logo

Toward a trust-based authentication framework of Northbound interface in Software Defined Networking

RESEARCH CREW
9:16 18/06/2019

Software Defined Networking (SDN) – a new rising terminology of network is recently gained more and more interest in both academic and industrial field. Not only decoupling of its control plane and data plane, SDN also provides the whole view of entire network for better and more flexible network management. Despite the benefits of the global view of the whole network, SDN with a single point of failure at the controller encounters some drawbacks and additional challenge for security. A malicious OpenFlow application (OF app) can access to SDN controller to perform illegal activities due to the lack of the authentication protocol in Northbound interface to ensure that only trusted, and authorized applications access critical network resources. The information about the whole network, such as topology data, flow information or statistics can be retrieved. Even worse the entire network can be controlled from the compromised controller. In this paper, we introduce Trust Trident - a framework of securing trustworthy authentication between applications and controller, with the controller-independent capability. It gives network administrator a fully and fine-grained observation of OF apps communicating with the controller. Threats in Northbound interface and counter measurements by our plugin are classified and evaluated according to the threat categories from the STRIDE methodology.

TIN LIÊN QUAN
An Approach of Adaptive Cyber Deception for Active Cyber-attack Defense Method based on Deep Reinforcement Learning
The diverse landscape of network models, including Software-Defined Networking (SDN), Cloud Computing (C2), and Internet of Things (IoT), is evolving to meet the demands of flexibility and performance. However, these environments face numerous security challenges due to cyber-attack complexity. Traditional defense mechanisms are no longer effective against modern attacks. Therefore,...
Multimodal Deep Learning Feedback for Generating Evasive Malware Samples against Malware Detector
As data driven-based Windows malware detectors become increasingly prevalent, the need for robust evaluation and enhancement of adversarial malware generation techniques also becomes imperative, as malicious actors will adapt and enhance their malware to evade detection. There are numerous works that introduce new techniques or enhancements for adversarial malware. One...
Medichain: A multi-chain based system for managing prescription medications using Zero Knowledge Proofs
The abuse of prescription medications has become a severe public health crisis fueled by limited coordination and oversight across healthcare systems. Current frameworks lack interoperability between doctors, pharmacies, and regulators, enabling abusive practices like doctor shopping and pharmacy hopping. To address these issues, this research proposes Medichain, a novel multichain-based...