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Development of the Intrusion Detection System for the Internet of Things Based on a Sequence Alignment Algorithm

Kalinin, M. O., Krundyshev, V. M., Sinyapkin, B. G.·2020·Automatic Control and Computer Sciences
DOI 10.3103/s0146411620080155
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Abstract

This work presents the method for traffic analysis based on the Needleman–Wunsch global algorithm of sequence alignment. A prototype of the intrusion detection system for the Internet of Things has been developed. Results of the experiments show the proposed approach is promising.

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Research works printed but not linked: 23

  1. IoT under fire: Kaspersky detects more than 100 million attacks on smart devices in H1 2019, 2019. https://www.kaspersky.com/about/press-releases/2019_iot-under-fire-kaspersky-detects-more-than-100-million-attacks-on-smart-devices-in-h1-2019. Accessed April 25, 2020.
  2. Lavrova, D., Pechenkin, A., and Gluhov, V., Applying correlation analysis methods to control flow violation detection in the Internet of Things, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 735–740. · DOI
  3. 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.
  4. Platonov, V.V. and Semenov, P.O., An adaptive model of a distributed intrusion detection system, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 894–898 · DOI
  5. Ovasapyan, T.D., Moskvin, D.A., and Kalinin, M.O., Using neural networks to detect internal intruders in VANETs, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 954–958. · DOI
  6. Lavrova, D., Zegzhda, D., and Yarmak, A., Using GRU neural network for cyber-attack detection in automated process control systems, 2019 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom, 2019, https://doi.org/10.1109/BlackSeaCom.2019.8812818 · DOI
  7. Zegzhda, D., Lavrova, D., and Khushkeev, A., Detection of information security breaches in distributed control systems based on values prediction of multidimensional time series, 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019, 2019, pp. 780–784. · DOI
  8. 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 International Russian Automation Conference, RusAutoCon, 2018. https://doi.org/10.1109/RUSAUTOCON.2018.8501783 · DOI
  9. Platonov, V.V. and Semenov, P.O., Using data-mining methods to detect network attacks, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 766–769. · DOI
  10. Markov, Y.A., Kalinin, M.O., and Zegzhda, D.P., A technique of abnormal behavior detection with genetic sequences alignment algorithms, International Conference on Enterprise Information Systems and Web Technologies 2010, 2010, pp. 104–110.
  11. Markov, Y.A. and Kalinin, M.O., Intellectual intrusion detection with sequences alignment methods, Lect. Notes Comput. Sci., 2010, vol. 6258, pp. 217–228. · DOI
  12. Coull, S., Branch, J., Szymański, B., and Breimer, E., Intrusion detection: A bioinformatics approach, Proc. 19th Ann. Comput. Secur. Appl. Conf., Washington, 2003.
  13. Zegzhda, P.D., Kort, S.S., and Suprun, A.F., Detection of anomalies in behavior of the software with usage of markov chains, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 820–825. https://doi.org/10.3103/S0146411615080386 · DOI
  14. Malyshev, E.V., Moskvin, D.A., and Zegzhda, D.P., Application of an artificial neural network for detection of attacks in VANETs, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 889–894. · DOI
  15. 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. · DOI
  16. 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. · DOI
  17. Lavrova, D.S. and Vasil’ev, Y.S., An ontological model of the domain of applications for the Internet of Things in analyzing information security, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 817–823. · DOI
  18. Trabelsi, Z. and Hamdy, R., Implementation of a DNA-based anomaly identification system utilizing associative string processor (ASP), ACS/IEEE International Conference on Computer Systems and Applications – AICCSA 2010, Hammamet, 2010, pp. 1–8. · DOI
  19. Chakraborty, A. and Bandyopadhyay, S., FOGSAA: Fast optimal global sequence alignment algorithm, Sci. Rep., 2013, no. 3, p. 1746. · DOI
  20. Galvez, S., Diaz, D., Hernandez, P., Esteban, F.J., Caballero, J.A., and Dorado, G., Next-generation bioinformatics: Using many-core processor architecture to develop a web service for sequence alignment, Bioinformatics, 2010, no. 26, pp. 683–686. · DOI
  21. Al-Ibaisi, T., Abu-Dalhoum, A., Al-Rawi, M., Alfonseca, M., and Ortega, A., Network intrusion detection using genetic algorithm to find best DNA signature, WSEAS Trans. Syst., 2008, vol. 7, no. 7, pp. 589–599.
  22. Kang, H., Ahn, D., et al., IoT network intrusion dataset, IEEE Dataport, 2019. https://ieee-dataport.org/open-access/iot-network-intrusion-dataset. Accessed April 25, 2020.
  23. Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B., Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset, 2018. https://arxiv.org/ abs/1811.00701. · DOI · Paper page

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Metadata

Authors
Kalinin, M. O., Krundyshev, V. M., & Sinyapkin, B. G.
Venue
Automatic Control and Computer Sciences
Publisher
Allerton Press
Published
2020-12
Volume
54
Issue
8
Pages
993-1000
ISSN
  • 0146-4116
  • 1558-108X
DOI
10.3103/s0146411620080155
Cited by
4
References
0
Language
en
Licence

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CoChat. (2026). Citation check: Development of the Intrusion Detection System for the Internet of Things Based on a Sequence Alignment Algorithm. https://app.cochat.ai/papers/10.3103/s0146411620080155
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Kalinin, M. O., Krundyshev, V. M., & Sinyapkin, B. G. (2020). Development of the Intrusion Detection System for the Internet of Things Based on a Sequence Alignment Algorithm. Automatic Control and Computer Sciences. https://doi.org/10.3103/s0146411620080155