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RETRACTED ARTICLE: Intrusion detection and performance simulation based on improved sequential pattern mining algorithm

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This article was retracted on 01 December 2022

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Abstract

Traditional network intrusion detection algorithm is based on pattern matching, which has made great progress in network intrusion detection system, but the efficiency of this algorithm for data packet matching is quite low. With the rapid increase of Internet scale and capacity, the general information security problem appears, and it brought hidden danger for an open network security. In this paper, the author analyse the intrusion detection and performance simulation based on improved sequential pattern mining algorithm. We integrate the data mining algorithms to implement the IDS, and the simulation result reflects the effectiveness of the methodology. The simulation shows that when minimum support is very small, PrefixSpan running time running a lot less time than other algorithm, and the difference between the two is obvious. Due to the mining algorithm of the relative independence of intrusion detection system, algorithm does not depend on the specific data and specific system, so the intrusion detection system based on data mining to data source requirement is very low.

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References

  1. Kim, K., Roh, M., Ha, S.: Expert system based on the arrangement evaluation model for the arrangement design of a submarine. Expert Syst. Appl. 42(22), 8731–8744 (2015)

    Article  Google Scholar 

  2. Shidpour, H., Cunha, C., Bernard, A.: Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory. Expert Syst. Appl. 64, 633–644 (2016)

    Article  Google Scholar 

  3. Vinodh, S., Kamala, V., Jayakrishna, K.: Application of fuzzy axiomatic design methodology for selection of design alternatives. J. Eng. Design Technol. 13(1), 2–22 (2015)

    Article  Google Scholar 

  4. George, A., Binu, D.: DRL-Prefixspan: a novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns. Open Comput. Sci. 2(4), 426–439 (2012)

    Article  Google Scholar 

  5. Goel, A., Mallick, B.: Customer purchasing behavior using sequential pattern mining technique. Int. J. Comput. Appl. 119, 54–60 (2015)

    Google Scholar 

  6. Hegde, G., Govilkar, S.: Study on parallel algorithm for sequential pattern mining. Proc. SPIE 2, 657–670 (2012)

    Google Scholar 

  7. Hijawi, H.M., Saheb, M.H.: Sequence pattern mining in data streams. Comput. Inform. Sci. 8(3), 64–68 (2015)

    Article  Google Scholar 

  8. Wang, H.: Cost control analysis of cold chain transportation of tropical fruits and fresh products. Arch. Latinoam. Nutr. 69(1), 289–300 (2019)

    Google Scholar 

  9. Wensun, C., Jiashu, Y.: Clinical effect of levofloxacin on prevention of infection in patients with multiple bone and joint trauma. Bol. Malariol. Salud Amb. 59(2), 252–258 (2019)

    Google Scholar 

  10. Jia-xin, L.I.U.: An interactive sequential patterns mining algorithm based on frequent sequence tree. Comput. Technol. Dev. 5, 10–17 (2012)

    Google Scholar 

  11. Kaur, D., Kaur, R.: Minimizing the repeated database scan using an efficient frequent pattern mining algorithm in web usage mining. Int. J. Res. Adv. Technol. 2(6), 2321–9637 (2014)

    Google Scholar 

  12. Kumar, A., Kumar, V.: MISP (Modified IncSpan+): incremental mining of sequential patterns. Int. J. Comput. Appl. 65(8), 40–45 (2013)

    Google Scholar 

  13. Li, J., Hao, H., Hao, F.: The prefix span algorithm research of synthetic decision support system based on internet of things. In: Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium, vol. 1, pp. 174–176. (2014)

  14. Pereira, C., Ferreira, C.: Identification of IT value management practices and resources in COBIT 5. RISTI-Revis. Ibér. Sist. Tecnol. Inform. 15, 17–33 (2015)

    Google Scholar 

  15. Wu, J., Xie, S.: VDifference of fresh fruit storage quality at room temperature and influencing factors of consumption trust. Arch. Latinoam. Nutr. 69(1), 272–280 (2019)

