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A Survey and Taxonomy of Classifiers of Intrusion Detection Systems

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Computer and Network Security Essentials

Abstract

In this chapter, a new review and taxonomy of the classifiers that have been used with intrusion detection systems in the last two decades is presented. The main objective of this chapter is to provide the reader with the knowledge required to build an effective classifier for IDSs problems by reviewing this phase in component-by-component structure rather than paper-by-paper organization. We start by presenting the extracted features that resulted from the pre-processing phase. These features are supposed to be supplied to the pattern analyzer, and therefore different types of analyzers are presented. We discuss also the knowledge representation that is produced from these pattern analyzers. In addition, the decision making component (of IDS) which we called here detection phase is also presented in details with the most common algorithms used with IDS. The chapter explores the classifier decision types and the possible threats with their subclasses. The chapter also discusses the current open issues that face pattern analyzers that work in adversarial environments like intrusion detection systems and some contributions in this field. The components discussed in this chapter represent the core of the framework of any IDS.

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References

  1. Bergadano, F., Gunetti, D., & Picardi, C. (2003). Identity verification through dynamic keystroke analysis. Intelligence Data Analaysis, 7(5), 469–496.

    Google Scholar 

  2. Bhuse, V., & Gupta, A. (2006). Anomaly intrusion detection in wireless sensor networks. Journal of High Speed Networks, 15(1), 33–51.

    Google Scholar 

  3. Biggio, B., Fumera, G., & Roli, F. (2010). Multiple classifier systems for robust classifier design in adversarial environments. International Journal of Machine Learning and Cybernetics, 1(1), 27–41. doi:10.1007/s13042-010-0007-7

  4. Biggio, B., Fumera, G., & Roli, F. (2011). Design of robust classifiers for adversarial environments. In IEEE international conference on systems, man, and cybernetics (SMC) (pp. 977–982). IEEE.

    Google Scholar 

  5. Biggio, B., Fumera, G., & Roli, F. (2014). Security evaluation of pattern classifiers under attack. IEEE Transactions on Knowledge and Data Engineering, 26(4), 984–996. doi:10.1109/TKDE.2013.57

  6. Cho, S. B., & Park, H. J. (2003). Efficient anomaly detection by modeling privilege flows using hidden markov model. Computers & Security, 22(1), 45–55. doi:10.1016/S0167-4048(03)00112-3

  7. Dastjerdi, A. V., & Bakar, K. A. (2008). A novel hybrid mobile agent based distributed intrusion detection system. Proceedings of World Academy of Science, Engineering and Technology, 35, 116–119.

    Google Scholar 

  8. Gandhi, G. M., Appavoo, K., & Srivatsa, S. (2010). Effective network intrusion detection using classifiers decision trees and decision rules. International Journal of Advanced Networking and Applications, 2(3), 686–692.

    Google Scholar 

  9. Gong, Y., Mabu, S., Chen, C., Wang, Y., & Hirasawa, K. (2009). Intrusion detection system combining misuse detection and anomaly detection using genetic network programming. In ICCAS-SICE, 2009, (pp. 3463–3467).

    Google Scholar 

  10. Haidar, G. A., & Boustany, C. (2015). High perception intrusion detection system using neural networks. In 2015 ninth international conference on complex, intelligent, and software intensive systems (pp. 497–501). doi:10.1109/CISIS.2015.73

  11. Jalil, K. A., Kamarudin, M. H., & Masrek, M. N. (2010) Comparison of machine learning algorithms performance in detecting network intrusion. In 2010 international conference on networking and information technology (pp. 221–226). doi:10.1109/ICNIT.2010.5508526

  12. Kumar, M., Hanumanthappa, M., & Kumar, T. V. S. (2012). Intrusion detection system using decision tree algorithm. In 2012 IEEE 14th international conference on communication technology (pp. 629–634). doi:10.1109/ICCT.2012.6511281

  13. Lan, F., Chunlei, W., & Guoqing, M. (2010). A framework for network security situation awareness based on knowledge discovery. In 2010 2nd international conference on computer engineering and technology (Vol. 1, pp. V1–226–V1–231). doi:10.1109/ICCET.2010.5486194.

    Google Scholar 

  14. Lane, T. (2006). A decision-theoritic, semi-supervised model for intrusion detection. In Machine learning and data mining for computer security (pp. 157–177). London: Springer.

