Skip to main content

Analysis of Neural Network Training and Cost Functions Impact on the Accuracy of IDS and SIEM Systems

  • Conference paper
  • First Online:
Codes, Cryptology and Information Security (C2SI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11445))

Abstract

Nowadays, companies are implementing security tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management systems (SIEM) to deal with sophisticated computer attacks. These attacks evolve each year in terms of sophistication and complexity in order to steal or alter sensitive information. Machine learning techniques are used in order to provide pattern recognition and adaptation to IDS and SIEM systems. In this paper, we have proposed a model based on neural networks and support vector machines to analyze and identify network intrusions. We studied the impact of some important parameters in neural networks on the classification accuracy. We evaluated and compared 37 different feed-forward neural networks according to these parameters and choose the best training algorithm for our model using NSL-KDD dataset. Our results suggest that the choice of the appropriate performance function and training algorithm may be critical to achieve higher classification accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Verizonent: 2018 Data Breach Investigations Report (p. 8) (2018). https://www.verizonenterprise.com

  2. Mathews, L.: ThyssenKrupp Attackers Stole Trade Secrets In Massive Hack (2016). http://www.forbes.com/sites/leemathews/2016/12/08/thyssenkrupp-attackers-stole-trade-secrets-in-massive-hack/LeeMathews,Lee. Accessed 12 Oct 2016

  3. Schwartz, M.J.: Lockheed Martin Suffers Massive Cyberattack (2011). http://www.darkreading.com/risk-management/lockheed-martin-suffers-massive-cyberattack/d/d-id/1098013. Accessed 2 Mar 2017

  4. Markoff, J.: SecurID Company Suffers a Breach of Data Security (2011). http://www.nytimes.com/2011/03/18/technology/18secure.html. Accessed 2 Mar 2017

  5. Gogoi, P., Bhattacharyya, D.K., Borah, B., Kalita, J.K.: MLH-IDS: a multi-level hybrid intrusion detection method. Comput. J. 57(4), 602–623 (2013). https://doi.org/10.1093/comjnl/bxt044

    Article  Google Scholar 

  6. Orhanou, G., Lakbabi, A., Moukafih, N., El Hajji, S. (n.d.): Network access control and collaborative security against APT and AET. In: Security and Privacy in Smart Sensor Networks, pp. 201–230. IGI Global. https://doi.org/10.4018/978-1-5225-5736-4.ch010

  7. Hall, D.L., Llinas, J.: An introduction to multisensor data fusion. Proc. IEEE 85(1), 6–23 (1997). https://doi.org/10.1109/5.554205

    Article  Google Scholar 

  8. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, Boston (2005)

    Google Scholar 

  9. Zhang, C., Jiang, J., Kamel, M.: Intrusion detection using hierarchical neural networks. Pattern Recognit. Lett. 26(6), 779–791 (2005). https://doi.org/10.1016/j.patrec.2004.09.045

    Article  Google Scholar 

  10. Yamaguchi, F., Lindner, F., Rieck, K.: Vulnerability extrapolation: assisted discovery of vulnerabilities using machine learning. In: Proceedings of the 5th USENIX Conference on Offensive Technologies (2011)

    Google Scholar 

  11. Livshits, B., Zimmermann, T.: DynaMine. ACM SIGSOFT Softw. Eng. Notes 30(5), 296 (2005). https://doi.org/10.1145/1095430.1081754

    Article  Google Scholar 

  12. Rieck, K., Trinius, P., Willems, C., Holz, T.: Automatic analysis of malware behavior using machine learning. J. Comput. Secur. 19(4), 639–668 (2011)

    Article  Google Scholar 

  13. Kotler, J.Z., Maloof, M.A.: Learning to detect and classify malicious executables in the wild. J. Mach. Learn. Res. 7, 2721–2744 (2006)

    MathSciNet  MATH  Google Scholar 

  14. Anderson, J.P.: Computer security threat monitoring and surveillance, vol. 17. Technical report, James P. Anderson Company, Fort Washington, Pennsylvania (1980)

    Google Scholar 

  15. Chiba, Z., Abghour, N., Moussaid, K., El Omri, A., Rida, M.: A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Comput. Secur. 75, 36–58 (2018). https://doi.org/10.1016/j.cose.2018.01.023

    Article  Google Scholar 

  16. Sen, R., Chattopadhyay, M., Sen, N.: An efficient approach to develop an intrusion detection system based on multi layer backpropagation neural network algorithm. In: Proceedings of the 2015 ACM SIGMIS Conference on Computers and People Research - SIGMIS-CPR 2015. ACM Press (2015). https://doi.org/10.1145/2751957.2751979

  17. Kuang, F., Xu, W., Zhang, S., Wang, Y., Liu, K.: A novel approach of KPCA and SVM for intrusion detection. J. Comput. Inf. Syst. 8(8), 3237–3244 (2012)

    Google Scholar 

  18. Devaraju, S., Ramakrishnan, S.: Performance analysis of intrusion detection system using various neural network classifiers. In: 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE (2011). https://doi.org/10.1109/icrtit.2011.5972289

  19. Ussath, M., Jaeger, D., Cheng, F., Meinel, C.: Identifying suspicious user behavior with neural networks. In: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). IEEE (2017). https://doi.org/10.1109/cscloud.2017.10

