Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems

  • S. ElhagEmail author
  • A. Fernández
  • S. Alshomrani
  • F. Herrera
Part of the Studies in Computational Intelligence book series (SCI, volume 779)


The so-called Evolutionary Fuzzy Systems consists of the application of evolutionary algorithms in the design process of fuzzy systems. Thanks to this hybridization, excellent abilities are provided to fuzzy systems in different work scenarios of data mining, such as standard classification, regression problems and association rule mining. The main reason of their success is the adaptation of their inner characteristics to any context. Among different areas of application, Evolutionary Fuzzy Systems have recently excelled in the area of Intrusion Detection Systems, yielding both accurate and interpretable models. To fully understand the nature and goodness of these type of models, we will introduce a full taxonomy on Evolutionary Fuzzy Systems. Then, we will overview a number of proposals from this research area that have been developed to address Intrusion Detection Systems. Finally, we will present a case study highlighting the good behaviour of Evolutionary Fuzzy Systems in this particular context.


Computational intelligence Evolutionary fuzzy systems Intrusion detection systems Multi-objective evolutionary fuzzy systems Fuzzy rule based systems 


  1. 1.
    Abadeh, M.S., Mohamadi, H., Habibi, J.: Design and analysis of genetic fuzzy systems for intrusion detection in computer networks. Expert Syst. Appl. 38(6), 7067–7075 (2011)CrossRefGoogle Scholar
  2. 2.
    Abadeh, M.S., Habibi, J., Lucas, C.: Intrusion detection using a fuzzy genetics-based learning algorithm. J. Netw. Comput. Appl. 30(1), 414–428 (2007)CrossRefGoogle Scholar
  3. 3.
    Aburomman, A., Reaz, M.: A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput. Secur. 65, 135–152 (2017)CrossRefGoogle Scholar
  4. 4.
    Alcala-Fdez, J., Alcala, R., Gonzalez, S., Nojima, Y., Garcia, S.: Evolutionary fuzzy rule-based methods for monotonic classification. IEEE Trans. Fuzzy Syst. 25(6), 1376–1390 (2017)CrossRefGoogle Scholar
  5. 5.
    Alcala-Fdez, J., Alcala, R., Herrera, F.: A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans. Fuzzy Syst. 19(5), 857–872 (2011)CrossRefGoogle Scholar
  6. 6.
    Alcala-Fdez, J., Herrera, F., Marquez, F.A., Peregrin, A.: Increasing fuzzy rules cooperation based on evolutionary adaptive inference systems. International Journal of Intelligent Systems 22(9), 1035–1064 (2007)CrossRefGoogle Scholar
  7. 7.
    Alshomrani, S., Bawakid, A., Shim, S.O., Fernandez, A., Herrera, F.: A proposal for evolutionary fuzzy systems using feature weighting: dealing with overlapping in imbalanced datasets. Knowl. -Based Syst. 73, 1–17 (2015)CrossRefGoogle Scholar
  8. 8.
    Ashfaq, R., Wang, X.Z., Huang, J., Abbas, H., He, Y.L.: Fuzziness based semi-supervised learning approach for intrusion detection system. Inf. Sci. 378, 484–497 (2017)CrossRefGoogle Scholar
  9. 9.
    Benferhat, S., Boudjelida, A., Tabia, K., Drias, H.: An intrusion detection and alert correlation approach based on revising probabilistic classifiers using expert knowledge. Appl. Intell. 38(4), 520–540 (2013)CrossRefGoogle Scholar
  10. 10.
    Cardoso, J.S., Sousa, R.: Measuring the performance of ordinal classification. Int. J. Pattern Recogn. Artif. Intell. 25(8), 1173–1195 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Casillas, J., Cordon, O., del Jesus, M.J., Herrera, F.: Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans. Fuzzy Syst. 13(1), 13–29 (2005)CrossRefGoogle Scholar
  12. 12.
    Castillo, O., Melin, P.: Optimization of type-2 fuzzy systems based on bio-inspired methods: a concise review. Inf. Sci. 205, 1–19 (2012)CrossRefGoogle Scholar
  13. 13.
