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Autor
Kokoszka Piotr (Colorado State University, USA), Lin Mengting (Colorado State University, USA), Wang Haonan (Colorado State University, USA), Hayne Stephen (Colorado State University, USA)
Tytuł
Statistical Risk Quantification of Two-directional Internet Traffic Flows
Źródło
Statistics in Transition, 2024, vol. 25, nr 1, s. 1-22, tab., wykr., bibliogr. 36 poz.
Słowa kluczowe
Funkcje połączeń, Analiza danych funkcjonalnych, Internet, Analizy głównych komponentów
Copula Functions, Functional data analysis, Internet, Principal Component Analysis (PCA)
Uwagi
summ.
Abstrakt
We develop statistical methodology for the quantification of risk of source-destination pairs in an internet network. The methodology is developed within the framework of functional data analysis and copula modeling. It is summarized in the form of computational algorithms that use bidirectional source-destination packet counts as input. The usefulness of our approach is evaluated by an application to real internet traffic flows and via a simulation study. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Pełny tekst
Pokaż
Bibliografia
Pokaż
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  3. Awan, Mazhar Javed, Farooq, Umar, Babar, Hafiz Muhammad Aqeel, Yasin, Awais, Nobanee, Haitham, Hussain, Muzammil, Hakeem, Owais and Zain, Azlan Mohd, (2021). Real-time DDoS attack detection system using big data approach. Sustainability, 13, no. 19, 10743.
  4. Berrendero, José R, Justel, Ana and Svarc, Marcela, (2011). Principal components for multivariate functional data. Computational Statistics & Data Analysis, 55, no. 9, pp. 2619-2634.
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  6. Bosq, Dennis, (2000). Linear Processes in Function Spaces. Springer.
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  8. Czado, Claudia, (2019). Analyzing Dependent Data with Vine Copulas: A Practical Guide with R. Springer.
  9. Dai, Wenlin and Genton, Marc G., (2018). Multivariate functional data visualization and outlier detection. Journal of Computational and Graphical Statistics, 27, no. 4, pp. 923-934.
  10. Demarta, Stefano and McNeil, Alexander, (2005). The t copula and related copulas. International Statistical Review, 73, pp. 111-129.
  11. Dong, Shi and Sarem, Mudar, (2019). DDoS attack detection method based on improved KNN with the degree of DDoS attack in software-defined networks. IEEE Access, 8, pp. 5039-5048.
  12. Ferraty, Frédérick and Vieu, Philippe, (2006). Nonparametric Functional Data Analysis: Theory and Practice. Springer.
  13. Fouladi, Ramin Fadaei, Kayatas, Cemil Eren and Anarim, Emin, (2016). Frequency based DDoS attack detection approach using naive Bayes classification. In 2016 39th International Conference on Telecommunications and Signal Processing (TSP), pp. 104-107. IEEE.
  14. Fouladi, Ramin Fadaei, Seifpoor, Tina and Anarim, Emin, (2013). Frequency characteristics of DoS and DDoS attacks. In 2013 21st Signal Processing and Communications Applications Conference (SIU), pp. 1-4. IEEE.
  15. Genest, Christian and Nešlehová, Johanna, (2012). Copulas and copula models. In Encyclopedia of Environmetrics (eds El-Shaarawi A.H. and PiegorschW.W.), 2 edn, volume 2, pp. 541-553. Wiley, Chichester.
  16. Górecki, Tomasz, Krzyśko, Mirosław, Waszak, Łukasz and Wołyński, Waldemar, (2018). Selected statistical methods of data analysis for multivariate functional data. Statistical Papers, 59, no. 1, pp. 153-182.
  17. Happ, Clara and Greven, Sonja, (2018). Multivariate functional principal component analysis for data observed on different (dimensional) domains. Journal of the American Statistical Association, 113, number 522, pp. 649-659.
  18. Hofert, Marius, Kojadinovic, Ivan, Mächler, Martin and Yan, Jun, (2018). Elements of Copula Modeling with R. Springer.
  19. Horváth, Lajos and Kokoszka, Piotr, (2012). Inference for Functional Data with Applications, volume 200. Springer Science & Business Media.
  20. Hubert, Mia, Rousseeuw, Peter J and Vanden Branden, Karlien, (2005). ROBPCA: a new approach to robust principal component analysis. Technometrics, 47, no. 1, pp. 64-79.
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  25. Krzyśko, Mirosław and Smaga, Łukasz, (2020). Measuring and testing mutual dependence of multivariate functional data. Statistics in Transition, 21, no. 3, pp. 21-37.
  26. Krzyśko, Mirosław and Smaga, Łukasz, (2021). Two-sample tests for functional data using characteristic functions. Austrian Journal of Statistics, 50, no. 4, pp. 53-64.
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  28. Modi, Chirag, Patel, Dhiren, Borisaniya, Bhavesh, Patel, Hiren, Patel, Avi and Rajarajan, Muttukrishnan, (2013). A survey of intrusion detection techniques in Cloud. Journal of Network and Computer Applications, 36, no. 1, pp. 42-57.
  29. Nelsen, Roger, (2006). An Introduction to Copulas. Springer.
  30. Nishanth, N. and Mujeeb, A., (2020). Modeling and detection of flooding-based denialof- service attack in wireless ad hoc network using Bayesian inference. IEEE Systems Journal, 15, no. 1, pp. 17-26.
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Cytowane przez
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ISSN
1234-7655
Język
eng
URI / DOI
http://dx.doi.org/10.59170/stattrans-2024-001
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