    Google Scholar 

  16. Parimala, M., Sathiyabama, S.: SPMLS: an efficient sequential pattern mining algorithm with candidate generation and frequency testing. Int. J. Comput. Sci. Eng. 4(4), 590–601 (2012)

    Google Scholar 

  17. Schweizer, D., Zehnder, M., Wache, H., Witschel, H. F., Zanatta, D., Rodriguez, M.: Using consumer behavior data to reduce energy consumption in smart homes: applying machine learning to save energy without lowering comfort of inhabitants. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 1123–1129. (2015)

  18. Song, W., Yang, K.: Discovering sequential patterns with various time constraints. J. Comput. Inform. Syst. 9(15), 6047–6054 (2013)

    Google Scholar 

  19. Angelo, M., Palhares, R., Filho, M., Maia, R.: A new fault classification approach applied to tennessee eastman benchmark process. Appl. Soft Comput. 49, 676–686 (2016)

    Article  Google Scholar 

  20. Cerrada, M., Sánchez, R., Pacheco, F., Cabrera, D.: Hierarchical feature selection based on relative dependency for gear fault diagnosis. Appl. Intell. 44, 687–703 (2016)

    Article  Google Scholar 

  21. Neng, Lu: Dietary protein nutritional composition and improvement of muscle attenuation syndrome with exercise intervention. Arch. Latinoam. Nutr. 69(1), 281–288 (2019)

    Google Scholar 

  22. Rocio, S., Vanete, O., Nicacio, M.: Optimal combinations of target intervention for diseases. Bol. Malariol. Salud Amb. 59(1), 1–9 (2019)

    Google Scholar 

  23. Jales, B., Parvanov, P., Affonso, L.: Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier. Neurocomputing 150, 289–303 (2015)

    Article  Google Scholar 

  24. Fan, J., Wang, Y.: Fault detection and diagnosis of non-linear non-gaussian dynamic processes using kernel dynamic independent component analysis. Inform. Sci. 259, 369–379 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  25. Gomes, C., Jales, B., Affonso, L., Parvanov, P.: An evolving approach to unsupervised and real-time fault detection in industrial processes. Expert Syst. Appl. 63, 134–144 (2016)

    Article  Google Scholar 

  26. Treur, J.: Dynamic modeling based on a temporal–causal network modeling approach. Biol. Inspir. Cogn. Arc. 16, 131–168 (2016)

    Google Scholar 

  27. Wang, L., Meng, J., Xu, P.: Mining temporal association rules with frequent itemsets tree. Appl. Soft Comput. 62, 817–829 (2018)

    Article  Google Scholar 

  28. Mishra, D., Samantaray, S., Joos, G.: A combined wavelet and data-mining based intelligent protection scheme for microgrid. IEEE Trans. Smart Grid 7(5), 2295–2304 (2016)

    Article  Google Scholar 

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Acknowledgement

This paper was supported by (1) National Natural Science Foundation of China (31702232); (2) Education teaching reform project of Henan(2019-JSJYYB-056); (3) Education teaching reform project of Zhoukou normal university (J2019002).

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Authors and Affiliations

  1. School of Mathematics and Statistics, ZhouKou Normal University, Zhoukou, China

    Yazi Wang

  2. College of Mechanical and Electrical Engineering, ZhouKou Normal University, Zhoukou, China

    Yingbo Liang

  3. School of Mathematics and Statistics, Fuyang Normal University, Fuyang, China

    Huaibo Sun

  4. College of Network Engineering, ZhouKou Normal University, Zhoukou, China

    Yuankun Ma

Authors
  1. Yazi Wang
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  2. Yingbo Liang
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  3. Huaibo Sun
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  4. Yuankun Ma
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Corresponding author

Correspondence to Huaibo Sun.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s10586-022-03832-8

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Wang, Y., Liang, Y., Sun, H. et al. RETRACTED ARTICLE: Intrusion detection and performance simulation based on improved sequential pattern mining algorithm. Cluster Comput 23, 1927–1936 (2020). https://doi.org/10.1007/s10586-020-03129-8

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  • DOI: https://doi.org/10.1007/s10586-020-03129-8

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