| Title | Adversarial Network Forensics in Software Defined Networking |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Achleitner, Stefan, La Porta, Thomas, Jaeger, Trent, McDaniel, Patrick |
| Conference Name | Proceedings of the Symposium on SDN Research |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4947-5 |
| Keywords | Attacks on SDN, Network Security Architecture, OpenFlow rule reconstruction, pubcrawl, resilience, Resiliency |
| Abstract | Software Defined Networking (SDN), and its popular implementation OpenFlow, represent the foundation for the design and implementation of modern networks. The essential part of an SDN-based network are flow rules that enable network elements to steer and control the traffic and deploy policy enforcement points with a fine granularity at any entry-point in a network. Such applications, implemented with the usage of OpenFlow rules, are already integral components of widely used SDN controllers such as Floodlight or OpenDayLight. The implementation details of network policies are reflected in the composition of flow rules and leakage of such information provides adversaries with a significant attack advantage such as bypassing Access Control Lists (ACL), reconstructing the resource distribution of Load Balancers or revealing of Moving Target Defense techniques. In this paper we introduce a new attack vector on SDN by showing how the detailed composition of flow rules can be reconstructed by network users without any prior knowledge of the SDN controller or its architecture. To our best knowledge, in SDN, such reconnaissance techniques have not been considered so far. We introduce SDNMap, an open-source scanner that is able to accurately reconstruct the detailed composition of flow rules by performing active probing and listening to the network traffic. We demonstrate in a number of real-world SDN applications that this ability provides adversaries with a significant attack advantage and discuss ways to prevent the introduced reconnaissance techniques. Our SDNMap scanner is able to reconstruct flow rules between network endpoints with an accuracy of over 96%. |
| URL | http://doi.acm.org/10.1145/3050220.3050223 |
| DOI | 10.1145/3050220.3050223 |
| Citation Key | achleitner_adversarial_2017 |
Adversarial Network Forensics in Software Defined Networking