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A new differential evolution algorithm for cooperative fuzzy rule mining: application to anomaly detection

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Abstract

Fuzzy classification rule mining can be considered as a challenging optimization problem with the purpose of extracting accurate and interpretable rules. This paper deals with a Michigan cooperative approach for mining fuzzy rules. The proposed algorithm named EDE-FRMiner uses an enhanced differential evolution that evolves population of individuals; each one represents a single rule. The whole population collaborates to generate in one shot an accurate and reduced number of rules. EDE-FRMiner is an intelligent process of evolution that uses fast arithmetic operators and a new cooperative weights memory. This latter allows sharing information between individuals. In addition, it uses a new threshold based fitness function using a redefined support and confidence measures. The adaptive threshold mechanism used in the fitness function aims to adapt the miner system to problems with dynamic training data. Experiments are carried out using the NSL-kdd’99 intrusion detection data set and other data sets from the UCI repository. A comparative study with other competitive evolutionary rule based systems is performed and the results show the effectiveness of proposed algorithm.

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References

  1. Aguilar-Ruiz JS, Giraldez R, Riquelme JC (2007) Natural encoding for evolutionary supervised learning. IEEE Trans Evol Comput 11(4):466–479

    Article  Google Scholar 

  2. Aguilar-Ruiz JS, Riquelme JC, Toro M (2003) Evolutionary learning of hierarchical decision rules. IEEE Trans Syst Man Cybern B 33(2):324–331

    Article  Google Scholar 

  3. Abadeh MS, Habibi J, Barzegar Z, Sergi M (2007a) A parallel genetic local search algorithm for intrusion detection in computer networks. Eng Appl Artif Intell 20(8):1058–1069

    Article  Google Scholar 

  4. Abadeh MS, Habibi J, Lucas C (2007b) Intrusion detection using a fuzzy genetics-based learning algorithm. J Netw Comput Appl 30(1):414–428

    Article  Google Scholar 

  5. Antonelli M, Ducange P, Marcelloni F (2014) A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Inf Sci 283:36–54

    Article  MathSciNet  MATH  Google Scholar 

  6. Aggarwal CC (2014) Data classification: algorithms and applications. Chapman & Hall/CRC

  7. Berlanga F, Rivera A, del Jesus M, Herrera F (2010) GP-COACH: Genetic Programming-based learning of Compact and Accurate fuzzy rule-based classification systems for High-dimensional problems. Inf Sci 180(8):1183–1200

    Article  Google Scholar 

  8. Bacardit J (2004) Pittsburgh genetic-based machine learning in the data mining era: representations, generalization and runtime. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain

  9. Boonyopakorn P (2019) The optimization and enhancement of network intrusion detection through fuzzy association rules. In: proceedings of the 6th international conference on technical education (ICTechEd6), Thailand, pp 1–5

  10. Chen T, Shen Q, Su P, Shang C (2014) Refinement of fuzzy rule weights with particle swarm optimization. In: Proceedings of the 14th UK workshop on computational intelligence, pp 1–7

  11. Elhag S, Fernández A, Altalhi A, Alshomrani S, Herrera F (2019) A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems. Soft Comput 23:1321–1336

    Article  Google Scholar 

  12. García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180:2044–2064

    Article  Google Scholar 

  13. García-Honrado I (2013) A beginner’s view on fuzzy logic. Springer, Berlin

    Book  Google Scholar 

  14. Gonzblez A, Perez R (1999) Slave: a genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 7(2):176–191

    Article  Google Scholar 

  15. Guendouzi W, Boukra A (2017) EDDE-LNS: a new hybrid ensemblist approach for feature selection. Int J Memetic Comput 10(1):63–79

    Article  Google Scholar 

  16. Guendouzi W, Boukra A (2017) GAB-BBO: adaptive biogeography based feature selection approach for intrusion detection. Int J Comput Intell Syste 10(1):914–935

    Article  Google Scholar 

  17. Hall M, Frank E, Holmes G, Pfahringer B, Reute-mann P, Witten I H (2009) The weka data mining software: An update.ACM SIGKDD Explorations Newsletter, 11, 10–18

