Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. As the network size increases, the single centralized controller cannot meet the increasing demand for flow processing. Thus, the promising solution for SDN with large-scale networks is the multi-controller. In this paper, we present a compressive survey for multi-controller research in SDN. First, we introduce the overview of multi-controller, including the origin of multi-controller and its challenges. Then, we classify multi-controller research into four aspects (scalability, consistency, reliability, and load balancing) depending on the process of implementing the multi-controller. Finally, we propose some relevant research issues to deal with in the future and conclude the multi-controller research.

The rapid development and popularization of the network have brought many problems to network security. Intrusion detection technology is often used as an effective security technology to protect the network. The deep belief network (DBN), as a classic model of deep learning, has good classification performance and is often used in the field of intrusion detection. However, the network structure of DBN is generally set through practical experience. For the optimization problem of the DBN-based intrusion detection classification model (DBN-IDS), this paper proposes a new joint optimization algorithm to optimize the DBN's network structure. First, we design a particle swarm optimization (PSO) based on the adaptive inertia weight and learning factor. Second, we use the fish swarm behavior of cluster, foraging, and other behaviors to optimize the PSO to find the initial optimization solution. Then, based on the initial optimization solution, we use the genetic operators with self-adjusting crossover probability and mutation probability to optimize the PSO to search the global optimization solution. Finally, the global optimization solution constructed by the above-mentioned joint optimization algorithm is used as the network structure of the intrusion detection classification model. The experimental results show that compared with other DBN-IDS optimization algorithms, our algorithm shortens the average detection time by at least 24.69% on the premise of increasing the average training time by 6.9%; compared with the tested classification algorithms, our DBN-IDS improves the average classification accuracy by at least 1.3% and up to 14.80% in the five-category classification, which is proved to be an efficient DBN-IDS optimization method.

In order to simplify the management of the traditional network, Software-defined networking (SDN) has been proposed as a promising paradigm shift that decouples control plane and data plane, providing programmability to configure the network. With the deployment and application of SDN, researchers have found that the controller placement directly affect network performance in SDN. In this paper, the state of the art of controller placement problem are surveyed from the perspective of optimization objective. First, we introduce the overview of software-defined networking and controller placement problem. Then we classify literature of controller placement problem into four aspects (latency, reliability, cost and multi-objective) depending on their objective and analyze specific algorithms in different application scenarios. Finally, we identify some relevant open issues and research challenge to deal with in the future and conclude the controller placement problem.

Software-defined networking (SDN) achieves flexible and efficient network management by decoupling control plane from the data plane, where the controller with a global network view is responsible for planning routing for packets. However, the centralized design makes the controller become a potential bottleneck, and adversaries can exploit this vulnerability to launch distributed denial-of-service (DDoS) attacks to the controller. Existing solutions are fundamentally based forged traffic analysis, increasing computational cost and being prone to produce false positives. This paper proposes a safe-guard scheme (SGS) for protecting control plane against DDoS attacks, and the main characteristic of SGS is deploying multi-controller in control plane through the controller's clustering. SGS procedures are organized in two modules: anomaly traffic detection and controller dynamic defense. Anomaly traffic detection focuses on switches in data plane to distinguish forged flows from legitimate ones by innovatively adopting four-tuple feature vector. Controller dynamic defense mitigates DDoS attacks' effects on control plane by remapping controller and sending the access control message to switches. The simulation results demonstrate the efficiency of our proposed SGS with real-time DDoS attack defense and high detection accuracy, as well as high-efficiency network resource utilization.