    Castillo, O., Melin, P., Garza, A.A., Montiel, O., Sepulveda, R.: Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms. Soft Comput. 15(6), 1145–1160 (2011)CrossRefGoogle Scholar
  14. 14.
    Chebrolu, S., Abraham, A., Thomas, J.P.: Feature deduction and ensemble design of intrusion detection systems. Comput. Secur. 24(4), 295–307 (2005)CrossRefGoogle Scholar
  15. 15.
    Chung, Y.Y., Wahid, N.: A hybrid network intrusion detection system using simplified swarm optimization (SSO). Appl. Soft Comput. 12(9), 3014–3022 (2012)CrossRefGoogle Scholar
  16. 16.
    Coello-Coello, C.A., Lamont, G., van Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-objective Problems, Genetic and Evolutionary Computation, 2nd edn. Springer, Berlin, Heidelberg (2007)zbMATHGoogle Scholar
  17. 17.
    Cordon, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst. 141, 5–31 (2004)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Cordon, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic fuzzy systems. In: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. World Scientific, Singapore, Republic of Singapore (2001)Google Scholar
  19. 19.
    Cordon, O., Herrera, F., Villar, P.: Generating the knowledge base of a fuzzy rule-based system by the genetic learning of data base. IEEE Trans. Fuzzy Syst. 9(4), 667–674 (2001)CrossRefGoogle Scholar
  20. 20.
    Cordon, O.: A historical review of evolutionary learning methods for mamdani-type fuzzy rule-based systems: designing interpretable genetic fuzzy systems. Int. J. Approx. Reasoning 52(6), 894–913 (2011)CrossRefGoogle Scholar
  21. 21.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester, New York (2001)zbMATHGoogle Scholar
  22. 22.
    Dickerson, J., Dickerson, J.: Fuzzy network profiling for intrusion detection. In: Proceedings of the 19th International Conference of the North American Fuzzy Information Society (NAFIPS’00). pp. 301–306. IEEE Press, Atlanta, GA, USA (2000)Google Scholar
  23. 23.
    Dickerson, J., Juslin, J., Koukousoula, O., Dickerson, J.: Fuzzy intrusion detection. In: Proceedings of the 20th International Conference of the North American Fuzzy Information Society (NAFIPS’01) and Joint the 9th IFSA World Congress. vol. 3, pp. 1506–1510. IEEE Press, Vancouver, Canada (2001)Google Scholar
  24. 24.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computation. Springer, Berlin, Germany (2003)CrossRefGoogle Scholar
  25. 25.
    Elhag, S., Fernández, A., Altalhi, A., Alshomrani, S., Herrera, F.: On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Soft Comput. 1–16 (2018) (in press)Google Scholar
  26. 26.
    Elhag, S., Fernández, A., Bawakid, A., Alshomrani, S., Herrera, F.: On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst. Appl. 42(1), 193–202 (2015)CrossRefGoogle Scholar
  27. 27.
    Elkano, M., Galar, M., Sanz, J.A., Fernandez, A., Tartas, E.B., Herrera, F., Bustince, H.: Enhancing multiclass classification in farc-hd fuzzy classifier: on the synergy between \(n\)-dimensional overlap functions and decomposition strategies. IEEE Trans. Fuzzy Syst. 23(5), 1562–1580 (2015)CrossRefGoogle Scholar
  28. 28.
    Fazzolari, M., Alcala, R., Nojima, Y., Ishibuchi, H., Herrera, F.: A review of the application of multi-objective evolutionary systems: current status and further directions. IEEE Trans. Fuzzy Syst. 21(1), 45–65 (2013)CrossRefGoogle Scholar
  29. 29.
    Fernandez, A., Almansa, E., Herrera, F.: Chi-Spark-RS: an spark-built evolutionary fuzzy rule selection algorithm in imbalanced classification for big data problems (2017)Google Scholar
  30. 30.
