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Fuzzy min–max neural network and particle swarm optimization based intrusion detection system

Abstract

To maintain the integrity, availability, reliability of the data and services available on web requires a strong network security framework, in such consequence IDS based on data mining are the best solution. In this paper we proposed an intrusion detection system which is based on the fuzzy min max neural network and the particle swarm optimization. The proposed system is tested with the help of preprocessed KDD CUP data set. Classification accuracy and classification error are taken as a performance evaluation parameter to test the effectiveness of the system. The proposed system is compared with the some of the well-known methods, the results shows that the proposed system performed well as compared to the other systems.

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References

  1. Abadeh MS, Mohamadi H, Habibi J (2011) Design and analysis of genetic fuzzy systems for intrusion detection in computer networks. Expert Syst Appl 38(6):7067–7075

    Article  Google Scholar 

  2. Alcala-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

    Article  Google Scholar 

  3. Altwaijry H (2013) Bayesian based intrusion detection system. In: Kim HK et al (eds) IAENG transactions on engineering technologies, Lecture Notes in Electrical Engineering, vol 170. Springer, Netherlands. doi:10.1007/978-94-007-4786-9_3

  4. Anderson JP (1980) Computer security threat monitoring and surveillance. Technical report, James P. Anderson Company, Fort Washington, PA

  5. Anming Z (2012) An intrusion detection algorithm based on NFPA. Phys Proc 33:491–497

    Article  Google Scholar 

  6. Aydın MA, Zaim AH, Ceylan KG (2009) A hybrid intrusion detection system design for computer network security. Comput Electr Eng 35(3):517–526

    Article  MATH  Google Scholar 

  7. Azad C, Jha VK (2013) Data mining in intrusion detection: a comparative study of methods, types and data sets. Int J Inf Technol Comput Sci 5(8):75–90

    Google Scholar 

  8. Azad C, Jha VK (2014) Data mining based hybrid intrusion detection system. Indian J Sci Technol 7(6):781–789

    Google Scholar 

  9. Balajinath B, Raghavan SV (2001) Intrusion detection through learning behavior model. Comput Commun 24(12):1202–1212

    Article  Google Scholar 

  10. Barbara D, Jajodia S (2002) Applications of data mining in computer security. Springer Science & Business Media, Berlin, p 6

    Book  MATH  Google Scholar 

  11. Barbará D, Couto J, Jajodia S, Wu N (2001) ADAM: a testbed for exploring the use of data mining in intrusion detection. ACM Sigmod Record 30(4):15–24

    Article  Google Scholar 

  12. Bazan JG, Nguyen HS, Nguyen SH, Synak P, Wróblewski J (2000) Rough set algorithms in classification problem. In: Rough set methods and applications, Physica-Verlag HD, pp 49–88

  13. Boulaiche A, Bouzayani H, Adi K (2012) A quantitative approach for intrusions detection and prevention based on statistical n-gram models. In: Proceedings of the 3rd international conference on ambient systems, networks and technologies (ANT), procedia computer science, vol 10, pp 450–457

  14. Brauckhoff D, Dimitropoulos X, Wagner A, Salamatian K (2012) Anomaly extraction in backbone networks using association rules. IEEE/ACM Trans Netw 20(6):1788–1799

    Article  Google Scholar 

  15. Carvalho DR, Freitas AA (2004) A hybrid decision tree/genetic algorithm method for data mining. Inf Sci 163(1):13–35

    Article  Google Scholar 

  16. Casas P, Mazel J, Owezarski P (2012) Unsupervised network intrusion detection systems: detecting the unknown without knowledge. Comput Commun 35(7):772–783

    Article  Google Scholar 

  17. Chari SN, Cheng PC (2003) BlueBox: a policy-driven, host-based intrusion detection system. ACM Trans Inf Syst Secur 6(2):173–200

    Article  Google Scholar 

  18. Chirag Modi et al (2013) A survey of intrusion detection techniques in cloud. J Netw Comput Appl 36(1):42–57

    Article  Google Scholar 

  19. Denning DE (1987) An intrusion-detection model. IEEE Trans Softw Eng 13(2):222–232

    Article  Google Scholar 

  20. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  21. Innella P (2001) The evolution of intrusion detection systems. http://www.symantec.com/connect/articles/evolution-intrusion-detection-systems

