Automatic DDoS Attack Detection on SDNs: Preprint

Sabrina Corsetti, Avi Purkayastha, Adarsh Hasandka, Michael Samon

Research output: Contribution to conferencePaper


Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks pose a serious threat to computing networks - especially to critical systems within the U.S. electrical grid. As attack mechanisms have increased in complexity and variety, more sophisticated detection mechanisms have become necessary to ensure network security. This paper explores the use of artificial intelligence to automate the process of detection and mitigation of DoS and DDoS attacks within the framework of Software-Defined Networking (SDN), to a high degree. Machine learning algorithms are trained to recognize DoS and DDoS attacks and are deployed in real-time to mitigate malicious network traffic. The results show a well-tuned gradient-boosted decision tree detecting DoS and DDoS attacks, as well as initial successful mitigation of attacks within an SDN framework.
Original languageAmerican English
Number of pages17
StatePublished - 2022
Event2021 International Conference on Security and Management (SAM'21) - Las Vegas, Nevada
Duration: 26 Jul 202129 Jul 2021


Conference2021 International Conference on Security and Management (SAM'21)
CityLas Vegas, Nevada

NREL Publication Number

  • NREL/CP-2C00-81041


  • cyber detection
  • denial of service
  • machine learning


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