Collaborative Anomaly Detection in Distributed SDN

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

4 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference (GLOBECOM 2020) - Proceedings
PublisherIEEE
ISBN (Electronic)978-1-7281-8298-8
Publication statusPublished - Dec 2020

Publication series

NameIEEE Global Communications Conference, GLOBECOM - Proceedings

Conference

Title2020 IEEE Global Communications Conference (GLOBECOM 2020)
LocationVirtual
PlaceTaiwan
CityTaipei
Period7 - 11 December 2020

Abstract

To mitigate the issues of scalability and reliability in centralized SDN, distributed SDN has emerged. However, cyber attacks in distributed SDN become increasingly serious. Since each distributed SDN controller can only obtain the network flows of its sub-network, a single controller with the biased flow information cannot detect all types of attacks in the entire network and the overall detection is a challenge. To solve the biased flow problem, we propose a collaborative anomaly detection scheme in distributed SDN, which enables multiple SDN controllers jointly train a global detection model to identify cyber attacks. We evaluate its performance based on a real-world dataset and the results show that our scheme is efficient and accurate in cyber attack detection.

Research Area(s)

  • Collaborative anomaly detection, cyber attacks, distributed SDN, generative adversarial networks

Citation Format(s)

Collaborative Anomaly Detection in Distributed SDN. / Zhou, Lei; Shu, Jiangang; Jia, Xiaohua.

2020 IEEE Global Communications Conference (GLOBECOM 2020) - Proceedings. IEEE, 2020. 9322364 (IEEE Global Communications Conference, GLOBECOM - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review