Fingerprinting OpenFlow controllers: The first step to attack an SDN control plane
November 08, 2016 Β· Declared Dead Β· π Global Communications Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Abdelhadi Azzouni, Othmen Braham, Nguyen Thi Mai Trang, Guy Pujolle, Raouf Boutaba
arXiv ID
1611.02370
Category
cs.NI: Networking & Internet
Citations
40
Venue
Global Communications Conference
Last Checked
6 months ago
Abstract
Software-Defined Networking (SDN) controllers are considered as Network Operating Systems (NOSs) and often viewed as a single point of failure. Detecting which SDN controller is managing a target network is a big step for an attacker to launch specific/effective attacks against it. In this paper, we demonstrate the feasibility of fingerpirinting SDN controllers. We propose techniques allowing an attacker placed in the data plane, which is supposed to be physically separate from the control plane, to detect which controller is managing the network. To the best of our knowledge, this is the first work on fingerprinting SDN controllers, with as primary goal to emphasize the necessity to highly secure the controller. We focus on OpenFlow-based SDN networks since OpenFlow is currently the most deployed SDN technology by hardware and software vendors.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted