Advertisement

Fuzzy Min-Max Neural Network-Based Intrusion Detection System

  • Azad Chandrashekhar
  • Jha Vijay Kumar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 403)

Abstract

In this chapter, a novel intrusion detection system has been proposed which is based on the fuzzy min max neural network. The objective of the proposed intrusion detection system is to protect the end user system from the cope of various types of cyberattacks. The main hurdles in the today’s intrusion detections system are the nonlinear separablity, online adaption, preprocessing of the network logs, attribute selection, and the learning of the desired system for the anomalous or the signature detection. The proposed system is tested on the KDD Cup 99 dataset, and the classification accuracy and classification error are used for performance evaluation. The critical experiment on the proposed system gives the superior performance.

Keywords

Anomaly detection Misuse detection HIDS NIDS Fuzzy min-max neural network 

References

  1. 1.
    Rao HR (ed) (2007) Managing information assurance in financial services. IGI GlobalGoogle Scholar
  2. 2.
    Vokorokos L, Ennert M, Radušovský J (2014) A survey of parallel intrusion detection on graphical processors. Cent Eur J Comp Sci 4(4):222–230Google Scholar
  3. 3.
  4. 4.
    Azad C, Jha VK (2013) Data mining in intrusion detection: a comparative study of methods, types and data sets. Int J Info Technol Comput Sci (IJITCS) 5(8):75Google Scholar
  5. 5.
    Liao HJ, Lin CHR, Lin YC, Tung KY (2013) Intrusion detection system: a comprehensive review. J Network Comput Appl 36(1):16–24CrossRefGoogle Scholar
  6. 6.
    Yang H, Li T, Hu X, Wang F, Zou Y (2014) A survey of artificial immune system based intrusion detection. Sci World JGoogle Scholar
  7. 7.
    Elshoush HT, Osman IM (2011) Alert correlation in collaborative intelligent intrusion detection systems—a survey. Appl Soft Comput 11(7):4349–4365CrossRefGoogle Scholar
  8. 8.
    Chari SN, Cheng PC (2003) BlueBox: a policy-driven, host-based intrusion detection system. ACM Trans Info Syst Sec (TISSEC) 6(2):173–200CrossRefGoogle Scholar
  9. 9.
    Gautam SK, Om H (2015) Multivariate linear regression model for host based intrusion detection. In: Computational intelligence in data mining, vol 3. Springer, India, pp 361–371Google Scholar
  10. 10.
    Vigna G, Kemmerer RA (1999) NetSTAT: a network-based intrusion detection system. J Comput Sec 7(1):37–71CrossRefGoogle Scholar
  11. 11.
    Joshi SA, Pimprale VS (2013) Network intrusion detection system (NIDS) based on data mining. Int J En Sci Innovative Technol (IJESIT) 2Google Scholar
  12. 12.
    Ragsdale DJ, Carver CA, Humphries JW, Pooch UW (2000) Adaptation techniques for intrusion detection and intrusion response systems. In: IEEE international conference on systems, man, and cybernetics, vol. 4, pp 2344–2349Google Scholar
  13. 13.
  14. 14.
    Liao S-H, Chu P-H, Hsiao P-Y (2012) Data mining techniques and applications–a decade review from 2000 to 2011. Expert Syst Appl 39(12):11303–11311CrossRefGoogle Scholar
  15. 15.
    Julisch K (2002) Data mining for intrusion detection. Applications of data mining in computer security. Springer, US, pp 33–62CrossRefGoogle Scholar
  16. 16.
    Azad C, Jha VK (2014) Data mining based hybrid intrusion detection system. Indian J Sci Technol 7(6):781–789Google Scholar
  17. 17.
    Chen T, Zhang X, Jin S, Kim O (2014) Efficient classification using parallel and scalable compressed model and its application on intrusion detection. Expert Syst Appl 41(13):5972–5983CrossRefGoogle Scholar
  18. 18.
    Gu B, Guo H (2014) The intrusion detection system based on a novel association rule. In: International conference on information science, electronics and electrical engineering (ISEEE), vol. 2, pp 1313–1316Google Scholar
  19. 19.
    Tong Xiaojun, Wang Zhu, Haining Yu (2009) A research using hybrid RBF/Elman neural networks for intrusion detection system secure model. Comput Phys Commun 180(10):1795–1801CrossRefGoogle Scholar
  20. 20.
    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):6225–6232CrossRefGoogle Scholar
  21. 21.
    Lei JZ, Ghorbani AA (2012) Improved competitive learning neural networks for network intrusion and fraud detection. Neurocomputing 75(1):135–145CrossRefGoogle Scholar
  22. 22.
    Shun J, Malki HA (2008) Network intrusion detection system using neural networks. In: Fourth international conference on natural computation ICNC’08, vol. 5, pp 242–246Google Scholar
  23. 23.
    Sarasamma ST, Zhu QA, Huff J (2005) Hierarchical Kohonenen net for anomaly detection in network security. Syst Man Cybern Part B: Cybern IEEE Trans 35(2):302–312CrossRefGoogle Scholar
  24. 24.
    Linda O, Vollmer T, Manic M (2009) Neural network based intrusion detection system for critical infrastructures. In: International joint conference on neural networks, pp 1827–1834Google Scholar
  25. 25.
    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):69–75CrossRefGoogle Scholar
  26. 26.
    Zhou ZH, Jiang Y (2004) NeC4. 5: neural ensemble based C4. 5. IEEE Trans Knowl Data Eng 16(6):770–773CrossRefGoogle Scholar
  27. 27.
    Sindhu S, Siva S, Geetha S, Kannan A (2012) Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst Appl 39(1):129–141CrossRefzbMATHGoogle Scholar
  28. 28.
    Simpson PK (1992) Fuzzy min-max neural networks. I. classification. IEEE Trans Neural Networks 3(5):776–786CrossRefGoogle Scholar
  29. 29.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 11(1):10–18CrossRefGoogle Scholar
  30. 30.
    Pfahringer B (2000) Winning the KDD99 classification cup: bagged boosting. ACM SIGKDD Explorations Newsletter 1(2):65–66CrossRefGoogle Scholar
  31. 31.
    Levin I (2000) KDD-99 classifier learning contest: LLSoft’s results overview. SIGKDD Explorations 1(2):67–75CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of Technology MesraRanchiIndia

Personalised recommendations