    Fernandez, A., Carmona, C., del Jesus, M., Herrera, F.: A view on fuzzy systems for big data: progress and opportunities. Int. J. Comput. Intell. Syst. 9(1), 69–80 (2016)CrossRefGoogle Scholar
  31. 31.
    Fernández, A., Río, S., López, V., Bawakid, A., del Jesus, M.J., Benítez, J., Herrera, F.: Big data with cloud computing: an insight on the computing environment, MapReduce and programming framework. WIREs Data Mining Knowl. Discov. 4(5), 380–409 (2014)CrossRefGoogle Scholar
  32. 32.
    Fernandez, A., Altalhi, A., Alshomrani, S., Herrera, F.: Why linguistic fuzzy rule based classification systems perform well in big data applications? Int. J. Comput. Intell. Syst. 10, 1211–1225 (2017)CrossRefGoogle Scholar
  33. 33.
    Fernandez, A., Lopez, V., del Jesus, M.J., Herrera, F.: Revisiting evolutionary fuzzy systems: taxonomy, applications, new trends and challenges. Knowl. Based Syst. 80, 109–121 (2015)CrossRefGoogle Scholar
  34. 34.
    Fernandez, A., del Rio, S., Lopez, V., Bawakid, A., del Jesus, M.J., Benitez, J.M., Herrera, F.: Big data with cloud computing: an insight on the computing environment, MapReduce and programming frameworks. Wiley Interdisc. Rev.: Data Mining Knowl. Discov. 4(5), 380–409 (2014)Google Scholar
  35. 35.
    Fernandez, A., Calderon, M., Barrenechea, E., Bustince, H., Herrera, F.: Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations. Fuzzy Sets Syst. 161(23), 3064–3080 (2010)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Ferranti, A., Marcelloni, F., Segatori, A., Antonelli, M., Ducange, P.: A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data. Inf. Sci. 415–416, 319–340 (2017)CrossRefGoogle Scholar
  37. 37.
    Florez, G., Bridges, S., Vaughn, R.: An improved algorithm for fuzzy data mining for intrusion detection. In: Proceedings of the 21st North American Fuzzy Information Processing Society Conference (NAFIPS’02). pp. 457–462. New Orleans, LA (2002)Google Scholar
  38. 38.
    Gacto, M.J., Alcala, R., Herrera, F.: Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems. Soft Comput. 13(5), 419–436 (2009)CrossRefGoogle Scholar
  39. 39.
    Gacto, M.J., Alcala, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340–4360 (2011)CrossRefGoogle Scholar
  40. 40.
    Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn. 44(8), 1761–1776 (2011)CrossRefGoogle Scholar
  41. 41.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Upper Saddle River, NJ, USA (1989)zbMATHGoogle Scholar
  42. 42.
    Gomez, J., Dasgupta, D.: Evolving fuzzy classifiers for intrusion detection. In: Proceedings of IEEE Workshop on Information Assurance. pp. 68–75. United States Military Academy, West Point, New York (2001)Google Scholar
  43. 43.
    Gorzalczany, M., Rudzinski, F.: Interpretable and accurate medical data classification–A multi-objective genetic-fuzzy optimization approach. Expert Syst. Appl. 71, 26–39 (2017)CrossRefGoogle Scholar
  44. 44.
    Greene, D.P., Smith, S.F.: Competition-based induction of decision models from examples. Mach. Learn. 13(2–3), 229–257 (1993)CrossRefGoogle Scholar
  45. 45.
    Herrera, F.: Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol. Intell. 1(1), 27–46 (2008)CrossRefGoogle Scholar
  46. 46.
    Herrera, F., Charte, F., Rivera, A.J., del Jesús, M.J.: Multilabel Classification-Problem Analysis. Springer, Metrics and Techniques (2016)Google Scholar
  47. 47.
    Herrera, F., Ventura, S., Bello, R., Cornelis, C., Zafra, A., Tarragó, D.S., Vluymans, S.: Multiple Instance Learning—Foundations and Algorithms. Springer (2016)Google Scholar
  48. 48.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, USA (1975)Google Scholar
  49. 49.
