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Reseach Article

Novel DoS/DDoS Attack Detection and Signature Generation

by Vijay Katkar, S. G. Bhirud
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 10
Year of Publication: 2012
Authors: Vijay Katkar, S. G. Bhirud
10.5120/7224-0055

Vijay Katkar, S. G. Bhirud . Novel DoS/DDoS Attack Detection and Signature Generation. International Journal of Computer Applications. 47, 10 ( June 2012), 18-24. DOI=10.5120/7224-0055

@article{ 10.5120/7224-0055,
author = { Vijay Katkar, S. G. Bhirud },
title = { Novel DoS/DDoS Attack Detection and Signature Generation },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 10 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number10/7224-0055/ },
doi = { 10.5120/7224-0055 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:41:31.157589+05:30
%A Vijay Katkar
%A S. G. Bhirud
%T Novel DoS/DDoS Attack Detection and Signature Generation
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 10
%P 18-24
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Denial of Service (DoS) and Distributed DoS (DDoS) attacks are evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money. Combination of Intrusion detection System and Firewall is used by Business Organizations to detect and prevent Organizations' network from these attacks. But this combination cannot prevent network from novel attacks as Signatures to detect them are not available. This paper presents a light-Weight mechanism to detect novel DoS/DDoS (Resource Consumption) attacks and automatic Signature generation process to represent them in real time. Experimental results are provided to support the proposed mechanism.

References
  1. Gang Xiong, Minxia Zhang, "A Novel Method of Outliers within Data Streams Based on Clustering Evolving Model for Detecting Intrusion Attacks of Unknown Type", 2010 International Conference on Multimedia Information Networking and Security
  2. Pedro García Teodoro, Pablo Muñoz Feldstedt, David Ruete Zúñiga, "Automatic Signature Generation for Network Services Through Selective Extraction of Anomalous Contents", 2010 Sixth Advanced International Conference on Telecommunications
  3. Jie Yang, Xin Chen, Xudong Xiang, Jianxiong Wan, "HIDS-DT: An Effective Hybrid Intrusion Detection System Based on Decision Tree", 2010 International Conference on Communications and Mobile Computing
  4. Bharanidharan Shanmugam, Norbik Bashah Idris, "Improved Intrusion Detection System using Fuzzy Logic for Detecting Anamoly and Misuse type of Attacks", 2009 International Conference of Soft Computing and Pattern Recognition
  5. Imen Brahmi, Sadok Ben Yahia, Pascal Poncelet, "MAD-IDS: Novel Intrusion Detection System Using Mobile Agents and Data Mining Approaches", Lecture Notes in Computer Science, 2010, Volume 6122/2010, 73-76, DOI: 10. 1007/978-3-642-13601-6_9
  6. Feng Guo, Yingzhen Yang , Lian duan , "Anomaly Detection by Clustering in the Network", International Conference on Computational Intelligence and Software Engineering, 2009, ISBN: 978-1-4244-4507-3
  7. Z. Muda, W. Yassin, M. N. Sulaiman, N. I. Udzir, "Intrusion Detection based on K-Means Clustering and Naïve Bayes Classification", International Conference on Information Technology in Asia (CITA 11), IEEE 2011, ISBN: 978-1-61284-128-1
  8. Yu-Xin Ding, Min Xiao, Ai-Wu Liu, "Research And Implementation On Snort-Based Hybrid Intrusion Detection System", Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July IEEE 2009, DOI: 10. 1109/ICMLC. 2009. 5212282
  9. Adetunmbi A. Olusola, Adeola S. Oladele, Daramola O. Abosede, "Analysis of KDD '99 Intrusion Detection Dataset for Selection of Relevance Features", Proceedings of the World Congress on Engineering and Computer Science 2010 Volume I, IEEE 2010
  10. Neveen I. Ghali, "Feature Selection for Effective Anomaly-Based Intrusion Detection", International zJournal of Computer Science and Network Security, VOL. 9 No. 3, March 2009, pp. 285-289
  11. H. Güne? Kayac?k, A. Nur Zincir-Heywood, Malcolm I. Heywood, "Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets", Proceedings of the Third Annual Conference on Privacy, Security and Trust, October 2005, St. Andrews, Canada
  12. Wei Wang, Sylvain Gombault, Thomas Guyet, "Towards fast detecting intrusions: using key attributes of network traffic", The Third International Conference on Internet Monitoring and Protection, 978-0-7695-3189-2/08, 2008 IEEE, pp. 86 – 91
  13. M. Soleimani, E. Khosrowshahi, M. Doroud, M. Damanafshan, A. Behzadi, M. Abbaspour, "RAAS: A Reliable Analyzer and Archiver for Snort Intrusion Detection System," ACM SAC, 2007
  14. I. Qualys, "The laws of vulnerabilities: Six axioms for understanding risk" http://www. qualys. com/docs/Laws-Report. pdf
  15. KDD99CUP Dataset, http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html
  16. Vijay Katkar, Rejo Mathew, "One Pass Incremental Association Rule Detection Algorithm For Network Intrusion Detection System", International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462 Vol. 3 No. 4 Apr 2011
  17. Kristopher Kendall, "A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems", http://www. ll. mit. edu/mission/communications/ist/files/kkendall_thesis. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Novel Dos Attack Detection Automatic Signature Generation Main Memory Database Management System