Skip to main content
Log in

RETRACTED ARTICLE: Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 23 May 2022

This article has been updated

Abstract

The problem of low rate attack detection has been well studied in different situations. However the methods suffer to achieve higher performance in low rate attack detection. The multimedia transmission is focused on transmitting video and audio which claims higher bandwidth conditions. There exists no such algorithm in detecting low rate attacks for invariant network conditions. To solve this issue, an invariant feature based approach is presented in this paper. The method maintains the network features like the routes, bandwidth conditions and traffic. Based on these features, a set of routes has been identified for each data transmission. Here, low rate attack detection is performed at the reception of any packet and the data transmission is performed using cooperative routing. From the packet features, and the route being followed, the method identifies the class of route, traffic and bandwidth conditions of the route. Using these features, the method computes Network Transmission Support measure. Based on the NTS value, the method performs low rate attack detection and improves the performance.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Change history

References

  • Ain A , Bhuyan MH (2016) Rank correlation for low-rate DDoS attack detection: an empirical evaluation. Int J Netw Secur 18(3):474–480

    Google Scholar 

  • Baskar M, Gnansekaran T (2017a) Developing efficient intrusion tracking system using region based traffic impact measure towards the denial of service attack mitigation. J Comput Theor Nanosci 14(7):3576–3582

    Article  Google Scholar 

  • Baskar M, Gnansekaran T (2017b) Multi model network analysis for improved intrusion tracing towards mitigating DDoS attack. Asian J Res Soc Sci Hum 7(3):1343–1353

    Google Scholar 

  • Bhuyan MH, Bhattacharyya DK, Kalita JK (2016) Network anomaly detection: methods, systems and tools. Secur Commun Netw 9(16):3251–3270

    Article  Google Scholar 

  • Chen Z (2017) FRRED: fourier robust RED algorithm to detect and mitigate LDoS attacks. Zooming Innovation in Consumer Electronics International Conference (ZINC), IEEE, pp. 1–10

  • Gupta A, Singh D, Kaur M (2020) An efficient image encryption using non-dominated sorting genetic algorithm-III based 4-D chaotic maps. J Ambient Intell Hum Comput 11:1309–1324

    Article  Google Scholar 

  • Indra P, Manikandan M (2020) Multilevel tetrolet transform based breast cancer classifier and diagnosis system for healthcare applications. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-01755-z

    Article  Google Scholar 

  • Jia B, Xiaohong H (2017) A DDoS attack detection method based on hybrid heterogeneous multi classifier ensemble learning. J Electr Comput Eng 2017:1–9

    Article  Google Scholar 

  • Latif R, Haider A (2014) Analyzing feasibility for deploying very fast decision tree for DDoS attack detection in cloud-assisted WBA. Intell Comput Theory 8(5):507–519

    Article  Google Scholar 

  • Lu Z (2017) Low-rate DDoS attack detection using expectation of packet size, Hindawi. Secur Commun Netw 2017:1–14

    Google Scholar 

  • Luo J, Yang X (2014) A mathematical model for low-rate shrew DDoS. IEEE Trans Inf Forensics Secur 9(7):1069–1083

    Article  Google Scholar 

  • Saied A (2016) Detection of known and unknown DDoS attacks using artificial neural networks. Elsevier Nuro Comput 172(8):385–393

    Google Scholar 

  • Suleman K, Ainuddin AG (2018) Feature selection of denial-of-service attacks using entropy and granular computing. Arab J Sci Eng 43(2):499–508

    Article  Google Scholar 

  • Thapngam T (2014) Distributed denial of service (DDoS) detection by traffic pattern analysis. Peer-to-Peer Netw Appl 7(4):346–358

    Article  Google Scholar 

  • Xiao P, Qu WY (2015) Detecting DDoS attacks against data center with correlation analysis. Comput Commun 67:66–74

    Article  Google Scholar 

  • Yang X, Li K, Wanlie Z (2011) Low-rate DDoS attacks detection and traceback by using new information metrics. IEEE Trans Inf Forens Secur 6(2):426–437

    Article  Google Scholar 

  • Zhang S (2010) Detection of low-rate DDoS attack based on self-similarity. IEEE workshop on Educational technology and computer science (ETCS) pp 1–15

  • Zhijun W (2016) Low-rate DoS attacks detection based on network multifractal. IEEE Trans Depend Secur Comput 13(5):559–567

    Article  Google Scholar 

  • Zhou W, Jia W, Wen S (2014) Detection and defense of application-layer DDoS attacks in backbone web traffic. Fut Gen Comput Syst 38:36–46

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Baskar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-03919-5

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suchithra, M., Baskar, M., Ramkumar, J. et al. RETRACTED ARTICLE: Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS. J Ambient Intell Human Comput 12, 5471–5477 (2021). https://doi.org/10.1007/s12652-020-02056-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02056-1

Keywords

Navigation