A comprehensive security assessment framework for software-defined networks

As Software-Defined Networking (SDN) is getting popular, its security issue is being magnified as a new controversy, and this trend can be found from recent studies of presenting possible security vulnerabilities in SDN. Understanding the attack surface of SDN is necessary, and it is the starting po...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers & security 2020-04, Vol.91, p.101720-15, Article 101720
Hauptverfasser: Lee, Seungsoo, Kim, Jinwoo, Woo, Seungwon, Yoon, Changhoon, Scott-Hayward, Sandra, Yegneswaran, Vinod, Porras, Phillip, Shin, Seungwon
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:As Software-Defined Networking (SDN) is getting popular, its security issue is being magnified as a new controversy, and this trend can be found from recent studies of presenting possible security vulnerabilities in SDN. Understanding the attack surface of SDN is necessary, and it is the starting point to make it more secure. However, most existing studies depend on empirical methods in different environments, and thus they have stopped short of converging on a systematic methodology or developing automated systems to rigorously test for security flaws in SDNs. Therefore, we need to disclose any possible attack scenarios in diverse SDN environments and examine how these attacks operate in those environments. Inspired by the necessity for disclosing the vulnerabilities in diverse SDN operating scenarios, we suggest an SDN penetration tool, DELTA, to regenerate known attack scenarios in diverse test cases. Furthermore, DELTA can even provide a chance of discovering unknown security problems in SDN by employing a fuzzing module. In our evaluation, DELTA successfully reproduced 26 known attack scenarios, across diverse SDN controller environments, and also discovered 9 novel SDN application mislead attacks.
ISSN:0167-4048
1872-6208
DOI:10.1016/j.cose.2020.101720