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With the rising demand for incorporating smartness over a security operation in networking technologies, Software Defined Network (SDN) has been witnessed to be extensively researched. SDN is one of the integral parts of operation in large scale networking operations e.g., Internet-of-Things (IoT), owing to its highly flexible communication protocols and centralized controlling features. Although there has been an extensive review of literature towards the security aspect of SDN, they do not offer full-fledged solutions especially if the adversary is unknown. Therefore, the proposed manuscript presents a novel framework capable of identifying the degree of severity of the attack from the rate of request message originated from a switch of SDN node and offers a decisive operation of resisting such malicious flows using an auxiliary agent. The auxiliary agent resides in the data plane and works alongside a switch to identify and confirm malicious flow. This information is further updated to the SDN controller, which can further take action that leads to isolating the adversary and allowing only flows with validated legitimacy. The study outcome shows, the proposed system excels better both in security and communication performance.

A Framework for Identifying and Mitigating Malicious Flow in Software Defined Network Deployed over an IoT Ecosystem