Generation of Synthetic Data for Honeypot Systems Using Deep Learning Methods
Authors: 
V. D. Danilov, T. D. Ovasapyan, D. V. Ivanov, A. S. Konoplev, D. A. MoskvinAuthors Info & Claims
Pages 916 - 926
Abstract
Abstract
This paper presents studies intended to analyze the methods for generating synthetic data to fill honeypot systems. To choose the generated data types, the topical target objects in the context of honeypot systems are revealed. The existing methods of generation are investigated. Methods for analyzing the quality of generated data in the context of honeypot systems are also analyzed. As a result, the layout of an automated system for generating synthetic data for honeypot systems is developed and the efficiency of its operation is estimated.
References
[1]
Positive Technologies, 2021. https://www.ptsecurity.com/ru-ru/research/analytics/cybersecurity-threatscape-2021-q1/. Cited February, 2022.
[2]
Mairh, A., Barik, D., Verma, K., Jena, D., Honeypot in network security: A survey, ICCCS ’11: Proc. 2011 Int. Conf. on Communication, Computing & Security, Rourkela Odisha, India, 2011, New York: Association for Computing Machinery, 2011, pp. 600–605.
[3]
Ovasapyan T.D., Knyazev P.V., and Moskvin D.A. Application of taint analysis to study the safety of software of the internet of things devices based on the ARM architecture Autom. Control Comput. Sci. 2020 54 834-840
[4]
Bao, J., Ji, C., and Gao, M., Research on network security of defense based on honeypot, 2010 Int. Conf. on computer application and system modeling (ICCASM 2010), Taiyuan, China, 2010, IEEE, 2010, pp. V10-299–V10-302.
[5]
Kalinin M., Zegzhda D., and Zavadskii E. Protection of energy network infrastructures applying a dynamic topology virtualization Energies 2022 15 4123
[6]
Positive Technologies. https://www.ptsecurity.com/ru-ru/about/news/positive-technologies-chislo-atak-na-promyshlennye-kompanii-vyroslo-na-91-po-sravneniyu-s-2019-godom/. Cited November 10, 2021.
[7]
Krundyshev, V. and Kalinin, M., The security risk analysis methodology for smart network environments, 2020 Int. Russian Automation Conf. (RusAutoCon), Sochi, Russia, 2020, IEEE, 2020, pp. 437–442.
[8]
Ivanov, D., Kalinin, M., Krundyshev, V., and Orel, E., Automatic security management of smart infrastructures using attack graph and risk analysis, 2020 Fourth World Conf. on Smart Trends in Systems, Security and Sustainability (WorldS5), London, 2020, IEEE, 2020, pp. 295–300.
[9]
Ognev R.A., Zhukovskii E.V., and Zegzhda D.P. Clustering of malicious executable files based on the sequence analysis of system calls Autom. Control Comput. Sci. 2019 53 1045-1055
[10]
Dakhnovich A.D., Moskvin D.A., and Ivanov D.V. A technique for safely transforming the infrastructure of industrial control systems to the Industrial Internet of Things Autom. Control Comput. Sci. 2020 54 841-849
[11]
Belenko, V., Krundyshev, V., and Kalinin, M., Synthetic datasets generation for intrusion detection in VANET, SIN ’18: Proc. 11th Int. Conf. on Security of Information and Networks, Cardiff, UK, 2018, New York: Association for Computing Machinery, 2018, p. 9.
[12]
Belenko, V., Chernenko, V., Kalinin, M., and Krundyshev, V., Evaluation of GAN applicability for intrusion detection in self-organizing networks of cyber physical systems, 2018 Int. Russian Automation Conf. (RusAutoCon), Sochi, Russia, 2018, IEEE, 2018, pp. 1–7.
[13]
Dakhnovich A.D., Moskvin D.A., and Zegzhda D.P. An approach to building cyber-resistant interactions in the Industrial Internet of Things Autom. Control Comput. Sci. 2019 53 948-953
[14]
Stadler T., Oprisanu B., and Troncoso C. Synthetic data-anonymisation Groundhog Day, 31st USENIX Security Symp. (USENIX Security 22) 2022 Boston USENIX Association
[15]
Shokri, R., Stronati, M., Song, C., and Shmatikov, V., Membership inference attacks against machine learning models, 2017 IEEE Symp. on Security and Privacy (SP), San Jose, Calif., 2017, IEEE, 2017, pp. 3–18.
[16]
Hayes, J., Melis, L., Danezis, G., and De Cristofaro, E., LOGAN: Membership inference attacks against generative models, Proc. on Privacy Enhancing Technologies Symp., Barcelona, 2018, De Gruyter, 2018, pp. 133–152.
[17]
Bellovin S.M., Dutta P.K., and Reitinger N. Privacy and synthetic datasets Standford Tech. L. Rev. 2019 22 1
[18]
Peterson L.E. K-nearest neighbor Scholarpedia 2009 4 1883
[19]
Oshiro, T., Perez, P.S., and Baranauskas, J.A., How many trees in a random forest?, Machine Learning and Data Mining in Pattern Recognition. MLDM 2012, Perner, P., Ed., Lecture Notes in Computer Science, vol. 7376, Berlin: Springer, 2012, pp. 154–168.
[20]
Large-scale CelebFaces Attributes (CelebA) Dataset. https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html/. Cited February 22, 2022.
[21]
Hospital Discharge Data Public Use Data File. https://www.dshs.texas.gov/THCIC/Hospitals/Download.shtm/. Cited February 22, 2022.
Index Terms
- Generation of Synthetic Data for Honeypot Systems Using Deep Learning Methods
Index terms have been assigned to the content through auto-classification.
Recommendations
Malware detection using adaptive data compression
AISec '08: Proceedings of the 1st ACM workshop on Workshop on AISecA popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of ...
Honeypot detection in advanced botnet attacks
Botnets have become one of the major attacks in the internet today due to their illicit profitable financial gain. Meanwhile, honeypots have been successfully deployed in many computer security defence systems. Since honeypots set up by security ...
Constructing Dynamic Honeypot Using Machine Learning
ICCSIE '23: Proceedings of the 8th International Conference on Cyber Security and Information EngineeringHoneypot is an active security defense technology that uses false information to lure attackers into attacking and record their behavior. Traditional honeypots are usually static, and inherent features and services can accelerate attackers' recognition ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© Allerton Press, Inc. 2022. ISSN 0146-4116, Automatic Control and Computer Sciences, 2022, Vol. 56, No. 8, pp. 916–926. © Allerton Press, Inc., 2022. Russian Text © The Author(s), 2022, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.
Publisher
Allerton Press, Inc.
United States
Publication History
Published: 01 December 2022
Accepted: 22 February 2022
Revision received: 16 February 2022
Received: 09 February 2022
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 17 Jan 2025