Application of Bioinformatics Algorithms for Polymorphic Cyberattacks Detection

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Abstract

The functionality of any system can be represented as a set of commands that lead to a change in the state of the system. The intrusion detection problem for signature-based intrusion detection systems is equivalent to matching the sequences of operational commands executed by the protected system to known attack signatures. Various mutations in attack vectors (including replacing commands with equivalent ones, rearranging the commands and their blocks, adding garbage and empty commands into the sequence) reduce the effectiveness and accuracy of the intrusion detection. The article analyzes the existing solutions in the field of bioinformatics and considers their applicability for solving the problem of identifying polymorphic attacks by signature-based intrusion detection systems. A new approach to the detection of polymorphic attacks based on the suffix tree technology applied in the assembly and verification of the similarity of genomic sequences is discussed. The use of bioinformatics technology allows us to achieve high accuracy of intrusion detection at the level of modern intrusion detection systems (more than 0.90), while surpassing them in terms of cost-effectiveness of storage resources, speed and readiness to changes in attack vectors. To improve the accuracy indicators, a number of modifications of the developed algorithm have been carried out, as a result of which the accuracy of detecting attacks increased by up to 0.95 with the level of mutations in the sequence up to 10%. The developed approach can be used for intrusion detection both in conventional computer networks and in modern reconfigurable network infrastructures with limited resources (Internet of Things, networks of cyber-physical objects, wireless sensor networks).

