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Published in: Automatic Control and Computer Sciences 8/2020

01-12-2020

Detection of Distributed Denial of Service Attacks in Large-Scale Networks Based on Methods of Mathematical Statistics and Artificial Intelligence

Author: I. V. Alekseev

Published in: Automatic Control and Computer Sciences | Issue 8/2020

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Abstract

It is proposed to use the mathematical apparatus of wavelet transforms combined with the clustering of the obtained and transformed coefficients to detect attacks in the traffic of backbone networks. The wavelet transform coefficients obtained from the parameters of network packets are checked for the degree of multiple dependence, on the basis of which the standard deviation is calculated and the resulting coefficients are clustered to identify anomalies of the investigated network flow. The efficiency of the proposed method is confirmed by the results of experiments on detecting denial of service attacks.
Literature
1.
go back to reference Anstee, D., Chui, C.F., Bowen, P., and Sockrider, G., Worldwide Infrastructure Security Report, Westford, MA: Arbor Networks Inc., 2017. Anstee, D., Chui, C.F., Bowen, P., and Sockrider, G., Worldwide Infrastructure Security Report, Westford, MA: Arbor Networks Inc., 2017.
2.
go back to reference Vasiliev, Y.S., Zegzhda, P.D., and Kuvshinov, V.I., Modern problems of cybersecurity, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2014, vol. 17, no. 3, pp. 210–214. Vasiliev, Y.S., Zegzhda, P.D., and Kuvshinov, V.I., Modern problems of cybersecurity, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2014, vol. 17, no. 3, pp. 210–214.
5.
go back to reference Belenko, V., Krundyshev, V., and Kalinin, M., Intrusion detection for Internet of Things applying metagenome fast analysis, Proceedings of the 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4, 2019, pp. 129–135. Belenko, V., Krundyshev, V., and Kalinin, M., Intrusion detection for Internet of Things applying metagenome fast analysis, Proceedings of the 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4, 2019, pp. 129–135.
6.
go back to reference Zegzhda, P., Zegzhda, D., Kalinin, M., Pechenkin, A., Minin, A., and Lavrova, D., Safe integration of SIEM systems with Internet of Things: Data aggregation, integrity control, and bioinspired safe routing, ACM International Conference Proceeding Series, 2016, pp. 81–87. Zegzhda, P., Zegzhda, D., Kalinin, M., Pechenkin, A., Minin, A., and Lavrova, D., Safe integration of SIEM systems with Internet of Things: Data aggregation, integrity control, and bioinspired safe routing, ACM International Conference Proceeding Series, 2016, pp. 81–87.
7.
go back to reference Cao, Y., et al., Understanding internet DDoS mitigation from academic and industrial perspectives, IEEE Access, 2018, no. 6, pp. 66641–66648. Cao, Y., et al., Understanding internet DDoS mitigation from academic and industrial perspectives, IEEE Access, 2018, no. 6, pp. 66641–66648.
8.
go back to reference Pulse Wave Heavy DDoS Attack to Take Down Multiple Protected Target Networks. https://gbhackers.com/ new-ddos-attack-pulse-wave/. Pulse Wave Heavy DDoS Attack to Take Down Multiple Protected Target Networks. https://​gbhackers.​com/​ new-ddos-attack-pulse-wave/.
9.
go back to reference Krundyshev, V., Kalinin, M., and Zegzhda, P., Artificial swarm algorithm for VANET protection against routing attacks, IEEE Industrial Cyber-Physical Systems, 2018, pp. 795–800.CrossRef Krundyshev, V., Kalinin, M., and Zegzhda, P., Artificial swarm algorithm for VANET protection against routing attacks, IEEE Industrial Cyber-Physical Systems, 2018, pp. 795–800.CrossRef
10.
go back to reference Lavrova, D.S., Alekseev, I.V., and Shtyrkina, A.A., Security analysis based on controlling dependences of network traffic parameters by wavelet transformation, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 931–935.CrossRef Lavrova, D.S., Alekseev, I.V., and Shtyrkina, A.A., Security analysis based on controlling dependences of network traffic parameters by wavelet transformation, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 931–935.CrossRef
11.
go back to reference Kozionov, A.P., Pyait, A.L., Mokhov, I.I., and Ivanov, Yu.P., Wavelet transform and one-class classification for monitoring the state of dams, Inf.-Upr. Sist., 2014, no. 4, pp. 24–32. Kozionov, A.P., Pyait, A.L., Mokhov, I.I., and Ivanov, Yu.P., Wavelet transform and one-class classification for monitoring the state of dams, Inf.-Upr. Sist., 2014, no. 4, pp. 24–32.
12.
go back to reference Li, L. and Lee, G., DDoS attack detection and wavelets, Telecommun. Syst., 2005, vol. 28, nos. 3–4, pp. 435–451.CrossRef Li, L. and Lee, G., DDoS attack detection and wavelets, Telecommun. Syst., 2005, vol. 28, nos. 3–4, pp. 435–451.CrossRef
