Distributed Detection System Using Wavelet Decomposition and Chi-Square Test

  • Fatima Ezzahra OuerfelliEmail author
  • Khaled Barbaria
  • Belhassen Zouari
  • Claude Fachkha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12026)


As of today, Distributed Denial of Service Attacks remain one the most devastating threats online. This paper presents an estimation model that integrates the discrete wavelet transform (DWT) and Chi-Square test (\( X_{2} \)) for detecting DDoS attacks. The present model presents a distributed architecture reducing the risk of single point of failure and increasing the reliability of the system. First, we uses the DWT to decompose the traffic data. Then, the obtained detail (high-frequency) components is used as input variable to forecast future traffic attack. To ensure a complete distribution of our system we test the PAXOS protocol which give a reliable communication between detection systems. The model is tested using real datasets of DDoS traces. So, our proposed system outperforms other conventional models that use a centralized architecture.


Denial of Service Wavelet decomposition Distributed systems DDoS Chi-Square 


  1. 1.
    Cheng, R., Xu, R., Tang, X., Sheng, V.S., Cai, C.: An abnormal network flow feature sequence prediction approach for DDoS attacks detection in big data environment. Comput. Mater. Contin. 55(1), 095–095 (2018)Google Scholar
  2. 2.
    Du, Z., Ma, L., Li, H., Li, Q., Sun, G., Liu, Z.: Network traffic anomaly detection based on wavelet analysis. In: 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 94–101. IEEE (2018)Google Scholar
  3. 3.
    Ouerfelli, F.E., Barbaria, K., Bou-Harb, E., Fachkha, C., Zouari, B.: On the collaborative inference of DDoS: an information-theoretic distributed approach. In: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 518–523. IEEE (2018) Google Scholar
  4. 4.
    Feder, A., Gandal, N., Hamrick, J., Moore, T.: The impact of DDoD and other security shocks on bitcoin currency exchanges: evidence from Mt. Gox. J. Cybersecur. 3(2), 137–144 (2018)CrossRefGoogle Scholar
  5. 5.
    Feinstein, L., Schnackenberg, D., Balupari, R., Kindred, D.: Statistical approaches to DDoS attack detection and response. In: 2003 Proceedings of the DARPA Information Survivability Conference and Exposition, vol. 1, pp. 303–314. IEEE (2003)Google Scholar
  6. 6.
    Kaur, G., Bansal, A., Agarwal, A.: Wavelets based anomaly-based detection system or J48 and Naïve bayes based signature-based detection system: a comparison. In: Perez, G.M., Tiwari, S., Trivedi, M.C., Mishra, K.K. (eds.) Ambient Communications and Computer Systems. AISC, vol. 696, pp. 213–224. Springer, Singapore (2018). Scholar
  7. 7.
    Kuznetsova, A., Monakhov, Y., Nikitin, O., Kharlamov, A., Amochkin, A.: A machine-synesthetic approach to DDoS network attack detection. arXiv preprint arXiv:1901.04017 (2019)
  8. 8.
    Lamport, L., et al.: Paxos made simple. ACM SIGACT News 32(4), 18–25 (2001)Google Scholar
  9. 9.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefGoogle Scholar
  10. 10.
    Nanadikar, K., Kachi, A., Karkhanis, A., Patole, S.: FireCol: a collaborative protection network for the detection of flooding DDoS attack. Int. J. Eng. Res. Technol. 3 (2014) Google Scholar
  11. 11.
    Procopiou, A., Komninos, N., Douligeris, C.: ForChaos: real time application DDoS detection using forecasting and chaos theory in smart home IoT network. Wirel. Commun. Mob. Comput. 2019 (2019)CrossRefGoogle Scholar
  12. 12.
    Sarre, R., Lau, L.Y.C., Chang, L.Y.: Responding to cybercrime: current trends (2018)CrossRefGoogle Scholar
  13. 13.
    Shannon, C.: CAIDA anonymized 2008 internet traces dataset.
  14. 14.
    Siddiqui, A.J., Boukerche, A.: On the impact of DDoS attacks on software-defined internet-of-vehicles control plane. In: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 1284–1289. IEEE (2018)Google Scholar
  15. 15.
    Snedecor, G.W., Cochran, W.G.: Statistical Methods. Iowa State University Press, Ames (1989)zbMATHGoogle Scholar
  16. 16.
    Soros, G.: Remarks delivered at the world economic forum (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Mediatron LabUniversity of CarthageTunisTunisia
  2. 2.University of DubaiDubaiUAE
  3. 3.Steppa Cyber Inc.LongueuilCanada

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