Towards an adaptive and effective IDS using openflow

Abstract

Processing huge amounts of traffic from core network components with respect to security remains a challenging task, since the amounts of data increase continuously. Therefore, new approaches need to be investigated to detect and handle attacks already in high-speed environments. In this PhD research, we will develop a new approach for detecting network attacks by processing data from core network components taking advantage of properties of OpenFlow in an SDN environment. Using this, we can collect metadata about forwarded traffic in an immediate and effective way. In addition, our solution will enable dynamic and adaptive redirection of traffic to various IDSs including cloud-based IDS solutions.