Semi-supervised machine learning approach for DDoS detection

@article{Idhammad2018SemisupervisedML,
  title={Semi-supervised machine learning approach for DDoS detection},
  author={Mohamed Idhammad and Karim Afdel and Mustapha Belouch},
  journal={Applied Intelligence},
  year={2018},
  volume={48},
  pages={3193-3208}
}
Even though advanced Machine Learning (ML) techniques have been adopted for DDoS detection, the attack remains a major threat of the Internet. [...] Key ResultsVarious experiments were performed to evaluate the proposed approach using three public datasets namely NSL-KDD, UNB ISCX 12 and UNSW-NB15. An accuracy of 98.23%, 99.88% and 93.71% is achieved for respectively NSL-KDD, UNB ISCX 12 and UNSW-NB15 datasets, with respectively the false positive rates 0.33%, 0.35% and 0.46%. Expand Abstract

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Publications citing this paper.
SHOWING 1-10 OF 14 CITATIONS

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  • 2019 IEEE 19th International Conference on Communication Technology (ICCT)
  • 2019
VIEW 1 EXCERPT
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 33 REFERENCES

Four Decades of Data Mining in Network and Systems Management

  • IEEE Transactions on Knowledge and Data Engineering
  • 2015
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

A detailed analysis of the KDD CUP 99 data set

  • 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications
  • 2009
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Entropy based worm and anomaly detection in fast IP networks

  • 14th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprise (WETICE'05)
  • 2005
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

A two-stage classifier approach using reptree algorithm for network intrusion detection

B Mustapha, EH Salah, I Mohamed
  • Int J Adv Comput Sci Appl (ijacsa)
  • 2017
VIEW 1 EXCERPT