Skip to main content
SpringerLink
Log in

Using known nonself samples to improve negative selection algorithm

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Negative selection algorithm is the core algorithm of artificial immune system. It only uses the self for training and generates detectors to detect abnormalities. Holes are feature space areas that the detector fails to cover, it is the root cause of the performance degradation of the negative selection algorithm. The conventional method generates a large number of detectors randomly to repair the holes, which is time-consuming and not effective. To alleviate the problem, we propose a V-Detector-KN algorithm in this paper. V-Detector is the abbreviation of the real-valued negative selection algorithm with Variable-sized Detectors, KN represents Known Nonself. The V-Detector-KN algorithm uses the known nonself as the candidate detector to further generate the detector based on the V-Detector randomly generated detector, so as to realize the repair of holes. Compared with the conventional method to randomly generate detectors to repair holes, our proposed V-Detector-KN method uses known nonself to repair holes, reducing the randomness and blindness of hole repair. Theoretical analysis shows that the detection rate of our algorithm is not lower than that of the conventional V-Detector algorithm. The results of experiment comparing with other 6 algorithms on 7 UCI data sets show the superiority of our proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning[J]. Phys D Nonlinear Phenom 22(1-3):187–204

    Article  MathSciNet  Google Scholar 

  2. Klarreich E (2002) Inspired by immunity. Nature 415:648–670

    Article  Google Scholar 

  3. Balthrop J, Forrest S, Newman MEJ, et al. (2004) Technological networks and the spread of computer viruses. Science 304:527–529

    Article  Google Scholar 

  4. Jin Z-Z, Liao M-H, Xiao G (2013) Survey of negative selection algorithms. J Chin Inst Commun 34.1:159–170

    Google Scholar 

  5. Forrest S., Perelson A.S., Allen L, et al. (1994) Self-nonself discrimination in a computer[C]. In: Proceedings of the 1994 IEEE symposium on security and privacy, IEEE computer society

  6. González F.A, Dasgupta D (2003) Anomaly detection using real-valued negative selection [J]. Genetic Programming and Evolvable Machine 4(4):383–403

    Article  Google Scholar 

  7. Zhou J, Dasgupta D (2004) Real-valued negative selection algorithm with detectors[C]. In: Proceedings genetic and evolutionary computation conference (GECCO), pp 287–298

  8. Ji Z, Dasgupta D (2009) V-detector: An efficient negative selection algorithm with probably adequate detector coverage[J]. Inf Sci 179(10):1390–1406

    Article  Google Scholar 

  9. Sun Z, Xu Y, Liang G, et al. (2018) An intrusion detection model for wireless sensor networks with an improved V-detector algorithm[J]. IEEE Sensors J 18(5):1971–1984

    Article  Google Scholar 

  10. Xinping XU, Wang R, Jiang L, et al. (2018) Research on fault diagnosis of rotor based on improved V-detector algorithm[J]. DEStech Transactions on Engineering and Technology Research

  11. Yang T, Chen W, Li T (2017) An antigen space density based real-value negative selection algorithm[J]. Appl Soft Comput 61:860–874

    Article  Google Scholar 

  12. Yang C, Jia L, Chen BQ, et al. (2020) Negative selection algorithm based on antigen density clustering[J]. IEEE Access 8:44967–44975

    Article  Google Scholar 

  13. Hofmeyr S, Forrest S. (2000) Architecture for an artificial immune system[J]. Evol Comput 8(4):443–473

    Article  Google Scholar 

  14. Bhuvaneswari G, Manikandan G (2019) An intelligent intrusion detection system for secure wireless communication using IPSO and negative selection classifier[J]. Clust Comput 22(5):12429–12441

    Article  Google Scholar 

  15. Clotet X, Moyano J, León G (2018) A real-time anomaly-based IDS for cyber-attack detection at the industrial process level of critical infrastructures[J]. Int J Crit Infrastruct Prot 23:11–20

    Article  Google Scholar 

  16. Aissa NB, Guerroumi M, Derhab A (2020) NSNAD: negative selection-based network anomaly detection approach with relevant feature subset[J]. Neural Comput Appl 32:3475–3501

    Article  Google Scholar 

  17. Li D, Liu S, Zhang H (2016) A boundary-fixed negative selection algorithm with online adaptive learning under small samples for anomaly detection[J]. Eng Appl Artif Intell 50:93–105

    Article  Google Scholar 

  18. Saurabh P, Verma B (2016) An efficient proactive artificial immune system based anomaly detection and prevention system[J]. Exp Syst Applic 60:311–320

    Article  Google Scholar 

  19. Lu T, Zhang L, Fu Y (2018) A novel Immune-Inspired shellcode detection algorithm based on hyperellipsoid detectors[J]. Secur Commun Netw 2018:1–10

    Article  Google Scholar 

  20. Outa R, et al. (2020) Prognosis and fail detection in a dynamic rotor using artificial immunological system. Engineering Computations

  21. Abid A, Khan MT, De Silva CW, et al. (2018) Layered and real-valued negative selection algorithm for fault detection[J]. IEEE Syst J 12(3):2960–2969