    Chapter  Google Scholar 

  15. Lane, T., & Brodley, C. E. (1997). An application of machine learning to anomaly detection. In Proceedings of the 20th national information systems security conference (Vol. 377, pp. 366–380).

    Google Scholar 

  16. Lin, W. C., Ke, S. W., & Tsai, C. F. (2015). Cann: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-Based Systems, 78, 13–21. doi:10.1016/j.knosys.2015.01.009

  17. Lin, Y., Zhang, Y., & Ou, Y-J (2010). The design and implementation of host-based intrusion detection system. In 2010 third international symposium on intelligent information technology and security informatics (pp. 595–598). doi:10.1109/IITSI.2010.127

  18. Maiwald, E. (2001). Network security: A beginner’s guide. New York, NY: New York Osborne/McGraw-Hill. http://openlibary.org./books/OL3967503M

    Google Scholar 

  19. Mantur, B., Desai, A., & Nagegowda, K. S. (2015). Centralized control signature-based firewall and statistical-based network intrusion detection system (NIDS) in software defined networks (SDN) (pp. 497–506). New Delhi: Springer. doi:10.1007/978-81-322-2550-8_48

  20. Mitchell, R., & Chen, I. R. (2015). Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems. IEEE Transactions on Dependable and Secure Computing, 12(1), 16–30. doi:10.1109/TDSC.2014.2312327

  21. Mo, Y., Ma, Y., & Xu, L. (2008). Design and implementation of intrusion detection based on mobile agents. In 2008 IEEE international symposium on IT in medicine and education (pp. 278–281). doi:10.1109/ITME.2008.4743870

  22. Mukkamala, S., Janoski, G., & Sung, A. (2002). Intrusion detection: Support vector machines and neural networks. IEEE International Joint Conference on Neural Networks (ANNIE), 2, 1702–1707.

    MATH  Google Scholar 

  23. Muntean, C., Dojen, R., & Coffey, T. (2009). Establishing and preventing a new replay attackon a non-repudiation protocol. In IEEE 5th international conference on intelligent computer communication and processing, ICCP 2009 (pp. 283–290). IEEE.

    Google Scholar 

  24. Newsome, J., Karp, B., & Song D. (2005). Polygraph: Automatically generating signatures for polymorphic worms. In 2005 IEEE symposium on security and privacy (S&P’05) (pp. 226–241). IEEE.

    Google Scholar 

  25. Pannell, G., & Ashman, H. (2010). Anomaly detection over user profiles for intrusion detection. In Proceedings of the 8th Australian information security management conference (pp. 81–94). Perth, Western Australia: School of Computer and Information Science, Edith Cowan University.

    Google Scholar 

  26. Pfleeger, C. P., & Pfleeger, S. L. (2006). Security in computing (4th ed.). Upper Saddle River, NJ: Prentice Hall PTR.

    MATH  Google Scholar 

  27. Rieck, K., Schwenk, G., Limmer, T., Holz, T., & Laskov, P. (2010). Botzilla: Detecting the phoning home of malicious software. In Proceedings of the 2010 ACM symposium on applied computing (pp. 1978–1984). ACM.

    Google Scholar 

  28. Di Pietro, R., & Mancini, L. V. (2008). Intrusion detection systems (Vol. 38). New York, NY: Springer Science & Business Media.

    Google Scholar 

  29. Sadeghi, Z., & Bahrami, A. S. (2013). Improving the speed of the network intrusion detection. In The 5th conference on information and knowledge technology (pp. 88–91). doi:10.1109/IKT.2013.6620044

  30. Sarvari, H., & Keikha, M. M. (2010). Improving the accuracy of intrusion detection systems by using the combination of machine learning approaches. In 2010 international conference of soft computing and pattern recognition (pp. 334–337). doi:10.1109/SOCPAR.2010.5686163

  31. Schonlau, M., DuMouchel, W., Ju, W. H., Karr, A. F., Theus, M., & Vardi, Y. (2001). Computer intrusion: Detecting masquerades. Statistical Science, 16(1), 58–74.

