violations is described in the form of approximation of that function and the calculation of its values for specific systems. Various approaches to the interpolation of such function are considered in the work, it is shown that the most promising option is the use of deep neural networks.
Literatur
1.
Zurück zum Zitat Kalinin, M.O., Krundyshev, V.M., and Semianov, P.V., Architectures for building secure vehicular networks based on SDN technology, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 907–914.CrossRef Kalinin, M.O., Krundyshev, V.M., and Semianov, P.V., Architectures for building secure vehicular networks based on SDN technology, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 907–914.CrossRef
2.
Zurück zum Zitat Kushik, N.G., Mammar, A., Cavalli, A., Yevtushenko, N.V., Jimenez, W., and Montes de Oca, E., A SPIN-based approach for detecting vulnerabilities in C programs, Autom. Control Comput. Sci., 2012, vol. 46, no. 7, pp. 379–386.CrossRef Kushik, N.G., Mammar, A., Cavalli, A., Yevtushenko, N.V., Jimenez, W., and Montes de Oca, E., A SPIN-based approach for detecting vulnerabilities in C programs, Autom. Control Comput. Sci., 2012, vol. 46, no. 7, pp. 379–386.CrossRef
3.
Zurück zum Zitat Godefroid, P., Microsoft Research, Fuzzing, Microsoft—A Research Perspective, ACSC 2017. Godefroid, P., Microsoft Research, Fuzzing, Microsoft—A Research Perspective, ACSC 2017.
4.
Zurück zum Zitat Pechenkin, A.I. and Nikolskiy, A.V., Architecture of a scalable system of fuzzing network protocols on a multiprocessor cluster, Autom. Control Comp. Sci., 2015, vol. 49, no. 8, pp. 758–765.CrossRef Pechenkin, A.I. and Nikolskiy, A.V., Architecture of a scalable system of fuzzing network protocols on a multiprocessor cluster, Autom. Control Comp. Sci., 2015, vol. 49, no. 8, pp. 758–765.CrossRef
5.
Zurück zum Zitat Poosankam, P., Pfenning, F., Platzer, A., Brumley, D., and McCamant, S., Scaling concolic execution of binary programs for security applications, PhD Thesis, Carnegie Mellon University, 2013. Poosankam, P., Pfenning, F., Platzer, A., Brumley, D., and McCamant, S., Scaling concolic execution of binary programs for security applications, PhD Thesis, Carnegie Mellon University, 2013.
6.
Zurück zum Zitat Hornik, K., Stinchcombe, M., and White, H., Multilayer feedforward networks are universal approximators, Neural Networks, 1989, vol. 2, no. 5, pp. 359–366.CrossRefMATH Hornik, K., Stinchcombe, M., and White, H., Multilayer feedforward networks are universal approximators, Neural Networks, 1989, vol. 2, no. 5, pp. 359–366.CrossRefMATH
7.
Zurück zum Zitat Hastad, J., Computational Limitations of Small-Depth Circuits, Cambridge, MA: MIT Press, 1987. Hastad, J., Computational Limitations of Small-Depth Circuits, Cambridge, MA: MIT Press, 1987.
8.
Zurück zum Zitat Yoshua Bengio, Learning deep architectures for AI, Found. Trends Mach. Learn., 2009, vol. 2, no. 1, pp. 1–127. http://dx.doi.org/.10.1561/2200000006CrossRefMATH Yoshua Bengio, Learning deep architectures for AI, Found. Trends Mach. Learn., 2009, vol. 2, no. 1, pp. 1–127. http://​dx.​doi.​org/​.​10.​1561/​2200000006CrossRefMATH
9.
Zurück zum Zitat Bengio, Y. and LeCun, Y., Scaling learning algorithms towards AI, in Large Scale Kernel Machines, Bottou, L., Chapelle, O., DeCoste, D., and Weston, J., Eds., MIT Press, 2007. Bengio, Y. and LeCun, Y., Scaling learning algorithms towards AI, in Large Scale Kernel Machines, Bottou, L., Chapelle, O., DeCoste, D., and Weston, J., Eds., MIT Press, 2007.
10.
Zurück zum Zitat Rumelhart, D.E., Hinton, G.E., and Williams, R.J., Learning representations by back-propagating errors, Nature, 1986, vol. 323, pp. 533–536.CrossRefMATH Rumelhart, D.E., Hinton, G.E., and Williams, R.J., Learning representations by back-propagating errors, Nature, 1986, vol. 323, pp. 533–536.CrossRefMATH
11.
Zurück zum Zitat Szegedy, C., et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015, pp. 1–9. Szegedy, C., et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015, pp. 1–9.
12.
Zurück zum Zitat Alemi, A.A., Ioffe, S., Szegedy, C., and Vanhoucke, V., Inceptionv4, Inception-ResNet and the impact of residual connections on learning, AAAI, 2017, vol. 4, p. 12. Alemi, A.A., Ioffe, S., Szegedy, C., and Vanhoucke, V., Inceptionv4, Inception-ResNet and the impact of residual connections on learning, AAAI, 2017, vol. 4, p. 12.
13.
Zurück zum Zitat Aliaksei, S. and Moschitti, S., Learning to rank short text pairs with convolutional deep neural networks, SIGIR’15 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 373–382. Aliaksei, S. and Moschitti, S., Learning to rank short text pairs with convolutional deep neural networks, SIGIR’15 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 373–382.
14.
Zurück zum Zitat Ng, A.Y., et al., Deep Speech: Scaling up end-to-end speech recognition, Presentation from a SF Meetup hosted event at NVIDIA (October 6th, 2015). Ng, A.Y., et al., Deep Speech: Scaling up end-to-end speech recognition, Presentation from a SF Meetup hosted event at NVIDIA (October 6th, 2015).
15.
Zurück zum Zitat Graves, A., Wayne, G., and Danihelka, I., Neural Turing Machines, 2014. http://arxiv.org/abs/1410.5401. Graves, A., Wayne, G., and Danihelka, I., Neural Turing Machines, 2014. http://​arxiv.​org/​abs/​1410.​5401.​
16.
Zurück zum Zitat Hinton, G.E., To Recognize Shapes, First Learn to Generate Images, Tech. Rep. UTML TR 2006-003, University of Toronto, 2016. Hinton, G.E., To Recognize Shapes, First Learn to Generate Images, Tech. Rep. UTML TR 2006-003, University of Toronto, 2016.
17.
Zurück zum Zitat Erhan, D., Manzagol, P.-A., Bengio, Y., Bengio, S., and Vincent, P., The difficulty of training deep architectures and the effect of unsupervised pre-training, Proceedings of The Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS’09), 2009, pp. 153–160. Erhan, D., Manzagol, P.-A., Bengio, Y., Bengio, S., and Vincent, P., The difficulty of training deep architectures and the effect of unsupervised pre-training, Proceedings of The Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS’09), 2009, pp. 153–160.
18.
Zurück zum Zitat Srivastava, N., et al., Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 2014, vol. 15, no. 1, pp. 1929–1958.MathSciNetMATH Srivastava, N., et al., Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 2014, vol. 15, no. 1, pp. 1929–1958.MathSciNetMATH
Metadaten
Titel
Application Model of Modern Artificial Neural Network Methods for the Analysis of Information Systems Security
Publikationsdatum
01.12.2018
Erschienen in
Automatic Control and Computer Sciences / Ausgabe 8/2018
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI
https://doi.org/10.3103/S0146411618080072

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