Skip to main content

The HTTP Content Segmentation Method Combined with AdaBoost Classifier for Web-Layer Anomaly Detection System

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

Abstract

In this paper we propose modifications to our machine-learning web-layer anomaly detection system that adapts HTTP content mechanism. Particularly we introduce more effective packet segmentation mechanism, adapt AdaBoost classifier, and present results on more challenging dataset. In this paper we also compared our approach with other techniques and reported the results of our experiments.

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

References

  1. Kozik, R., Choraś, M., Renk, R., Holubowicz, W.: Patterns extraction method for anomaly detection in HTTP traffic. In: Herrero, A., Baruque, B., Sedano, J., Quintan, H., Corchado, E. (eds.) International Joint Conference CISIS 2015 and ICEUTE 2015, Advances in Intelligent Systems and Computing, pp. 227–236. Springer, Switzerland (2015)

    Google Scholar 

  2. ModSecurity project homepage. https://www.modsecurity.org/

  3. PHPIDS project homepage. https://github.com/PHPIDS/PHPIDS

  4. NAXSI project homepage. https://github.com/nbs-system/naxsi

  5. NGINX project homepage. http://nginx.org/en/

  6. Kruegel, C., Vigna, G.: Anomaly detection of web-based attacks. In: Proceedings of the 10th ACM Conference on Computer and Communications Security, pp. 251–261 (2003)

    Google Scholar 

  7. Ingham, K.L., Somayaji, A., Burge, J., Forrest, S.: Learning DFA representations of HTTP for protecting web applications. Comput. Netw. 51(5), 1239–1255 (2007)

    Article  MATH  Google Scholar 

  8. Hadžiosmanović, D., Simionato, L., Bolzoni, D., Zambon, E., Etalle, S.: N-Gram against the machine: on the feasibility of the n-gram network analysis for binary protocols. In: Balzarotti, D., Stolfo, S.J., Cova, M. (eds.) RAID 2012. LNCS, vol. 7462, pp. 354–373. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33338-5_18

    Chapter  Google Scholar 

  9. Bolzoni, D., Zambon, E., Etalle, S., Hartel, PH.: POSEIDON: a 2-tier anomaly-based network intrusion detection system. In: IWIA 2006: Proceedings of 4th IEEE International Workshop on Information Assurance, pp. 144–156 (2006)

    Google Scholar 

  10. Wang, K., Parekh, J.J., Stolfo, S.J.: Anagram: a content anomaly detector resistant to mimicry attack. In: Recent Advances in Intrusion Detection, pp. 226–248 (2006)

    Google Scholar 

  11. Perdisci, R., Ariu, D., Fogla, P., Giacinto, G., Lee, W.: McPAD: a multiple classifier system for accurate payload-based anomaly detection. Comput. Netw. 53(6), 864–881 (2009)

    Article  MATH  Google Scholar 

  12. Sundfeld, D., Melo, A.C.M.A.: MSA-GPU: exact multiple sequence alignment using GPU. In: Setubal, J.C., Almeida, N.F. (eds.) BSB 2013. LNCS, vol. 8213, pp. 47–58. Springer, Heidelberg (2013). doi:10.1007/978-3-319-02624-4_5

    Chapter  Google Scholar 

  13. Higgins, D.G., Sharp, P.M.: Clustal: a package for performing alignment on a microcomputer. Gene 73, 237–244 (1988)

    Article  Google Scholar 

  14. Gotoh, O.: Sequence alignments by iterative refinement as assessed by reference to structural alignments. J. Mol. Biol. 264(4), 823–838 (1996)

    Article  Google Scholar 

  15. Wozniak, M.: Hybrid Classifiers: Methods of Data, Knowledge, and Classifiers Combination. Springer Series in Studies in Computational Intelligence. Springer, Heidelberg (2013)

    Google Scholar 

  16. Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Data Management Systems, 2nd edn. Morgan Kaufmann, USA (2005)

    MATH  Google Scholar 

  17. Torrano-Gimnez, C., Prez-Villegas, A., Alvarez, G.: The HTTP dataset CSIC (2010). http://users.aber.ac.uk/pds7/csic_dataset/csic2010http.html

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rafał Kozik .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kozik, R., Choraś, M. (2017). The HTTP Content Segmentation Method Combined with AdaBoost Classifier for Web-Layer Anomaly Detection System. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47364-2_54

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47363-5

  • Online ISBN: 978-3-319-47364-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics