An Optimal Framework for SDN Based on Deep Neural Network

Software-defined networking (SDN) is a new paradigm that promises to change by breaking vertical integration, decoupling network control logic from the underlying routers and switches, promoting (logical) network control centralization, and introducing network programming. However, the controller is similarly vulnerable to a ???single point of failure???, an attacker can execute a distributed denial of service (DDoS) attack that invalidates the controller and compromises the network security in SDN. To address the problem of DDoS traffic detection in SDN, a novel detection approach based on information entropy and deep neural network (DNN) is proposed. This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module. The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet???s source and destination Internet Protocol (IP) addresses, and then identifies it using the DDoS detection module based on DNN. DDoS assaults were found when suspected irregular traffic was validated. Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%, with a much better accuracy rate. The false alarm rate (FAR) is much lower than that of the information entropy-based detection method. Simultaneously, the proposed framework can shorten the detection time and improve the resource utilization efficiency.

Automated summary

This article proposes a novel DDoS traffic detection method based on information entropy and deep neural network (DNN). By calculating the information entropy value of data packets and using DNN for identification, it can accurately detect DDoS activity efficiently.