  20. Suarez-Tangil, G., Palomar, E., Ribagorda, A., Sanz, I.: Providing SIEM systems with self-adaptation. Inf. Fusion 21, 145–158 (2015). https://doi.org/10.1016/j.inffus.2013.04.009

    Article  Google Scholar 

  21. Rayan, J., Meng-Jang, L., Risto, M.: Intrusion Detection with Neural Networks. AAAI Technical Report WS-97-07 (1997)

    Google Scholar 

  22. Sharma, B., Venugopalan, K.: Comparison of neural network training functions for hematoma classification in brain CT images. IOSR J. Comput. Eng. (IOSR-JCE) 16(1), 31–35 (2014)

    Article  Google Scholar 

  23. Hesam, K., Sharareh, R.N., Reza, S.: Comparison of neural network training algorithms for classification of heart diseases. IAES Int. J. Artif. Intell. (IJ-AI) 7(4), 185–189 (2018)

    Google Scholar 

  24. Kumari, V.V., Varma, P.R.K.: A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE (2017). https://doi.org/10.1109/i-smac.2017.8058397

  25. Al-Yaseen, W.L., Othman, Z.A., Nazri, M.Z.A.: Intrusion detection system based on modified K-means and multi-level support vector machines. In: Berry, M.W., Mohamed, A.H., Wah, Y.B. (eds.) SCDS 2015. CCIS, vol. 545, pp. 265–274. Springer, Singapore (2015). https://doi.org/10.1007/978-981-287-936-3_25

    Chapter  Google Scholar 

  26. Baceand, R., Mell, P.: NIST Special Publication on Intrusion Detection Systems (2011). www.dtic.mil/dtic/tr/fulltext/u2/a393326.pdf. Accessed Mar 10 2018

  27. Intrusion Detection and Correlation: Advances in Information Security. Kluwer Academic Publishers (2005). https://doi.org/10.1007/b101493

  28. Moukafih, N., Sabir, S., Lakbabi, A., Orhanou, G.: SIEM selection criteria for an efficient contextual security. In: 2017 International Symposium on Networks, Computers and Communications (ISNCC). IEEE (2017). https://doi.org/10.1109/isncc.2017.8072035

  29. Miller, D.: Security Information and Event Management (SIEM) Implementation. McGraw-Hill, New York (2011)

    Google Scholar 

  30. Russell, S., Norvig, P., Davis, E.: Artificial Intelligence: A Modern Approach. Prentice Hall, Upper Saddle River (2010)

    MATH  Google Scholar 

  31. Ali, S., Smith, K.A.: On learning algorithm selection for classification. Appl. Soft Comput. 6(2), 119–138 (2006). https://doi.org/10.1016/j.asoc.2004.12.002

    Article  Google Scholar 

  32. Sutton, R.S.: Two problems with backpropagation and other steepest-descent learning procedures for networks. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society. Erlbaum, Hillsdale, NJ (1986)

    Google Scholar 

  33. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks. IEEE (1993) https://doi.org/10.1109/icnn.1993.298623

  34. Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 (1994)

    Google Scholar 

  35. Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993). https://doi.org/10.1016/s0893-6080(05)80056-5

  36. Fletcher, R.: Function minimization by conjugate gradients. Comput. J. 7(2), 149–154 (1964). https://doi.org/10.1093/comjnl/7.2.1494

    Article  MathSciNet  MATH  Google Scholar 

  37. Pham, D.T., Sagiroglu, S.: Training multilayered perceptrons for pattern recognition: a comparative study of four training algorithms. Int. J. Mach. Tools Manuf. 41(3), 419–430 (2001). https://doi.org/10.1016/s0890-6955(00)00073-0

    Article  Google Scholar 

  38. KDD CUP 99 dataset. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed 23 Oct 2018

  39. NSL-KDD dataset available. https://github.com/defcom17/NSL_KDD. Accessed 23 Oct 2018

  40. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. IEEE (2009). https://doi.org/10.1109/cisda.2009.5356528

  41. Ji, H., Kim, D., Shin, D., Shin, D.: A study on comparison of KDD CUP 99 and NSL-KDD using artificial neural network. In: Park, J.J., Loia, V., Yi, G., Sung, Y. (eds.) CUTE/CSA -2017. LNEE, vol. 474, pp. 452–457. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7605-3_74

    Chapter  Google Scholar 

  42. Ingre, B., Yadav, A.: Performance analysis of NSL-KDD dataset using ANN. In: 2015 International Conference on Signal Processing and Communication Engineering Systems. IEEE (2015). https://doi.org/10.1109/spaces.2015.7058223

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Said El Hajji .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El Hajji, S., Moukafih, N., Orhanou, G. (2019). Analysis of Neural Network Training and Cost Functions Impact on the Accuracy of IDS and SIEM Systems. In: Carlet, C., Guilley, S., Nitaj, A., Souidi, E. (eds) Codes, Cryptology and Information Security. C2SI 2019. Lecture Notes in Computer Science(), vol 11445. Springer, Cham. https://doi.org/10.1007/978-3-030-16458-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-16458-4_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16457-7

  • Online ISBN: 978-3-030-16458-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics