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Automatic Control and Computer Sciences

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01-12-2018

An Approach to the Programs Security Analysis using Vector Representation of Machine Code

Authors: R. A. Demidov, A. I. Pechenkin

Published in: Automatic Control and Computer Sciences | Issue 8/2018

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Abstract

In this article, the authors propose an approach to the security analysis of program code using vector representations of machine instructions. The article also proposes a method for constructing multidimensional vector spaces for a set of program code instructions. The construction of semantically expressive vector representations of machine instructions is considered as one of the important tasks in constructing a neural network code classifier for vulnerabilities. The applicability of the principle of transfer learning to machine code is demonstrated experimentally.

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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

Deep Learning on Disassembly, Black Hat USA, 2015. https://www.blackhat.com/docs/us-15/materials/us-15-Davis-Deep-Learning-On-Disassembly.pdf.

Chappell, T., et al., An Inital Exploration into Machine Learning for the Purposes of Finding Bugs in Source Code, Oracle Labs, 2016.

Xuan Huo, Ming Li, and Zhi-Hua Zhou, Learning unified features from natural and programming languages for locating buggy source code, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI’16), Brewka, G., Ed., 2016, pp. 1606–1612.

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).

Bengio, Y., Cho, K., and Bahdanau, D., Neural machine translation by jointly learning to align and translate, International Conference on Learning Representations, 2015.

Blunsom, P., Grefenstette, E., and Kalchbrenner, N., A convolutional neural network for modelling sentences, The 52nd Annual Meeting of the Association for Computational Linguistics, 2014.

Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., and Socher, R., Ask me anything: Dynamic memory networks for natural language processing, arXiv preprint arXiv:1506.07285, 2015.

Bottou, L., From machine learning to machine reasoning, J. Mach. Learn., 2014, vol. 94, no. 2, pp. 133–149.MathSciNetCrossRef

10.

Mikolov, T., Wen-Tau Yih, S., and Zweig, G., Linguistic regularities in continuous space word representations, Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-2013).

11.

Thang Luong, Socher, R., and Manning, C.D., Better word representations with recursive neural networks for morphology, CoNLL 2013, pp. 104–113.

12.

Mikolov, T., Chen, K., Corrado, G., and Dean, J., Efficient estimation of word representations in vector space, Proceedings of Workshop at ICLR, 2013.

13.

Sinno Jialin Pan and Qiang Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 2010, vol. 22, no. 10, pp. 1345–1359.

Title: An Approach to the Programs Security Analysis using Vector Representation of Machine Code
Authors: R. A. Demidov
A. I. Pechenkin
Publication date: 01-12-2018
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 8/2018
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411618080096

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