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Context-Aware Systems and Applications, and Nature of Computation and Communication. 9th EAI International Conference, ICCASA 2020, and 6th EAI International Conference, ICTCC 2020, Thai Nguyen, Vietnam, November 26–27, 2020, Proceedings

Research Article

Statistical Properties and Modelling of DDoS Attacks

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  • @INPROCEEDINGS{10.1007/978-3-030-67101-3_4,
        author={Pheeha Machaka and Antoine Bagula},
        title={Statistical Properties and Modelling of DDoS Attacks},
        proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 9th EAI International Conference, ICCASA 2020, and 6th EAI International Conference, ICTCC 2020, Thai Nguyen, Vietnam, November 26--27, 2020, Proceedings},
        proceedings_a={ICCASA \& ICTCC},
        year={2021},
        month={1},
        keywords={DDoS EWMA CUSUM Attack modelling Probability fitting},
        doi={10.1007/978-3-030-67101-3_4}
    }
    
  • Pheeha Machaka
    Antoine Bagula
    Year: 2021
    Statistical Properties and Modelling of DDoS Attacks
    ICCASA & ICTCC
    Springer
    DOI: 10.1007/978-3-030-67101-3_4
Pheeha Machaka1,*, Antoine Bagula2
  • 1: School of Computing, University of South Africa, Unisa Science Campus, Christiaan de Wet Road & Pioneer Avenue, Johannesburg
  • 2: University of the Western Cape, Robert Sobukwe Road, Bellville
*Contact email: machap@unisa.ac.za

Abstract

The work presented in this paper is an implementation of a design of a DDoS simulation testbed that uses parameter estimation and probability fitting of source IP address features of a network. We explored the issue of lack of adequate and recent evaluation datasets, we therefore designed a way that can be used to generate synthetic data that simulates a DDoS attack. We found that the Gaussian probability distribution best represents the normal operations of a network, while the Poisson probability distribution represents the operations of a network under a DDoS attack.

Keywords
DDoS EWMA CUSUM Attack modelling Probability fitting
Published
2021-01-13
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-67101-3_4
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