ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 10. Vol. 31. 2025

DOI: 10.17587/it.31.538-546

S. V. Belim, Dr. Phys.-Math. Sc., Professor, S. N. Munko, Assistant, S. Yu. Belim, PhD, Assistant Professor,
Omsk State Technical University, Omsk, 644050, Russian Federation

Model for Steganographic Data Embedding into Program Memory

Received on 09.11.2024
Accepted on 26.03.2025

The article suggests a method for embedding hidden data into the program dynamic memory. The method is based on connecting a dynamic authentication library. The library works directly with a heap of programs. The algorithm breaks the security label into blocks of the same size. The blocks are evenly distributed across the heap. The memory scheduler is not involved in the generation of this data. The method deletes embedded data after a certain time period. Heap address calculation parameters and time period are parameters of the algorithm. The authentication library that embeds the information is universal. The probabilistic model of the program with a heap is proposed in the article. This model is necessary to investigate the possibility of collisions between embedded data and dynamic program variables. The model treats the creation and deletion of dynamic variables as random events. Computer simulation of program behavior for different probability ratios was carried out. À computer experiment showed the basic patterns of heap use by the program. The simulation results demonstrate the linear dependence of heap filling on the probability ratio of creating and deleting variables. The criteria for selecting steganographic embedding parameters are determined based on modeling. The period for placement of embedded data blocks and the time of presence the steganographic insert in the program memory are determined by the statistical characteristics of the executable code.
Keywords: steganographic method, hidden data, security labels, probabilistic model, program memory model

P. 538-546

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References

1. El-Khalil R., Keromytis À . Hydan: Hiding information in program binaries. Lecture Notes in Computer Science, 2004, vol. 3269, pp. 187 —199, DOI: 10.1007/978-3-540-30191-2_15
2. Krasov A., Arshinov A., Ushakov I. Embedding the hidden information into java byte code. ARPN Journal of Engineering and Applied Sciences, 2018, vol. 13 (8), pp. 2746—2752.
3. Krasov A., Tregubov Y., Shterenberg S. Research of copy protection methods software based on embed of digital watermarks into executable and library files. Cambridge Journal of Education and Science, 2015, vol. 2 (14), pp. 565—573.
4. Shterenberg S. I., Krasov A. V., Ushakov I. A. Analysis of using equivalent instructions at the hidden embedding of infor­mation into the executable files. Journal of Theoretical and Applied Information Technology, 2015, vol. 80 (1), pp. 28—34.
5. Hirohisa Í. Data hiding for text and binary files. Computational linguistics: concepts, methodologies, tools, and applications, 2014, pp. 1495—1514, DOI: 10.4018/978-1-4666-6042-7.ch074.
6. Choi S., Park H., Lim H.-I, Han T. À static birthmark of binary executables based on API call structure. ASIAN'07: Proceedings of the 12th Asian computing science conference on Advances in computer science: computer and network security. 2007, pp. 2—16, DOI: 10.1007/978-3-540-76929-3_2.
7. Anckaert B., De Sutter B., Chanet D., De Bosschere K. Steganography for executables and code transformation signatures. Lecture Notes in Computer Science, 2005, vol. 3506, pp. 425—439, DOI: 10.1007 /11496618 _31.
8. Stern J. P., Hachez G., Koeune F., Quisquater J. J. Robust object watermarking: application to code. Lecture Notes in Computer Science, 2000, vol. 1768, pp. 368—378, DOI: 10.1007/10719724_25.
9. Mairesse J., Vuillon L. Asymptotic behavior in a heap model with two pieces. Theoretical Computer Science, 2002, vol. 270, pp. 525—560, DOI: 10 .1016 /S0304-3975(01)00004-4.
10. Mandrykin M. U., Muffin V. S. Modeling Memory with Unin­terpreted Functions for Predicate Abstractions. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS), 2015, vol. 27 (5), pp. 117—142 (in Russian), DOI: 10.15514/ISPRAS-2015-27(5)-7.
11. Berger E. D., Zorn  . G. DieHard: Probabilistic memory safety for unsafe languages. ACMsigplan notices, 2006, vol. 41 (6), pp. 158—168, DOI: 10.1145/1133255.1134000.
12. Fraguela B. B., Doallo R., Zapata E. L. Probabilistic Miss Equations: Evaluating Memory Hierarchy Performance. IEEE Transaction on computers, 2003, vol. 52 (3), pp. 321—336, DOI: 10.1109/TC.2003.1183947.
13. Stefanakos I., Calinescu R., Gerasimou S. Probabilistic Program Performance Analysis. 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2021, pp. 148—157, DOI: 10.1109 /SEAA53835.2021.00027.

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