Artificial Intelligence-Based Traffic Anomaly Detection
https://doi.org/10.31854/1813-324X-2025-11-5-9-20
EDN: WALXIJ
Abstract
Relevant. Nowadays to detect signs of abnormal traffic behavior signature analysis is used, but this method has its limitations. Given the disadvantages of signature analysis, it becomes clear that using this method alone can limit the ability to detect and prevent new and unknown anomalies. Considered implementation of a custom analysis in addition to the signature to provide a more complete and reliable information system protection.
The aim of the study is to increase the efficiency of detecting signs of abnormal traffic behavior through the use of artificial intelligence methods.
In result the following were developed: an algorithm for detecting network anomalies, a software tool "Detection of network anomalies based on methods of artificial intelligence", a software stand.
The novelty of the study lies in the fact that the software allows you to calculate the criteria for detecting anomalies of network traffic in a period of time shorter than that of previously presented analogs and allows you to detect various anomalies without prior training on ready-made anomaly templates.
The practical significance. The results obtained in the work can be used for classification of anomalies of network traffic in information systems and infrastructures.
About the Authors
M. V. Bliznyuk
Federal Security Service of the Russian Federation in the North-Western Federal District
Russian Federation
Russian Federation
V. I. Bliznyuk
Academy of the Federal Guard Service of the Russian Federation
Russian Federation
Russian Federation
A. P. Postarnak
The Bonch-Bruevich Saint Petersburg State University of Telecommunications
Russian Federation
Russian Federation
A. V. Bolbenkov
Academy of the Federal Guard Service of the Russian Federation
Russian Federation
Russian Federation
A. Yu. Kibalin
Academy of the Federal Guard Service of the Russian Federation
Russian Federation
Russian Federation
References
1. Dainotti A., Benson K., King A., Claffy K.C., Kallitsis M., Glatz E., et al. Estimating Internet Address Space Usage Through Passive Measurements. ACM SIGCOMM Computer Communication Review. 2011;41(2):30–37. DOI:10.1145/2567561.2567568
2. Lazarevic A., Kumar V. Feature Bagging for Outlier Detection. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD '05, 21–24 August 2005, Chicago, USA. New York: Association for Computing Machinery; 2005. p.157–166. DOI:10.1145/1081870.1081891
3. Talukder M.A., Islam M.M., Uddin M.A., Hasan K.F., Sharmin S., Alyami S.A. Machine learning‑based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. Journal of Big Data. 2024;11:11. DOI:10.1186/s40537-024-00886-w
4. Shaburov A.S., Nikitin A.S. The model for detecting computer attacks on objects of critical information infrastructure. Bulletin of Perm National Research Polytechnic University. Electrical engineering, information technologies, control systems. 2019;29:104–117. (in Russ.) EDN:ZBKJTN
5. Bugorsky M., Kaplin M., Ostrotsky S., Kazakova O., Selin V. Features of using critical information infrastructure facilities with a modern intrusion detection system. Sciences of Europe. 2021;66-1(66):42–46. (in Russ.) DOI:10.24412/3162-2364-2021-66-1-42-46. EDN:SXGMHB
6. Semenov V.V., Arustamov S.A. Risk identification of security information violations in cyber-physical systems based on analysis of digital signals. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2020;20(5):770–772. (in Russ.) DOI:10.17586/2226-1494-2020-20-5-770-772. EDN:BHITPY
7. Mirkovic J., Prier G., Reiher P. Attacking DDoS at the Source. Proceedings of the 10th IEEE International Conference on Network Protocols, 12–15 November 2002, Paris, France. IEEE; 2002. p.312–321. DOI:10.1109/ICNP.2002.1181418
8. Ahmed M., Mahmood A.N., Hu J. A survey of network anomaly detection techniques. Journal of Network and Computer Applications. 2016;60:19–31. DOI:10.1016/j.jnca.2015.11.016
9. Alali A., Yousef M. A Survey on Intrusion Detection Systems (IDS) Using Machine Learning Algorithms. Journal of Xi’an Shiyou University. 2022:18(6):183–197.
10. Chandola V., Banerjee A., Kumar V. Anomaly Detection: A Survey. ACM Computing Surveys. 2009;41(3):1–58. DOI:10.1145/1541880.1541882. EDN:MYREHF
11. Jordan M.I., Mitchell T.M. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–260. DOI:10.1126/science.aaa8415
Review
For citations:
Bliznyuk M.V., Bliznyuk V.I., Postarnak A.P., Bolbenkov A.V., Kibalin A.Yu. Artificial Intelligence-Based Traffic Anomaly Detection. Proceedings of Telecommunication Universities. 2025;11(5):9-20. (In Russ.) https://doi.org/10.31854/1813-324X-2025-11-5-9-20. EDN: WALXIJ
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