Detection of sources of network attacks based on the data sampling

Cover Page

Cite item

Full Text

Abstract

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.

About the authors

Evgeny S. Sagatov

Sevastopol State University

ORCID iD: 0000-0001-9780-8302
Scopus Author ID: 36802472700
ResearcherId: B-6527-2017
33 Universitetskaya St., Sevastopol 299053, Russia

Andrei Mikhailovich Sukhov

Sevastopol State University

ORCID iD: 0000-0001-6948-4988
Scopus Author ID: 54790189900
ResearcherId: K-4191-2013
33 Universitetskaya St., Sevastopol 299053, Russia

Vadim V. Azhmyakov

Sevastopol State University

ORCID iD: 0000-0003-3634-6786
Scopus Author ID: 57193314969
ResearcherId: J-6247-2016
33 Universitetskaya St., Sevastopol 299053, Russia

References

  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, 2017, vol. 201, pp. 417–427. https://doi.org/10.1016/j.proeng.2017.09.666
  2. Claise B. Cisco systems netflow services export version 9. 2004. https://doi.org/10.17487/rfc3954
  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, 2014, vol. 62, pp. 122–136. https://doi.org/10.1016/j.bjp.2013.10.014
  4. Li B., Springer J., Bebis G., Gunes M. H. A survey of network flow applications. Journal of Network and Computer Applications, 2013, vol. 36, iss. 2, pp. 567–581. https://doi.org/10.1016/j.jnca.2012.12.020
  5. Feinstein L., Schnackenberg D., Balupari R., Kindred D. Statistical approaches to DDoS attack detection and response. In: Proceedings DARPA Information Survivability Conference and Exposition. Washington, DC, USA, 2003, vol. 1, pp. 303–314. https://doi.org/10.1109/DISCEX.2003.1194894
  6. David J., Thomas C. DDoS attack detection using fast entropy approach on flow-based network traffic. Procedia Computer Science, 2015, vol. 50, pp. 30–36. https://doi.org/10.1016/j.procs.2015.04.007
  7. David J., Thomas C. Efficient DDoS flood attack detection using dynamic thresholding on flow-based network traffic. Computers & Security, 2019, vol. 82, pp. 284–295. https://doi.org/10.1016/j.cose.2019.01.002
  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, 2020, vol. 51, art. 102457. https://doi.org/10.1016/j.jisa.2020.102457
  9. Garcia-Teodoro P., Diaz-Verdejo J., Macia-Fernandez G., Vazquez E. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 2009, vol. 28, iss. 1–2, pp. 18–28. https://doi.org/10.1016/j.cose.2008.08.003
  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, 2016, vol. 9, iss. 6, pp. 339–350. https://doi.org/10.14257/ijfgcn.2016.9.6.32
  11. D’Cruze H., Wang P., Sbeit R. O., Ray A. A software-defined networking (SDN) approach to mitigating DDoS attacks. In: Latifi S. (ed.) Information Technology – New Generations, Advances in Intelligent Systems and Computing, vol. 558. Springer, Cham, 2018, pp. 141–145. https://doi.org/10.1007/978-3-319-54978-1_19
  12. Bekeneva Ya. A. Analysis of DDoS-attacks topical types and protection methods against them. Proceedings of Saint Petersburg Electrotechnical University Journal, 2016, vol. 1, pp. 7–14 (in Russian). EDN: TGYPJD
  13. Zakharov A. A., Popov E. F., Fuchko M. M. SDN architecture, cyber security aspects. Vestnik SibGUTI, 2016, iss. 1, pp. 83–92 (in Russian). EDN: WLSRVP
  14. Glassman S. A caching relay for the world wide web. Computer Networks and ISDN Systems, 1994, vol. 27, iss. 2, pp. 165–173. https://doi.org/10.1016/0169-7552(94)90130-9
  15. Wang D., Cheng H., Wang P., Huang X., Jian G. Zipf’s law in passwords. IEEE Transactions on Information Forensics and Security, 2017, vol. 12, iss. 11, pp. 2776–2791. https://doi.org/10.1109/TIFS.2017.2721359
  16. Zhang S., Sun W., Liu J., Nei K. Physical layer security in large-scale probabilistic caching: Analysis and optimization. IEEE Communications Letters, 2019, vol. 23, iss. 9, pp. 1484–1487. https://doi.org/10.1109/LCOMM.2019.2926967

Supplementary files

Supplementary Files
Action
1. JATS XML
Download


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).