In SDN, the controller is the core and is responsible for processing all flow requests of the network switches. However, due to the sudden occurrence and unbalanced distribution of flows in the network, it is likely that some controllers suffer workload that is far heavier than their load capacity, which leads to the failure of the controller and further leads to the paralysis of the entire network. To solve this problem, we propose a dynamic flow redirection scheme (DFR) to prevent network crash. We describe the phenomenon of controller failure caused by numerous flow requests. The flow redirection is formalized as a multi-objective optimization problem and constrained by flow table and bandwidth. We prove that the problem is NP-hard. We solve this problem with the dynamic flow redirection approach (DFR). First, state detection module detects whether the current flow requests will exceed the controller load. The Flow Redirection Assignment Module then computes the redirect path for the redundant flow request. Finally, Rule Dispense issues the flow rules to the corresponding switches. Simulation results show that DFR reduces network latency and reduces the overload probability of controllers by at least 3 times.

The intrusion detection models (IDMs) based on machine learning play a vital role in the security protection of the network environment, and, by learning the characteristics of the network traffic, these IDMs can divide the network traffic into normal behavior or attack behavior automatically. However, existing IDMs cannot solve the imbalance of traffic distribution, while ignoring the temporal relationship within traffic, which result in the reduction of the detection performance of the IDM and increase the false alarm rate, especially for low-frequency attacks. So, in this paper, we propose a new combined IDM called LA-GRU based on a novel imbalanced learning method and gated recurrent unit (GRU) neural network. In the proposed model, a modified local adaptive synthetic minority oversampling technique (LA-SMOTE) algorithm is provided to handle imbalanced traffic, and then the GRU neural network based on deep learning theory is used to implement the anomaly detection of traffic. The experimental results evaluated on the NSL-KDD dataset confirm that, compared with the existing state-of-the-art IDMs, the proposed model not only obtains excellent overall detection performance with a low false alarm rate but also more effectively solves the learning problem of imbalanced traffic distribution.