CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

DDoS Attacks Impact on Network Traffic and its Detection Approach

by Anup Bhange, Amber Syad, Satyendra Singh Thakur
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 11
Year of Publication: 2012
Authors: Anup Bhange, Amber Syad, Satyendra Singh Thakur
10.5120/5011-7332

Anup Bhange, Amber Syad, Satyendra Singh Thakur . DDoS Attacks Impact on Network Traffic and its Detection Approach. International Journal of Computer Applications. 40, 11 ( December 2012), 36-40. DOI=10.5120/5011-7332

@article{ 10.5120/5011-7332,
author = { Anup Bhange, Amber Syad, Satyendra Singh Thakur },
title = { DDoS Attacks Impact on Network Traffic and its Detection Approach },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 40 },
number = { 11 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number11/5011-7332/ },
doi = { 10.5120/5011-7332 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:21.510615+05:30
%A Anup Bhange
%A Amber Syad
%A Satyendra Singh Thakur
%T DDoS Attacks Impact on Network Traffic and its Detection Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 11
%P 36-40
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Denial of Service (DoS) attack is a malicious effort to keep endorsed users of a website or web service from accessing it, or limiting their ability to do so. A Distributed Denial of Service (DDoS) attack is a type of DoS attack in which many computers are used to cripple a web page, website or web-based service. Fault either in users’ implementation of a network or in the standard specification of protocols has resulted in gaps that allow various kinds of network attack to be launched of the type of network attacks, denial-of-service flood attacks have reason the most severe impact. This analysis study on flood attacks and Flash Crowd their improvement, classifying such attacks as either high-rate flood or low-rate flood. Finally, the attacks are appraised against principle related to their characteristics, technique and collision. This paper discusses a statistical approach to analysis the distribution of network traffic to recognize the normal network traffic behavior. The EM algorism is discussed to approximate the distribution parameter of Gaussian mixture distribution model. Another time series analysis method is studied. This paper also discusses a method to recognize anomalies in network traffic, based on a non restricted ?-stable first-order model and statistical hypothesis testing.

References
  1. http://www.cert.com
  2. M. Li. An approach to reliably identifying signs of DDOS flood attacks based on LRD traffic pattern Recognition. Computers & Security, 23(7): 549-558, 2004.
  3. C.S. Sastry, S. Rawat and A.K. Pujari. Network traffic analysis using singular value decomposition and multiscale transforms. Information Sciences, 177(23): 5275-5291, 2007.
  4. M.F. Rohani, M.A. Maarof and A. Selamat. Continuous LoSS detection using iterative window based on SOSS model and MLS approach. In Proceedings of the International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia, May 2000
  5. H. Hajji. Statistical analysis of network traffic for adaptive faults detection. IEEE Transactions on Neural Networks, 16(5):1053–1063, September 2005.
  6. J. D. Brutlag. Aberrant behavior detection in time series for network monitoring. In LISA ’00: Proceedings of the 14th USENIX conference on System administration, pages 139–146, Berkeley, CA, USA, 2000.
  7. D. Rincón and S. Sallent. On-line segmentation of non-stationary fractal network traffic with wavelet transforms and Log-likelihood-based statistics. LNCS, 3375: 110-123, 2005
  8. C. Douligeris and A. Mitrokotsa. DDoS attacks and defense mechanisms: classification and state of- the-art. Computer Networks, 44(5): 643-666, 2004.
  9. P. García-Teodoro, J. Díaz-Verdejo and G. Maciá- Fernández. Anomaly-based network intrusion detection: techniques, systems and challenges. Computers & Security, 28(1-2): 18-28, 2009.
  10. V. A. SIRIS and F. PAPAGALOU. Application of anomaly detection algorithms for detecting syn flooding attacks. In Proceedings of IEEE Global Telecommunications Conference (GLOBECOM ’04), volume 4, pages 2050–2054, Dallas, USA, 2004
  11. H. Wang, D. Zhang, and K. G. Shin. Syn-dog: Sniffing syn flooding sources. In Proceedings of the 22th International Conference on Distributed Computing Systems (ICDCS’02), pages 421–429, Washington, DC, USA, 2002. IEEE Computer Society.
  12. M. Charikar, K. Chen, and M. Farach-Colton. Finding frequent items in data streams. In Proceedings of the 29th International Colloquium on Automata, Languages and Programming (ICALP ’02), pages 693–703, London, UK, 2002. Springer-Verlag.
  13. J. D. Brutlag. Aberrant behavior detection in time series for network monitoring. In LISA ’00: Proceedings of the 14th USENIX conference on System administration, pages 139–146, Berkeley, CA, USA, 200
  14. V. Paxson. Bro: A System for Detecting Network Intruders in Real- Time. In Computer Networks, volume 31 (23–24), pages 2435–2463, 1999.
  15. E. S. Page. Continuous inspection schemes. Biometrika, 41:100–115, 1954.
  16. S.X. Wu and W. Banzhaf. The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing, 10: 1- 35, 2010.
  17. O. Salem, S. Vaton, and A. Gravey. An efficient online anomalies detection mechanism for high-speed networks. In IEEE Workshop on Monitoring, Attack Detection and Migitation (MonAM 2007), November 2007.
  18. W. Leland, M. Taqqu, W. Willinger, and D. Wilson, “On the Self-Similar Nature of Ethernet Traffic (Extended Version),” IEEE/ACM Trans. Networking, vol. 2, no. 1, pp. 1-15, Feb. 1994
  19. Y. Guan, A. A. Ghorbani, and N. Belacel, An unsupervised clustering algorithm for intrusion detection. In Proc. of the Sixteenth Canadian Conference on Artificial Intel ligancy (AI 2003), pages 616-617, Halifax, Canada, May 2003. Springer
  20. Huang Kai, Qi Zhengwei, Liu Bo” Network Anomaly Detection Based on Statistical Approach and Time Series Analysis” 2009 International Conference on Advanced Information Networking and Applications Workshops
  21. Federico Simmross, Juan Ignacio, Pablo Cassia-de-la-Higuera, Ioannis A. Dimitriadis” Anomaly Detection in Network Traffic Based on Statistical Inference and ?-Stable Modeling” IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 8, NO. 4, JULY/AUGUST 2011
  22. Khadijah Wan Mohd Ghazali and Rosilah Hassan:” Flooding Distributed Denial of Service Attacks-A Review “Journal of Computer Science7 (8): 1218-1223, 2011 ISSN 1549-3636
  23. W. Leland, M. Taqqu, W. Willinger, and D. Wilson, “On the Self-Similar Nature of Ethernet Traffic (Extended Version),” IEEE/ACM Trans. Networking, vol. 2, no. 1, pp. 1-15, Feb. 1994.
  24. A. Papoulis, Probability, Random Variables, and Stochastic Processes, third ed., McGraw-Hill, 1991.
  25. G.R. Arce, Nonlinear Signal Processing: A Statistical Approach. John Wiley and Sons, 2005.
  26. Monika sachdeva, gurvinder singh, Krishan Kumar, Kuldeep Singh”DDoS Incident and their Impact”IAJIT2010”
  27. Khadijah Wan, Mohd Ghazali Rosilah Hassan” Flooding Distributed Denial of Service Attacks-A Review “
  28. CNN. Cyber-attacks batter Web heavyweights, February2000/www.cnn.com/2000/TECH/computing/02/09/cyber.attacks
  29. CNN .Immense. Network assault takes down Yahoo, February http://www.cnn.com
Index Terms

Computer Science
Information Sciences

Keywords

DDoS Impact Anomaly Detection Method ?-Stable Model

Powered by PhDFocusTM