Classification of Sequences Generated by Compression and Encryption Algorithms

Authors

  • Alexander Kozachok TCATTT
  • Spirin Andrey Andreevich

DOI:

https://doi.org/10.54654/isj.v10i2.59

Keywords:

signal, voice signal segmentation, informative speech features, autoregression models, discriminant data analysis.

Tóm tắt

Abstract—An approach to the formation of the voice signal (VS) informative features of the Vietnamese language on the basis of stationary autoregressive model coefficients is described. An original algorithm of VS segmentation based on interval estimation of speech sample numerical characteristics was developed to form local stationarity areas of the voice signal. The peculiarity is the use of high order autoregressive coefficients, the set of which is determined on the basis of discriminant analysis.

Tóm tắt— Bài báo mô tả một cách tiếp cận để tạo ra các đặc trưng thông tin tín hiệu thoại (VS-voice signal) của tiếng Việt trên cơ sở các hệ số của mô hình tự hồi quy dừng. Một thuật toán độc đáo để phân đoạn tín hiệu thoại  dựa trên ước tính khoảng của các đặc trưng số mẫu tiếng nói đã được phát triển để tạo ra các vùng tĩnh cục bộ của tín hiệu thoại. Điểm đặc biệt là việc sử dụng các hệ số tự hồi quy bậc cao, tập hợp của chúng được xác định trên cơ sở phân tích biệt thức.

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References

[1]. INFOWATCH company group site. URL: https://www.infowatch.ru/analytics/reports.4.html (дата обращения: 30.05.2019).

[2]. INFOWATCH company group site. URL: https://www.infowatch.ru/sites/default/files/report/analytics/russ/infowatch_otchet_032014_smb_fin.pdf (дата обращения: 30.05.2019).

[3]. INFOWATCH company group site. URL: https://www.infowatch.ru/analytics/leaks_monitoring/15678 (дата обращения: 30.05.2019).

[4]. X. Huang, Y. Lu, D. Li, M. Ma. A novel mechanism for fast detection of transformed data leakage // IEEE Access. Special section on challenges and opportunities of big data against cyber crime. Vol. 6, 2018. pp. 35926-35936

[5]. Y. Miao, Z. Ruan, L. Pan, Y. Wang, J. Zhang, Y. Xiang. Automated Big Traffic Analytics for Cyber Security // Eprint arXiv:1804.09023, bibcode: 2018arXiv180409023M. 2018.

[6]. S. Miller, K. Curran, T. Lunney. Multilayer Perceptron Neural Network for Detection of Encrypted VPN Network Traffic // International Conference on Cyber Situational Awarness, Data Analytics and Assessment. 2018. ISBN: 978-1-5386-4565-9.

[7]. P. Wang, X. Chen, F. Ye, Z. Sun. A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning // IEEE Access. Special section on challenges and opportunities of big data against cyber crime. Vol. 7, 2019. pp. 54024-54033 doi: 10.1109/ACCESS.2019.2912896

[8]. K. Demertzis, N. Tziritas, P. Kikiras, S.L. Sanchez, L. Iliadis. The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for Efficient Defense against Adversarial Attacks // Big Data and Cognitive Computing, 2019 3(6).

[9]. H. Zhang, C. Papadopoulos, D. Massey. Detecting encrypted botnet traffic // 16th IEEE Global Internet Symposium. 2013. p. 3453.

[10]. T. Radivilova, L. Kirichenko, D. Ageyev, M. Tawalbeh, V. Bulakh Decrypting SSL/TLS Traffic for Hidden Threats Detection // IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), 2018. ISBN: 978-1-5386-5903-8.

[11]. M. Piccinelli, P. Gubian. Detecting hidden encrypted volume files via statistical analysis // International Journal of Cyber-Security and Digital Forensics. Vol. 3(1). 2013 pp. 30-37.

[12]. NIST STS manual. URL: https://csrc.nist.gov/Projects/Random-Bit-Generation/ (дата обращения: 14.01.2019).

[13]. Toolkit for the transport layer security and secure sockets layer protocols.URL: http://openssl.org (дата обращения: 14.01.2019)

[14]. Archive manager WinRAR. URL: http://rarlab.com (дата обращения: 14.01.2019).

[15]. Pedregosa F., et al. Scikit-learn: Machine Learning in Python // Journal of Machine Learning Research 12. 2011. pp. 2825-2830.

[16]. Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees // Wadsworth, Belmont, CA. 1984. 368 p. ISBN: 9781351460491.

[17]. Hastie T., Tibshirani R., Friedman J. Elements of Statistical Learning // Springer. 2009. pp. 587-601. ISBN: 978-0387848570.

[18]. L. Breiman, A. Cutler. Random Forests // URL:https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm (дата обращения: 14.01.2019).

[19]. S. Raska. Python and machine learning // M .: DMK-Press. 2017. 418 p. ISBN: 978-5-97060-409-0.

[20]. L. Breiman. Random Forests // Journal Machine Learning 45(1). 2001. pp. 5-32.

[21]. M.Yu. Konyshev. Formation of probability distributions of binary vectors of the source of errors of a Markov discrete communication channel with memory using the method of "grouping probabilities" of error vectors. / M.Yu. Konyshev, A.Yu. Barabashov, K.E. Petrov, A.A. Dvilyansky // Industrial ACS and controllers. 2018. № 3. P. 42-52.

[22]. M.Yu. Konyshev. A compression algorithm for a series of distributions of binary multidimensional random variables. / M.Yu. Konyshev, A.A. Dvilyansky, K.E. Petrov, G.A. Ermishin // Industrial ACS and controllers. 2016. No. 8. P. 47-50

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Published

2020-04-09

How to Cite

Kozachok, A., & Andreevich, S. A. (2020). Classification of Sequences Generated by Compression and Encryption Algorithms. Journal of Science and Technology on Information Security, 2(10), 3-8. https://doi.org/10.54654/isj.v10i2.59

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Papers