  22. Intrusion Detection System (2015) http://en.wikipedia.org/wiki/Intrusion_detection_system

  23. Joo D, Hong T, Han I (2003) The neural network models for IDS based on the asymmetric costs of false negative errors and false positive errors. Expert Syst Appl 25(1):69–75

    Article  Google Scholar 

  24. KDD CUP (1999) Dataset. http://kdd.ics.uci.Edu/databases/kddcup99/kddcup99.html

  25. Kenkre PS, Pai A, Colaco L (2015) Real time intrusion detection and prevention system. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA). Springer International Publishing, pp 405–411

  26. Kennedy J (2010) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, USA, pp 760–766

  27. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. Piscataway, NJ, pp 1942–1948

  28. Lei JZ, Ghorbani AA (2012) Improved competitive learning neural networks for network intrusion and fraud detection. Neurocomputing 75(1):135–145

    Article  Google Scholar 

  29. Levin I (2000) KDD-99, classifier learning contest: LLSoft’s results overview. SIGKDD Explor 1(2):67–75

    Article  Google Scholar 

  30. Linda O, Vollmer T, Manic M (2009) Neural network based intrusion detection system for critical infrastructures. In: Neural networks, IJCNN 2009. International Joint Conference on 2009. IEEE. pp 1827–1834

  31. Lunt TF, Jagannathan R, Lee R, Listgarten S, Edwards DL, Neumann PG, Javitz HS, Valdes A (1988) Ides: the enhanced prototype-a real-time intrusion-detection expert system. In: SRI International, 333 Ravenswood Avenue, Menlo Park

  32. Onwubiko C (2012) Situational awareness in computer network defense: principles, methods and applications. IGI Global, Hershey, PA

    Book  Google Scholar 

  33. Panchev C, Dobrev P, Nicholson J (2014) Detecting port scans against mobile devices with neural networks and decision trees. In: Engineering applications of neural networks. Springer International Publishing, pp 175-182

  34. Pfahringer B (2000) Winning the KDD99 classification cup: bagged boosting. ACM SIGKDD Explor Newsl 1(2):65–66

    Article  Google Scholar 

  35. Sangeetha S et al (2015) Signature based semantic intrusion detection system on cloud. Information systems design and intelligent applications. Springer, India, pp 657–666

    Google Scholar 

  36. Sarasamma ST, Zhu Q, Huff J (2005) Hierarchical Kohonenen net for anomaly detection in network security. IEEE Trans Syst Man Cybern B Cybern 35(2):302–312

    Article  Google Scholar 

  37. Shun J, Malki H (2008) Network intrusion detection system using neural networks. In: Natural computation, 2008. ICNC’08. Fourth International Conference on 2008, vol 5, IEEE. pp 242–246

  38. Simpson PK (1992) Fuzzy min-max neural networks. I. Classification. IEEE Trans Neural Netw 5:776–786

    Article  Google Scholar 

  39. Sindhu SS, Geetha S, Kannan A (2012) Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst Appl 39(1):129–141

    Article  Google Scholar 

  40. Tong X, Wang Z, Yu H (2009) A research using hybrid RBF/Elman neural networks for intrusion detection system secure model. Comput Phys Commun 180(10):1795–1801

    Article  Google Scholar 

  41. Vasilomanolakis E, Karuppayah S, Mühlhäuser M, Fischer M (2015) Taxonomy and survey of collaborative intrusion detection. ACM Comput Surv (CSUR) 47(4):55

    Article  Google Scholar 

  42. Wang G, Hao J, Ma J, Huang L (2010) A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Syst Appl 37(9):6225–6232

    Article  Google Scholar 

  43. Wei M, Xia L, Jin J, Chen C (2014) Research of intrusion detection based on clustering analysis. In: Proceedings of the 2012 international conference on cybernetics and informatics. pp 1973–1979

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Correspondence to Chandrashekhar Azad.

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Azad, C., Jha, V.K. Fuzzy min–max neural network and particle swarm optimization based intrusion detection system. Microsyst Technol 23, 907–918 (2017). https://doi.org/10.1007/s00542-016-2873-8

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Keywords

  • Particle Swarm Optimization
  • Membership Function
  • Classification Accuracy
  • Intrusion Detection
  • Input Pattern