    Homaifar, A., McCormick, E.: Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans. Fuzzy Syst. 3(2), 129–139 (1995)CrossRefGoogle Scholar
  50. 50.
    Ishibuchi, H., Murata, T., Turksen, I.: Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst. 8(2), 135–150 (1997)CrossRefGoogle Scholar
  51. 51.
    Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Selection of fuzzy IF-THEN rules for classification problems using genetic algorithms. IEEE Trans. Fuzzy Syst. 3(3), 260–270 (1995)CrossRefGoogle Scholar
  52. 52.
    Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7(6), 643–658 (1999)CrossRefGoogle Scholar
  53. 53.
    Khor, K.C., Ting, C.Y., Phon-Amnuaisuk, S.: A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection. Appl. Intell. 36(2), 320–329 (2012)CrossRefGoogle Scholar
  54. 54.
    Kim, D., Choi, Y., Lee, S.Y.: An accurate cog defuzzifier design using lamarckian co-adaptation of learning and evolution. Fuzzy Sets Syst. 130(2), 207–225 (2002)MathSciNetCrossRefGoogle Scholar
  55. 55.
    Konar, A.: Computational intelligence: principles, techniques and applications. Springer, Berlin, Germany (2005)CrossRefGoogle Scholar
  56. 56.
    Kuok, C.M., Fu, A.W.C., Wong, M.H.: Mining fuzzy association rules in databases. SIGMOD Rec. 27(1), 41–46 (1998)CrossRefGoogle Scholar
  57. 57.
    Lee, W., Stolfo, S.: A framework for constructing features and models for intrusion detection systems. ACM Trans. Inf. Syst. Secur. 3(4), 227–261 (2000)CrossRefGoogle Scholar
  58. 58.
    Liao, T.: A procedure for the generation of interval type-2 membership functions from data. Appl. Soft Comput. J. 52, 925–936 (2017)CrossRefGoogle Scholar
  59. 59.
    Marquez, F., Peregrín, A., Herrera, F.: Cooperative evolutionary learning of linguistic fuzzy rules and parametric aggregation connectors for mamdani fuzzy systems. IEEE Trans. Fuzzy Syst. 15(6), 1162–1178 (2008)CrossRefGoogle Scholar
  60. 60.
    Mohammadi Shanghooshabad, A., Saniee Abadeh, M.: Sifter: an approach for robust fuzzy rule set discovery. Soft Comput. 20(8), 3303–3319 (2016)CrossRefGoogle Scholar
  61. 61.
    Muhuri, P., Ashraf, Z., Lohani, Q.: Multi-objective reliability-redundancy allocation problem with interval type-2 fuzzy uncertainty. IEEE Trans, Fuzzy Syst (2017)Google Scholar
  62. 62.
    Naik, N., Diao, R., Shen, Q.: Dynamic fuzzy rule interpolation and its application to intrusion detection. IEEE Trans, Fuzzy Syst (2017)Google Scholar
  63. 63.
    Özyer, T., Alhajj, R., Barker, K.: Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening. J. Netw. Comput. Appl. 30(1), 99–113 (2007)CrossRefGoogle Scholar
  64. 64.
    Patcha, A., Park, J.M.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)CrossRefGoogle Scholar
  65. 65.
    Pedrycz, W., Gomide, F.: Fuzzy Systems Engineering: Toward Human-Centric Computing, 1st edn. Wiley (2007)Google Scholar
  66. 66.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo-California, USA (1993)Google Scholar
  67. 67.
    Sambuc, R.: Function \(\Phi \)-flous, application a l’aide au diagnostic en Pathologie Thyroidienne. Ph.D. thesis, University of Marseille (1975)Google Scholar
  68. 68.
    Rey, M., Galende, M., Fuente, M., Sainz-Palmero, G.: Multi-objective based fuzzy rule based systems (FRBSS) for trade-off improvement in accuracy and interpretability: a rule relevance point of view. Knowl. -Based Syst. 127, 67–84 (2017)CrossRefGoogle Scholar
  69. 69.
