An Experimental Study on Assessing the Efficacy of Feature-Based Methods in Identifying DDoS Attacks Against SDN Controllers
Keywords:
Software-defined networking, Entropy, Controller, Attack detection, DDoSAbstract
A Software-Defined Network (SDN) provides several benefits to the networking industry via flexibility and centralized administration; nevertheless, this centralized control leaves it vulnerable to many forms of attacks. As a common tactic, attackers often use Distributed Denial of Service (DDoS) attacks to render the controller inoperable. To detect DDoS attacks on SDN controllers, entropy-based approaches & variants are believed to be most successful. Three modules comprise this system: traffic gathering, flow table delivery, and DDoS attack detection. To be ready for traffic identification, the traffic gathering module gathers traffic parameters. A DDoS attack detection system that takes advantage of flexible & multi-dimensional features of SDNs works by having the controller take data from statistics flow tables and apply a support vector machines (SVM) method to recognize attack traffic. The flow table delivery module then uses the traffic identification result to dynamically adjust the forwarding policy, therefore defending against DDoS attacks.
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