Biblio

Internet always remains the target for the cyberattacks, and attackers are getting equipped with more potent tools due to the advancement of technology to preach the security of the Internet. Industries and organizations are sponsoring many projects to avoid these kinds of problems. As a result, SDN (Software Defined Network) architecture is becoming an acceptable alternative for the traditional IP based networks which seems a better approach to defend the Internet. However, SDN is also vulnerable to many new threats because of its architectural concept. SDN might be a primary target for DoS (Denial of Service) and DDoS (Distributed Denial of Service) attacks due to centralized control and linking of data plane and control plane. In this paper, the we propose a novel technique for detection of DDoS attacks using information theory metric. We compared our approach with widely used Intrusion Detection Systems (IDSs) based on Shannon entropy and Renyi entropy, and proved that our proposed methodology has more power to detect malicious flows in SDN based networks. We have used precision, detection rate and FPR (False Positive Rate) as performance parameters for comparison, and validated the methodology using a topology implemented in Mininet network emulator.