    Sanz, J.A., Fernandez, A., Bustince, H., Herrera, F.: Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning. Inf. Sci. 180(19), 3674–3685 (2010)CrossRefGoogle Scholar
  70. 70.
    Sanz, J.A., Fernandez, A., Bustince, H., Herrera, F.: IVTURS: a linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection. IEEE Trans. Fuzzy Syst. 21(3), 399–411 (2013)CrossRefGoogle Scholar
  71. 71.
    Sanz, J., Fernandez, A., Bustince, H., Herrera, F.: A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position. Int. J. Approx. Reasoning 52(6), 751–766 (2011)CrossRefGoogle Scholar
  72. 72.
    Smith, S.: A learning system based on genetic algorithms. Ph.D. thesis, University of Pittsburgh, Pittsburgh, PA (1980)Google Scholar
  73. 73.
    Smith, S.: Flexible learning of problem solving heuristics through adaptive search. In: 8th International Joint Conference on Artificial Intelligence, pp. 422–425 (1983)Google Scholar
  74. 74.
    Tajbakhsh, A., Rahmati, M., Mirzaei, A.: Intrusion detection using fuzzy association rules. Appl. Soft Comput. 9(2), 462–469 (2009)CrossRefGoogle Scholar
  75. 75.
    Thrift, P.: Fuzzy logic synthesis with genetic algorithms. In: Proceedings of the 4th International Conference on Genetic Algorithms (ICGA’91), pp. 509–513 (1991)Google Scholar
  76. 76.
    Tsakiridis, N., Theocharis, J., Zalidis, G.: DECO3RUM: a differential evolution learning approach for generating compact mamdani fuzzy rule-based models. Expert Syst. Appl. 83, 257–272 (2017)CrossRefGoogle Scholar
  77. 77.
    Tsang, C.H., Kwong, S., Wang, H.: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recogn. 40(9), 2373–2391 (2007)CrossRefGoogle Scholar
  78. 78.
    Vasilomanolakis, E., Karuppayah, S., Muhlhauser, M., Fischer, M.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surv. 47(4), 55:1–55:33 (2015)CrossRefGoogle Scholar
  79. 79.
    Venturini, G.: SIA: a supervised inductive algorithm with genetic search for learning attributes based concepts. In: Brazdil, P. (ed.) Machine Learning ECML–93. LNAI, vol. 667, pp. 280–296. Springer (1993)CrossRefGoogle Scholar
  80. 80.
    Victorie, T.A., Sakthivel, M.: A local search guided differential evolution algorithm based fuzzy classifier for intrusion detection in computer networks. Int. J. Soft Comput. 6(5–6), 158–167 (2012)Google Scholar
  81. 81.
    Wang, H., Kwong, S., Jin, Y., Wei, W., Man, K.F.: Agent-based evolutionary approach for interpretable rule-based knowledge extraction. IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev. 35(2), 143–155 (2005)CrossRefGoogle Scholar
  82. 82.
    Wu, S.X., Banzhaf, W.: The use of computational intelligence in intrusion detection systems: a review. Appl. Soft Comput. 10(1), 1–35 (2010)CrossRefGoogle Scholar
  83. 83.
    Yager, R.R., Filev, D.P.: Essentials of fuzzy modeling and control. Wiley (1994)Google Scholar
  84. 84.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  85. 85.
    Zarpelao, B., Miani, R., Kawakani, C., de Alvarenga, S.: A survey of intrusion detection in internet of things. J. Netw. Comput. Appl. 84, 25–37 (2017)CrossRefGoogle Scholar
  86. 86.
    Zhu, D., Premkumar, G., Zhang, X., Chu, C.H.: Data mining for network intrusion detection: a comparison of alternative methods. Decis. Sci. 32(4), 635–660 (2001)CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • S. Elhag
    • 1
    Email author
  • A. Fernández
    • 2
  • S. Alshomrani
    • 3
  • F. Herrera
    • 2
  1. 1.Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  3. 3.Faculty of Computing and Information TechnologyUniversity of JeddahJeddahSaudi Arabia

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