BCFR: Blockchainbased Controller Against False Flow Rule Injection in SDN Software Defined Networking (SDN) technology increases the evolution of Internet and network development. SDN, with its logical centralization of controllers and global network overview changes the networkxe2x80x99s characteristics, on term of flexibility, availability and programmability. However, this development increased the network communication security challenges. To enhance the SDN security, we propose the BCFR solution to avoid false flow rules injection in SDN data layer devices. In this solution, we use the blockchain technology to provide the controller authentication and the integrity of the traffic flow circulated between the controller and the other network elements. This work is implemented using OpenStack platform and Onos controller. The evaluation results show the effectiveness of our proposal.

A Framework for Distributed Denial of Service Attack Detection and Reactive Countermeasure in Software Defined Network Software Defined Network is an evolving and promising architecture which allows greater control over network entities by centralising the control plane. Although on the surface SDN provides a simple framework for network programmability and monitoring, few has been said about security measures to make it more robust to hitherto security flaws. Among the identified security flaws, DDoS flooding attack continue to be one of the major security concerns as attack volumes are increasing year on year. In this paper, we developed and implement the feasibility of spoofing and flooding DDoS attack on data plane devices using Mininet emulator, floodlight and network performance testing tools. We further developed a mitigation mechanism to counter these attacks by pushing reactive flow through the controller to the attacking switch port. Our result shows that pushing flows through the controller mitigates the flooding attack with low performance overheads, and requires no change to the controllers mode of operation for deployment, which indicates a good performance of our model.