Logo PTI Logo FedCSIS

Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

Matrix profile for DDoS attacks detection


DOI: http://dx.doi.org/10.15439/2021F114

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 357361 ()

Full text

Abstract. Previous studies have focused on DDoS, which are a crucial problem in network security. This study explore a time series method MP, which has shown effective results in a number of applications. The MP is potentially well suited to use for DDoS as a rapid method of detection,a factor that is vital for the successful identification and cessation of DDoS.The study examined how the MP performed in diverse situations related to DDoS, as well as identifying those features that are most applicable in various scenarios.Results show the efficiency of MP against all types of DDoS with the exception of NTP.


  1. Dhruba Kumar Bhattacharyya and Jugal Kumar Kalita. Ddos attacks evolution, detection, prevention, reaction, and tolerance. 2016.
  2. Zhuo Lin. Internet security and firewall [j]. Journal of Changsha University, 15:32–35, 2001.
  3. Susan J Harrington. Why people copy software and create computer viruses. Information Resources Management Journal (IRMJ), 2(3):28–38, 1989.
  4. Neelam Dayal and Shashank Srivastava. Analyzing behavior of DDoS attacks to identify DDoS detection features in SDN. In 2017 9th International Conference on Communication Systems and Networks (COMSNETS), pages 274–281. IEEE, jan 2017.
  5. Shibo Luo, Jun Wu, Jianhua Li, and Bei Pei. A Defense Mechanism for Distributed Denial of Service Attack in Software-Defined Networks. 9th International Conference on Frontier of Computer Science and Technology (FCST 2015), pages 325–329, 2015.
  6. Chirag Modi, Dhiren Patel, Bhavesh Borisaniya, Hiren Patel, Avi Patel, and Muttukrishnan Rajarajan. A survey of intrusion detection techniques in cloud. Journal of network and computer applications, 36(1):42–57, 2013.
  7. Kevin Hoffman, David Zage, and Cristina Nita-Rotaru. A survey of attack and defense techniques for reputation systems. ACM Computing Surveys (CSUR), 42(1):1–31, 2009.
  8. Dhruba Kumar Bhattacharyya. DDoS attacks: evolution, detection, prevention, reaction, and tolerance. Chapman and Hall/CRC, 2019.
  9. Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. Matrix profile i: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM), pages 1317–1322. Ieee, 2016.
  10. Shaghayegh Gharghabi, Shima Imani, Anthony Bagnall, Amirali Darvishzadeh, and Eamonn Keogh. Matrix profile xii: Mpdist: a novel time series distance measure to allow data mining in more challenging scenarios. In 2018 IEEE International Conference on Data Mining (ICDM), pages 965–970. IEEE, 2018.
  11. Dieter De Paepe, Sander Vanden Hautte, Bram Steenwinckel, Filip De Turck, Femke Ongenae, Olivier Janssens, and Sofie Van Hoecke. A generalized matrix profile framework with support for contextual series analysis. Eng. Appl. Artif. Intell., 90(C), April 2020.
  12. Frank Madrid, Shima Imani, Ryan Mercer, Zachary Zimmerman, Nader Shakibay, and Eamonn Keogh. Matrix profile xx: Finding and visualizing time series motifs of all lengths using the matrix profile. In 2019 IEEE International Conference on Big Knowledge (ICBK), pages 175–182. IEEE, 2019.
  13. Haemwaan Sivaraks and Chotirat Ratanamahatana. Robust and accurate anomaly detection in ecg artifacts using time series motif discovery. Computational and mathematical methods in medicine, 2015:453214, 01 2015.
  14. Rutuja Wankhedkar and Sanjay Kumar Jain. Motif discovery and anomaly detection in an ecg using matrix profile. In Progress in Advanced Computing and Intelligent Engineering, pages 88–95. Springer, 2021.
  15. Mahmoud Said Elsayed, Nhien-An Le-Khac, Soumyabrata Dev, and Anca Delia Jurcut. Ddosnet: A deep-learning model for detecting network attacks. In 2020 IEEE 21st International Symposium on” A World of Wireless, Mobile and Multimedia Networks”(WoWMoM), pages 391–396. IEEE, 2020.