    Article  MathSciNet  MATH  Google Scholar 

  32. Sekar, R., Gupta, A., Frullo, J., Shanbhag, T., Tiwari, A., Yang, H., & Zhou, S. (2002). Specification-based anomaly detection: A new approach for detecting network intrusions. In Proceedings of the 9th ACM conference on computer and communications security, CCS ‘02 (pp. 265–274). New York, NY: ACM. doi:10.1145/586110.586146

  33. Shanmugavadivu, R., & Nagarajan, N. (2011). Network intrusion detection system using fuzzy logic. Indian Journal of Computer Science and Engineering (IJCSE), 2(1), 101–111.

    Google Scholar 

  34. Sheng Gan, X., Shun Duanmu, J., Fu Wang, J., & Cong, W. (2013). Anomaly intrusion detection based on {PLS} feature extraction and core vector machine. Knowledge-Based Systems, 40, 1–6. doi:10.1016/j.knosys.2012.09.004

  35. Shon, T., & Moon, J. (2007). A hybrid machine learning approach to network anomaly detection. Information Sciences, 177(18), 3799–3821. doi:10.1016/j.ins.2007.03.025

  36. Singh, S., & Silakari, S. (2009). A survey of cyber attack detection systems. IJCSNS International Journal of Computer Science and Network Security, 9(5), 1–10.

    Google Scholar 

  37. Terry, S., & Chow, B. J. (2005). An assessment of the DARPA IDS evaluation dataset using snort (Technical report, UC Davis Technical Report).

    Google Scholar 

  38. Trinius, P., Willems, C., Rieck, K., & Holz, T. (2009). A malware instruction set for behavior-based analysis (Technical Report TR-2009-07). University of Mannheim.

    Google Scholar 

  39. Vasudevan, A., Harshini, E., & Selvakumar, S. (2011). Ssenet-2011: a network intrusion detection system dataset and its comparison with kdd cup 99 dataset. In 2011 second asian himalayas international conference on internet (AH-ICI) (pp. 1–5). IEEE.

    Google Scholar 

  40. Wang, W., Guyet, T., Quiniou, R., Cordier, M. O., Masseglia, F., & Zhang, X. (2014). Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks. Knowledge-Based Systems, 70, 103–117. doi:10.1016/j.knosys.2014.06.018

  41. Wang, Y., Lin, C., Li, Q. L., & Fang, Y. (2007). A queueing analysis for the denial of service (dos) attacks in computer networks. Computer Networks, 51(12), 3564–3573.

    Article  MATH  Google Scholar 

  42. Xiaoqing, G., Hebin, G., & Luyi, C. (2010). Network intrusion detection method based on agent and svm. In 2010 2nd IEEE international conference on information management and engineering (pp. 399–402). doi:10.1109/ICIME.2010.5477694

  43. Xu, J., & Wu, S. (2010). Intrusion detection model of mobile agent based on aglets. In 2010 international conference on computer application and system modeling (ICCASM 2010) (Vol. 4, pp. V4-347–V4-350). doi:10.1109/ICCASM.2010.5620189

  44. Xue-qin, Z., Chun-hua, G., & Jia-jun, L. (2006). Intrusion detection system based on feature selection and support vector machine. In 2006 first international conference on communications and networking in China (pp. 1–5). doi:10.1109/CHINACOM.2006.344739

  45. Yang, W., Wan, W., Guo, L., & Zhang, L. J. (2007). An efficient intrusion detection model based on fast inductive learning. In 2007 international conference on machine learning and cybernetics, (Vol. 6, pp. 3249–3254). doi:10.1109/ICMLC.2007.4370708

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Authors and Affiliations

  1. School of Computer Science, University of Guelph, Guelph, ON, Canada

    Tarfa Hamed & Stefan C. Kremer

  2. Left Inc., Vancouver, BC, Canada

    Jason B. Ernst

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  1. Tarfa Hamed
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  2. Jason B. Ernst
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  3. Stefan C. Kremer
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Correspondence to Tarfa Hamed .

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  1. University of Detroit Mercy, Detroit, Michigan, USA

    Kevin Daimi

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Hamed, T., Ernst, J.B., Kremer, S.C. (2018). A Survey and Taxonomy of Classifiers of Intrusion Detection Systems. In: Daimi, K. (eds) Computer and Network Security Essentials. Springer, Cham. https://doi.org/10.1007/978-3-319-58424-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-58424-9_2

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