About the authors

D. P Zegzhda

Peter the Great St.Petersburg Polytechnic University

Email: dmitry@ibks.spbstu.ru
Politekhnicheskaya St. 29

M. O Kalinin

Peter the Great St.Petersburg Polytechnic University

Email: max@ibks.spbstu.ru
Politekhnicheskaya ul. 29

V. M Krundyshev

Peter the Great St.Petersburg Polytechnic University

Email: vmk@ibks.spbstu.ru
Politekhnicheskaya St. 29

D. S Lavrova

Peter the Great St.Petersburg Polytechnic University

Email: lavrova@ibks.spbstu.ru
Politekhnicheskaya St. 29

D. A Moskvin

Peter the Great St. Petersburg Polytechnic University

Email: moskvin@ibks.spbstu.ru
Politekhnicheskaya St. 29

E. Yu Pavlenko

Peter the Great St.Petersburg Polytechnic University

Email: pavlenko@ibks.spbstu.ru
Politekhnicheskaya St. 29

References

  1. Khraisat A., Gondal I., Vamplew P., Kamruzzaman J. Survey of intrusion detection systems: techniques, datasets and challenges // Cybersecurity. 2019. vol. 2. no. 1.
  2. Jatti S.A.V., Kishor Sontif V.J.K. Intrusion detection systems // International Journal of Recent Technology and Engineering. 2019. vol. 8. no. 2. special is-sue 11. pp. 3976–3983.
  3. Branitskiy A.A., Kotenko I.V. Analysis and classification of methods for net-work attack detection // SPIIRAS Proceedings. 2016. vol. 2. no. 45. pp. 207–244.
  4. Lakshminarayana D.H., Philips J., Tabrizi N. A survey of intrusion detection techniques // In Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. 2019. pp. 1122–1129.
  5. Platonov V.V., Semenov P.O. An adaptive model of a distributed intrusion detection system // Automatic Control and Computer Sciences. 2017. vol. 51. no. 8. pp. 894–898.
  6. Platonov V.V., Semenov P.O. Detection of Abnormal Traffic in Dynamic Com-puter Networks with Mobile Consumer Devices // Automatic Control and Computer Sciences, 2018. vol. 52. no. 8. pp. 959–964.
  7. Aljawarneh S.A., Moftah R.A., Maatuk A.M. Investigations of automatic methods for detecting the polymorphic worms signatures // Future Generation Computer Systems. 2016. vol. 60. pp. 67–77.
  8. Khonde S.R., Venugopal U. Hybrid architecture for distributed intrusion detec-tion system // Ingenierie des Systemes d’Information. 2019. vol. 24. no. 1. pp. 19–28.
  9. Zhang W.A., Hong Z., Zhu J.W., Chen B. A survey of network intrusion detection methods for industrial control systems // Kongzhi yu Juece/Control and Decision. 2019. vol. 34. no. 11. pp. 2277–2288.
  10. Seoane Fernández J.A., Miguélez Rico M. Bio-Inspired Algorithms in Bioinformatics I // Encyclopedia of Artificial Intelligence. 2011.
  11. Levshun D, Gaifulina D., Chechulin A., Kotenko I. Problematic issues of in-formation security of cyber-physical systems // SPIIRAS Proceedings. 2020. vol. 19. no. 5. pp. 1050–1088.
  12. Coull S., Branch J., Szymanski B., Breimer E. Intrusion detection: A bioinformatics approach // In Proceedings Annual Computer Security Applications Conference, ACSAC. 2003. vol. 2003-January. pp. 24–33.
  13. Lavrova D., Zaitceva E., Zegzhda P. Bio-inspired approach to self-regulation for industrial dynamic network infrastructure // CEUR Workshop Proceedings. 2019. vol. 2603. pp. 34–39.
  14. Miller W. An Introduction to Bioinformatics Algorithms // Journal of the American Statistical Association. 2006. vol. 101. no. 474. pp. 855–855.
  15. Sohn J. Il, Nam J.W. The present and future of de novo whole-genome assembly // Briefings in Bioinformatics. 2018. vol. 19, no. 1, pp. 23–40.
  16. Recanati A., Brüls T., D’Aspremont A. A spectral algorithm for fast de novo layout of uncorrected long nanopore reads // Bioinformatics. 2017. vol. 33, no. 20. pp. 3188–3194.
  17. Rizzi R., et al. Overlap graphs and de Bruijn graphs: data structures for de novo genome assembly in the big data era // Quantitative Biology. 2019. vol. 7, no. 4. pp. 278–292.
  18. Wittler R. Alignment- And reference-free phylogenomics with colored de Bruijn graphs // Algorithms for Molecular Biology. 2020. vol. 15. no. 1.
  19. Tan T.W., Lee E. Sequence Alignment // Beginners Guide to Bioinformatics for High Throughput Sequencing. 2018. pp. 81–115.
  20. Muhamad F.N., Ahmad R.B., Asi S.M., Murad M.N. Performance Analysis of Needleman-Wunsch Algorithm (Global) and Smith-Waterman Algorithm (Lo-cal) in Reducing Search Space and Time for DNA Sequence Alignment // Journal of Physics: Conference Series. 2018. vol. 1019. no. 1.
  21. Lee Y.S., Kim Y.S., Uy R.L. Serial and parallel implementation of Needleman-Wunsch algorithm // International Journal of Advances in Intelligent Informatics. 2020. vol. 6. no. 1. pp. 97–108.
  22. Čavojský M., Drozda M., Balogh Z. Analysis and experimental evaluation of the Needleman-Wunsch algorithm for trajectory comparison // Expert Systems with Applications. 2021. vol. 165.
  23. Sun J., Chen K., Hao Z. Pairwise alignment for very long nucleic acid sequences // Biochemical and Biophysical Research Communications. 2018. vol. 502. no. 3. pp. 313–317.
  24. Zou H., Tang S., Yu C., Fu H., Li Y., Tang W. ASW: Accelerating Smith–Waterman Algorithm on Coupled CPU-GPU Architecture // International Journal of Parallel Programming. 2019. vol. 47. no. 3. pp. 388–402.
  25. Chowdhury B., Garai G. A review on multiple sequence alignment from the perspective of genetic algorithm // Genomics. 2017. vol. 109. no. 5–6. pp. 419–431.
  26. Dijkstra M.J.J., Van Der Ploeg A.J., Feenstra K. A., Fokkink W.J., Abeln S., Heringa J. Tailor-made multiple sequence alignments using the PRALINE 2 alignment toolkit // Bioinformatics. 2019. vol. 35. no. 24. pp. 5315–5317.
  27. Chen S., Yang S., Zhou M., Burd R., Marsic I. Process-Oriented Iterative Multiple Alignment for Medical Process Mining // In IEEE International Conference on Data Mining Workshops, ICDMW. 2017. vol. 2017-November. pp. 438–445.
  28. Ye N. Markov Chain Models and Hidden Markov Models // Data Mining. 2021. pp. 287–305.
  29. Behera N., Jeevitesh M.S., Jose J., Kant K., Dey A., Mazher J. Higher accuracy protein multiple sequence alignments by genetic algorithm // Procedia Comput-er Science. 2017. vol. 108. pp. 1135–1144.
  30. Cui X., Shi H., Zhao J., Ge Y., Yin Y., Zhao K. High Accuracy Short Reads Alignment Using Multiple Hash Index Tables on FPGA Platform // In Proceed-ings of 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, ITOEC. 2020. pp. 567–573.
  31. Marçais G., Delcher A.L., Phillippy A.M., Coston R., Salzberg S.L., Zimin A. MUMmer4: A fast and versatile genome alignment system // PLoS Computational Biology. 2018. vol. 14. no. 1. 2018.
  32. Kay M. Substring alignment using suffix trees // Lecture Notes in Computer Science. 2004. vol. 2945. pp. 275–282.
  33. Ukkonen E. On-line construction of suffix trees // Algorithmica. 1995. vol. 14. no. 3. pp. 249–260.
  34. Breslauer D., Italiano G.F. On suffix extensions in suffix trees // Theoretical Computer Science. 2012. vol. 457. pp. 27–34.
  35. KDD Cup 1999 Data: URL: kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (дата доступа: 10.04.2021).

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