Biblio

Software Defined Networking (SDN) decouples the control plane and the data plane and solves the difficulty of new services deployment. However, the threat of a single point of failure is also introduced at the same time. The attacker can launch DDoS attacks towards the controller through switches. In this paper, a DDoS attack detection method based on information entropy and deep learning is proposed. Firstly, suspicious traffic can be inspected through information entropy detection by the controller. Then, fine-grained packet-based detection is executed by the convolutional neural network (CNN) model to distinguish between normal traffic and attack traffic. Finally, the controller performs the defense strategy to intercept the attack. The experiments indicate that the accuracy of this method reaches 98.98%, which has the potential to detect DDoS attack traffic effectively in the SDN environment.

Cloud is the requirement of today's competitive world that demand flexible, agile and adaptable technology to be at par with rapidly changing IT industry. Cloud offers scalable, on-demand, pay-as-you-go services to enterprise and has hence become a part of growing trend of organizations IT service model. With emerging trend of cloud the security concerns have further increased and one of the biggest concerns related to cloud is DDoS attack. DDoS attack tends to exhaust all the available resources and leads to unavailability of services in cloud to legitimate users. In this paper the concept of fog computing is used, it is nothing but an extension to cloud computing that performs analysis at the edge of the network, i.e. bring intelligence at the edge of the network for quick real time decision making and reducing the amount of data that is forwarded to cloud. We have proposed a framework in which DDoS attack traffic is generated using different tools which is made to pass through fog defender to cloud. Furthermore, rules are applied on fog defender to detect and filter DDoS attack traffic targeted to cloud.