13.
go back to reference Tian, X., Wu, J., and Ji, C., A unified framework for understanding network traffic using independent wavelet models, Proceedings. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, New York, 2002, vol. 1, pp. 446–454; Pescapè, A. and Ventre, G., Wavelet-based detection of DoS attacks, IEEE Globecom, San Francisco, CA, 2006, pp. 1–6. Tian, X., Wu, J., and Ji, C., A unified framework for understanding network traffic using independent wavelet models, Proceedings. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, New York, 2002, vol. 1, pp. 446–454; Pescapè, A. and Ventre, G., Wavelet-based detection of DoS attacks, IEEE Globecom, San Francisco, CA, 2006, pp. 1–6.
14.
go back to reference Lima Filho, F.S., et al., Smart detection: An online approach for DoS/DDoS attack detection using machine learning, Secur. Commun. Networks, 2019, vol. 2019; Bhaya, W.S. and Ebadymanna, M., DDoS attack detection approach using an efficient cluster analysis in large data scale, 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), Baghdad, 2017, pp. 168–173. Lima Filho, F.S., et al., Smart detection: An online approach for DoS/DDoS attack detection using machine learning, Secur. Commun. Networks, 2019, vol. 2019; Bhaya, W.S. and Ebadymanna, M., DDoS attack detection approach using an efficient cluster analysis in large data scale, 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), Baghdad, 2017, pp. 168–173.
15.
go back to reference Tang, D., et al., Low-rate DoS attack detection based on two-step cluster analysis, International Conference on Information and Communications Security, Lille, 2018, pp. 92–104. Tang, D., et al., Low-rate DoS attack detection based on two-step cluster analysis, International Conference on Information and Communications Security, Lille, 2018, pp. 92–104.
16.
go back to reference Lavrova, D., Zegzhda, D., and Yarmak, A., Using GRU neural network for cyber-attack detection in automated process control systems, IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sochi, 2019, pp. 1–3. Lavrova, D., Zegzhda, D., and Yarmak, A., Using GRU neural network for cyber-attack detection in automated process control systems, IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sochi, 2019, pp. 1–3.
17.
go back to reference Ivanov, D.V. and Moskvin, D.A., Application of fractal methods to ensure the cyber-resilience of self-organizing networks, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 4, pp. 336–341. Ivanov, D.V. and Moskvin, D.A., Application of fractal methods to ensure the cyber-resilience of self-organizing networks, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 4, pp. 336–341.
18.
go back to reference Lavrova, D., Zaitceva, E., and Zegzhda, P., Bio-inspired approach to self-regulation for industrial dynamic network infrastructure, CEUR Workshop Proc., 2019, vol. 2603, pp. 34–39. Lavrova, D., Zaitceva, E., and Zegzhda, P., Bio-inspired approach to self-regulation for industrial dynamic network infrastructure, CEUR Workshop Proc., 2019, vol. 2603, pp. 34–39.
19.
go back to reference Lavrova, D., Zegzhda, D., and Yarmak, A., Predicting cyber attacks on industrial systems using the Kalman filter, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 317–321. Lavrova, D., Zegzhda, D., and Yarmak, A., Predicting cyber attacks on industrial systems using the Kalman filter, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 317–321.
20.
go back to reference Zegzhda, D., Lavrova, D., and Poltavtseva, M., Multifractal security analysis of cyberphysical systems, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 2, pp. 196–204. Zegzhda, D., Lavrova, D., and Poltavtseva, M., Multifractal security analysis of cyberphysical systems, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 2, pp. 196–204.
21.
go back to reference DARPA Intrusion Detection Evaluation. Intrusion Detection Attacks Database. https://archive.ll.mit.edu/ideval/ data/1999/training/week2/index.html. DARPA Intrusion Detection Evaluation. Intrusion Detection Attacks Database. https://​archive.​ll.​mit.​edu/​ideval/​ data/1999/training/week2/index.html.
22.
go back to reference Canadian Institute for Cybersecurity DDoS Evaluation Dataset (CICDDoS2019). https://www.unb.ca/ cic/datasets/ddos-2019.html. Canadian Institute for Cybersecurity DDoS Evaluation Dataset (CICDDoS2019). https://​www.​unb.​ca/​ cic/datasets/ddos-2019.html.
Metadata
Title
Detection of Distributed Denial of Service Attacks in Large-Scale Networks Based on Methods of Mathematical Statistics and Artificial Intelligence
Author
I. V. Alekseev
Publication date
01-12-2020
Publisher
Pleiades Publishing
Published in
Automatic Control and Computer Sciences / Issue 8/2020
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI
https://doi.org/10.3103/S0146411620080052

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