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Abstract-Software Defined Networking (SDN) introduces a new network architecture offering means of programmability through an externalized centralized control plane. As a result most security research addresses attacks against this central entity. Contrary to that, attacks against the data plane in SDN did not perceive a broad attention in the scientific community so far. In this work we discuss Denial of Service attacks against the data plane and their impact. We propose a tailored statistical detection approach as well as a lightweight countermeasure. We evaluate the detection by simulation and an analytical approach. Throughout this evaluation, we highlight the trade-off between detection speed and adaptability and show a way to tune the solution analytically. Our results show, that we can detect and mitigate attacks against the data plane in a lightweight and dependable way.

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Network programmability also sneaked into the mobile world leading to the emergence of Software-Defined Radio Access Network (SD-RAN) architectures. Interestingly, while only a small number of prototype architectures exist for SD-RAN, their performance evaluations are unfortunately also limited. Recent evaluations are carried out for small network dimensions of up to 50 devices, while emerging 5G/6G networks envision numbers of devices beyond 5000. Although 5G/6G applications are more stringent with respect to latency guarantees, performance evaluations of such low scale remain questionable. To fill this void, this paper presents MARC: a novel benchmarking tool for SD-RAN architectures and their controllers. We use MARC to measure, analyze and identify performance implications for two state-of-the-art open source SD-RAN solutions: FlexRAN and 5G-EmPOWER. We perceive results for monitoring application scenarios considering fully centralized control. For this setting, our findings show that the proposed architectures with a single SD-RAN controller are not scalable and can even lead to unpredictable network operations. Using our tool and based on our insights, we provide and implement design guidelines for the internal working behavior of the existing controllers.

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