Detection of sources of network attacks based on the data sampling
Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 24 (2024) no. 3, pp. 452-462.

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This article defines the rules for finding the threshold values for the main network variables used to detect network intrusions under conditions of limited data sampling. The sFlow technology operates with a limited sample of packets, and one packet out of 50 can be analyzed, but this value can reach 5000. The main conclusion is that the product of the threshold value and sample resolution remains a constant value. The article defines the size of the maximum resolution, at which an attack with a given threshold can be detected. Based on the experimental data, this hypothesis was tested; considering the experimental error, it was verified.
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E. S. Sagatov; A. M. Sukhov; V. V. Azmyakov. Detection of sources of network attacks based on the data sampling. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 24 (2024) no. 3, pp. 452-462. http://geodesic.mathdoc.fr/item/ISU_2024_24_3_a12/

[1] Sukhov A. M., Sagatov E. S., Baskakov A. V., “Rank distribution for determining the threshold values of network variables and the analysis of DDoS attacks”, Procedia Engineering, 201 (2017), 417–427 | DOI

[2] Claise B., Cisco systems netflow services export, version 9, 2004 | DOI

[3] Giotis K., Argyropoulos C., Androulidakis G., Kalogeras D., Maglaris V., “Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments”, Computer Networks, 62 (2014), 122–136 | DOI

[4] Li B., Springer J., Bebis G., Gunes M. H., “A survey of network flow applications”, Journal of Network and Computer Applications, 36:2 (2013), 567–581 | DOI

[5] Feinstein L., Schnackenberg D., Balupari R., Kindred D., “Statistical approaches to DDoS attack detection and response”, Proceedings DARPA Information Survivability Conference and Exposition (Washington, DC, USA, 2003), v. 1, 303–314 | DOI

[6] David J., Thomas C., “DDoS attack detection using fast entropy approach on flow-based network traffic”, Procedia Computer Science, 50 (2015), 30–36 | DOI

[7] David J., Thomas C., “Efficient DDoS flood attack detection using dynamic thresholding on flow-based network traffic”, Computers Security, 82 (2019), 284–295 | DOI

[8] Singh K., Dhindsa K. S., Nehra D., “T-CAD: A threshold based collaborative DDoS attack detection in multiple autonomous systems”, Journal of Information Security and Applications, 51 (2020), 102457 | DOI

[9] Garcia-Teodoro P., Diaz-Verdejo J., Maciá-Fernández G., Vázquez E., “Anomaly-based network intrusion detection: Techniques, systems and challenges”, Computers Security, 28:1–2 (2009), 18–28 | DOI

[10] Patel S. K., Sonker A., “Rule-based network intrusion detection system for port scanning with efficient port scan detection rules using snort”, International Journal of Future Generation Communication and Networking, 9:6 (2016), 339–350 | DOI

[11] D'Cruze H., Wang P., Sbeit R. O., Ray A., “A software-defined networking (SDN) approach to mitigating DDoS attacks”, Information Technology – New Generations, Advances in Intelligent Systems and Computing, 558, ed. Latifi S., Springer, Cham, 2018, 141–145 | DOI

[12] Bekeneva Ya. A., “Analysis of DDoS-attacks topical types and protection methods against them”, Proceedings of Saint Petersburg Electrotechnical University Journal, v. 1, 2016, 7–14 (in Russian)

[13] Zakharov A. A., Popov E. F., Fuchko M. M., “SDN architecture, cyber security aspects”, Vestnik SibGUTI, 2016, no. 1, 83–92 (in Russian)

[14] Glassman S., “A caching relay for the world wide web”, Computer Networks and ISDN Systems, 27:2 (1994), 165–173 | DOI

[15] Wang D., Cheng H., Wang P., Huang X., Jian G., “Zipf's law in passwords”, IEEE Transactions on Information Forensics and Security, 12:11 (2017), 2776–2791 | DOI

[16] Zhang S., Sun W., Liu J., Nei K., “Physical layer security in large-scale probabilistic caching: Analysis and optimization”, IEEE Communications Letters, 23:9 (2019), 1484–1487 | DOI