DDoS Attacks Detection with AutoEncoder

Kun Yang, Junjie Zhang, Yang Xu, Jonathan Chao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although many distributed denial of service (DDoS) attacks detection algorithms have been proposed and even some of them have claimed high detection accuracy, DDoS attacks are still a major problem for network security. The latent and inherent problems of these detection algorithms are 1) Requirement of both normal and attack data for building detection models, and 2) Almost inability to detect novel and unknown DDoS attacks. To conquer the problems, this paper proposes an AutoEncoder based DDoS attacks Detection Framework (AE-D3F), which only uses normal traffic to build the detection model and is able to update itself automatically as time goes. Experimental results on synthetic and public traffic show that our AE-D3F can not only achieve 82.00% detection rate (DR) with 0 false positive rate (FPR), better than classical anomaly detection approaches, but also detect novel and unknown attacks.

Original languageEnglish (US)
Title of host publicationProceedings of IEEE/IFIP Network Operations and Management Symposium 2020
Subtitle of host publicationManagement in the Age of Softwarization and Artificial Intelligence, NOMS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728149738
DOIs
StatePublished - Apr 2020
Event2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020 - Budapest, Hungary
Duration: Apr 20 2020Apr 24 2020

Publication series

NameProceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020

Conference

Conference2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
CountryHungary
CityBudapest
Period4/20/204/24/20

Keywords

  • Anomaly Detection
  • DDoS
  • Deep Learning
  • Machine Learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence

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  • Cite this

    Yang, K., Zhang, J., Xu, Y., & Chao, J. (2020). DDoS Attacks Detection with AutoEncoder. In Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020 [9110372] (Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NOMS47738.2020.9110372