    Article  Google Scholar 

  22. Dong LI, Liu S, Zhang H (2017) A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples[J]. Pattern Recogn 64:374–385

    Article  Google Scholar 

  23. Barontini A, Perera R, Masciotta MG, et al. (2019) Deterministically generated negative selection algorithm for damage detection in civil engineering systems[J]. Eng Struct 197:109444

    Article  Google Scholar 

  24. Ji Z, Dasgupta D (2007) Revisiting negative selection algorithms[J]. Evol Comput 15(2):223–251

    Article  Google Scholar 

  25. Idris I, Selamat A, Omatu S (2014) Hybrid email spam detection model with negative selection algorithm and differential evolution. Appl Artif Intell 28:97–110

    Article  Google Scholar 

  26. Cui L, Pi D, Chen C. (2015) BIORV-NSA bidirectional inhibition optimization r-variable negative selection algorithm and its application[J]. Appl Soft Comput 32:544–552

    Article  Google Scholar 

  27. Fan Z, Wen C, Tao L, et al. (2019) An antigen space triangulation coverage based real-value negative selection algorithm[J]. IEEE Access 7:51886–51898

    Article  Google Scholar 

  28. Hsu CW, Chang CC, Lin CJ (2016) A practical guide to support vector classification[J]

  29. Liu R, Yang B, Zio E, et al. (2018) Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mech Syst Signal Process 108:33–47

    Article  Google Scholar 

  30. Dasgupta D. (2006) Advances in artificial immune systems[J]. IEEE Comput Intell Mag 1 (4):40–49

    Article  Google Scholar 

  31. Evans MJ, Rosenthal JS (2010) Probability and statistics: the science of uncertainty (Second Edition). W. H. Freeman and company. ISBN-10: 1429224622

  32. Wen C, Tao L (2017) Parameter analysis of negative selection algorithm[J]. Inf Sci 420:218–234

    Article  Google Scholar 

  33. Exponential function, Last accessed: 2020-08-01. [Online]. Available:https://en.wikipedia.org/wiki/Exponential_function

  34. Lim T-S (1999) Haberman’s survival data set, UCI machine learning repository. Last accessed: 2021-01-10. [Online]. Available:https://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival

  35. Christensen R (2015) Thoughts on prediction and cross-validation[J]. Department of Mathematics and Statistics University of New Mexico

  36. Chang C-C, Lin C-J (2019) LIBSVM–a library for support vector machines. Version 3.24 released on September 11, 2019. Last accessed:2020-12-01. Available:https://www.csie.ntu.edu.tw/cjlin/libsvm/

  37. Li D, Liu S, Gao F, et al. (2020) Continual learning classification method with new labeled data based on the artificial immune system[J]. Appl Soft Comput 106423:94

    Google Scholar 

  38. Tao X, Li Q, Ren C, et al. (2019) Real-value negative selection over-sampling for imbalanced data set learning[J]. Expert Syst Appl 129:118–134

    Article  Google Scholar 

  39. Fisher RA (1988) Iris data set, UCI machine learning repository. Last accessed: 2021-01-10. [Online]. Available:http://archive.ics.uci.edu/ml/datasets/Iris

  40. Volker L (2013) Banknote authentication data set, UCI machine learning repository. Last accessed: 2021-01-10. [Online]. Available:https://archive.ics.uci.edu/ml/datasets/banknote+authenticationhttps://archive.ics.uci.edu/ml/datasets/banknote+authentication

  41. Bhatt R, Dhall A (2012) Skin segmentation data set, UCI machine learning repository. Last accessed: 2021-01-10. [Online]. Available:https://archive.ics.uci.edu/ml/datasets/Skin+Segmentation

  42. Turney P (1990) Pima Indians diabetes data set, [Online]. Last Accessed: 2021-01-10. Available: http://networkrepository.com/pima-indians-diabetes.php

  43. Siegler RS (1994) Balance scale data set, UCI machine learning repository, [Online]. Last Accessed: 2021-01-10. Available:http://archive.ics.uci.edu/ml/datasets/Balance+Scale

  44. Wolberg WH, Street N, Mangasarian OL (1995) Breast cancer wisconsin (Diagnostic) data set, UCI machine learning repository. Last accessed: 2021-01-10. [Online]. Available:https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

Download references

Acknowledgements

This work has been supported by the National Key Research and Development Program of China (Grant No.2020YFB1805400), National Natural Science Foundation of China (Grant No. U19A2068, No.U1736212, No.62032002).

Author information

Authors and Affiliations

  1. School of Cyber Science and Engineering, Sichuan University, Chengdu, 610065, China

    Zhiyong Li & Tao Li

  2. Information Technology Center, Honghe University, Mengzi, 661199, China

    Zhiyong Li

Authors
  1. Zhiyong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Li.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Li, T. Using known nonself samples to improve negative selection algorithm. Appl Intell 52, 482–500 (2022). https://doi.org/10.1007/s10489-021-02323-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02323-4

Keywords

Search

Navigation