Analysis of HTTP requests for anomaly detection of web attacks

Proceedings - 2014 World Ubiquitous Science Congress: 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing, DASC 2014

Volumn , Issue , 2014, Pages 406-411

  Zolotukhin, Mikhail ^a   Hämäläinen, Timo ^a   Kokkonen, Tero ^b   Siltanen, Jarmo ^b  

^a UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^b JAMK UNIVERSITY OF APPLIED SCIENCES   (Finland)

Author keywords

anomaly detection; data mining; entropy; intrusion detection; n gram

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; ENTROPY; HTTP; INTRUSION DETECTION; NETWORK SECURITY; WEB SERVICES; WEBSITES;

ACCURACY RATE; ANOMALY-DETECTION ALGORITHMS; CONFIDENTIAL INFORMATION; DATA MINING ALGORITHM; N-GRAMS; NETWORK ATTACK; NETWORK INTRUSIONS; WEB-BASED APPLICATIONS;

ANOMALY DETECTION;

EID: 84916211158     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DASC.2014.79     Document Type: Conference Paper

References (20)

1. S. Mukkamala and A. Sung. A comparative study of techniques for intrusion detection. Proc. of the 15th IEEE International Conference Tools with Artificial Intelligence, pp. 570-577, 2003.
2. A. Nguyen-Tuong, S. Guarnieri, D. Greene, J. Shirley and D. Evans. Automatically Hardening Web Applications Using Precise Tainting. Proc. of the 20th IFIP International Information Security Conference (SEC), pp. 372-382, 2005.
3. S. Axelsson. Research in intrusion-detection systems: A survey. Department of Computer Engineering, Chalmers University of Technology, Goteborg, Sweden, Technical Report. pp. 98-117, 1998.
4. D. Gollmann. Computer Security. Wiley, 2nd edition, 2006.
5. T. Sipola, A. Juvonen and J. Lehtonen. Anomaly detection from network logs using diffusion maps. Springer, Engineering Applications of Neural Networks, IFIP Advances in Information and Communication Technology, pp. 172-181, 2011.
6. W. K. G. Fan. An adaptive anomaly detection of WEB-based attacks. Proc. of the 7th International Conference on Computer Science & Education (ICCSE), pp. 690-694, 2012.
7. M. Zolotukhin, T. Hämäläinen, A. Juvonen. Online Anomaly Detection by Using N-gram Model and Growing Hierarchical Self-Organizing Maps. Proc. of the 8th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 47-52, 2012.
8. J. Wang, M. Zhang, X. Yang, K. Long and C. Zhou. HTTP-sCAN: Detecting HTTP-flooding attaCk by modeling multi-features of web browsing behavior from noisy dataset. Proc. of the 19th Asia-Pacific Conference on Communications (APCC), pp. 677-682, 2013.
9. Apache 2. 0 Documentation (2011). http://www. apache. org/.
10. A. Klein. Detecting and Preventing HTTP Response Splitting and HTTP Request Smuggling Attacks at the TCP Level. Tech. Note. http://www. securityfocus. com/archive/1/408135, Aug. 2005.
11. C. Y. Suen. n-Gram Statistics for Natural Language Understanding and Text Processing. IEEE Tran. on Pattern Analysis and Machine Intelligence, Vol. PAMI-1, Is. 2, pp. 164-172, 1979.
12. T. Hirsimaki, J. Pylkkonen and M. Kurimo. Importance of High-Order N-Gram Models in Morph-Based Speech Recognition. IEEE Tran. on Audio, Speech, and Language Processing, Vol. 17, Is. 4, pp. 724-732, 2009.
13. S. Lloyd. Least squares quantization in pcm. IEEE Trans. on Inf. Theor., Vol. 28 Is. 2, pp. 129-137, 2006.
14. D. Pham, S. Dimov and C. Nguyen. Selection of k in k-means clustering. Proc. of IMechE Mechanical Engineering Science, pp. 103-119, 2005.
15. M. Ester, H. Kriegel, S. Jörg, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press, pp. 226-231, 1996.
16. J. Kim. The Anomaly Detection by Using DBSCAN Clustering with Multiple Parameters. Proc. of ICISA, pp. 1-5, 2011.
17. Smiti, A. DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques. Proc. of International Conference on Intelligent Engineering Systems (INES), pp. 573-578, 2012.
18. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, Vol. 12, Is. 2, pp. 181-201, 2001.
19. D. M. J. Tax and R. P. W. Duin. Support Vector Data Description. Machine Learning, Vol. 54, Is. 1, pp. 45-56, 2004.
20. C. Saranya and G. Manikandan. A Study on Normalization Techniques for Privacy Preserving Data Mining. International Journal of Engineering and Technology (IJET), Vol. 5, Is. 3, pp. 2701-2704, 2009.