  18. Khalili-Damghani K, Sadi-Nezhad S, Lotfi FH, Tavana M (2013) A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection. Inf Sci 220:442–462

    Article  Google Scholar 

  19. Kromer P, Platos J, Snásel V, Abraham A (2011) Fuzzy classification by evolutionary algorithms. In: Proceedings of IEEE international conference on systems, man, and cybernetics, pp 313–318

  20. Moayedikia A, Jensen R, Wiil UK, Forsati R (2015) Weighted bee colony algorithm for discrete optimization problems with application to feature selection. Eng Appl Artif Intell 44:153–167

    Article  Google Scholar 

  21. Nikolaos L, Tsakiridis John B, Theocharis George C, Zalidis, (2016) DECO3R: a differential evolution-based algorithm for generating compact fuzzy rule-based classification systems. Knowl-Based Syst 105:160–174

  22. Otero J, Sánchez L (2006) Induction of descriptive fuzzy classifiers with the logit boost algorithm. Soft Comput 10(9):825–835

    Article  Google Scholar 

  23. Orriols-Puig A, Casillas J, Bernadó-Mansilla E (2008) Genetic-based machine learning systems are competitive for pattern recognition. Evol Intel 1:209–232

    Article  Google Scholar 

  24. Parashar S, Senthilnath J, Yang XS (2017) A novel bat algorithm fuzzy classifier approach for classification problems. Int J Artif Intell Soft Comput 6(2):108–128

    Google Scholar 

  25. Patel KK (2020) A compact network intrusion classifier using fuzzy logic. In: Trends in Computational Intelligence, Security and Internet of Things. Communications in Computer and Information Science, (1358), Springer

  26. Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization (natural computing series). Springer-Verlag, New York

    MATH  Google Scholar 

  27. Rastegari S, Hingston P, Lam C-P (2015) Evolving statistical rule sets for network intrusion detection. Appl Soft Comput 33:348–359

    Article  Google Scholar 

  28. Sanz JA, Fernández A, Bustince H, Herrera F (2013) 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

    Article  Google Scholar 

  29. Sawyer S, Tapia A (2005) The sociotechnical nature of mobile computing work: Evidence from a study of policing in the United States. Int J Technol Human Interact 1(3):1–14

    Article  Google Scholar 

  30. Shanghooshabad AM, Abadeh MS (2016) Robust, interpretable and high quality fuzzy rule discovery using krill herd algorithm. J Intell Fuzzy Syst 30(3):1601–1612

    Article  Google Scholar 

  31. Singh S, Virmani D, Gao X (2020) A fuzzy logic-based method to avert intrusions in wireless sensor networks using WSN-DS dataset. Int J Comput Intell Appl 19(3)

  32. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDDCUP 99 data set. In: Proceedings of the second IEEE symposium on computational intelligence for security and defence (applications)

  33. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics, 1.80–83

  34. Wu J, Yang L, Li T, Zhang C, Li Z (2015) Rule-based fuzzy classifier based on quantum ant optimization algorithm. J Intell Fuzzy Syst 29(6):2365–2371

    Article  MATH  Google Scholar 

  35. Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  36. Zhang A, Shi W (2020) Mining significant fuzzy association rules with differential evolution algorithm. Appl Soft Comput 97(B)

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Authors and Affiliations

  1. University of Algiers Benyoucef Benkhedda, 02 Didouche Mourad, Algiers, Algeria

    Wassila Guendouzi

  2. University of Science and Technology Houari Boumediene, El Alia, Bab-Ezzouar, Algiers, Algeria

    Abdelmadjid Boukra

Authors
  1. Wassila Guendouzi
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  2. Abdelmadjid Boukra
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Correspondence to Wassila Guendouzi.

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Guendouzi, W., Boukra, A. A new differential evolution algorithm for cooperative fuzzy rule mining: application to anomaly detection. Evol. Intel. 15, 2667–2678 (2022). https://doi.org/10.1007/s12065-021-00637-3

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  • DOI: https://doi.org/10.1007/s12065-021-00637-3

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