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 the applications of SDN, researchers have found that the controller placement directly affects network performance in SDN. In this paper, the state of the art of controller placement problem is surveyed from the perspective of optimization objective. First, we introduce the overview of SDN and controller placement problem. Then, we classify this paper of controller placement problem into four aspects (latency, reliability, and 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 safeguard scheme (SGS) for protecting control plane against DDoS attacks, and the main characteristic of SGS is deploying multicontroller 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. INDEX TERMS Software-defined networking, multi-controller, DDoS, network security, anomaly traffic detection.
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. INDEX TERMS Intrusion detection, deep belief network, particle swarm optimization, artificial fish swarm algorithm, genetic algorithm.
A novel high-selective triple-mode substrate integrated waveguide (SIW) bandpass filter using higher-order resonant modes is proposed in this Letter. The new triple-mode SIW resonator is realised by the second degenerate dual modes TM 210 and one perturbed higher-order mode TM 020 in a circular SIW cavity. The resonance of TM 020 mode can be independently moved to that of dual modes TM 210 by changing the size of a floating circular patch. The proposed triple-mode SIW filter can produce three finite-transmission zeros (FTZs), which can also be controlled well by the angle between two feeding lines. For the demonstration, a triple-mode SIW filter with the centre frequency of 13.5 GHz was designed, fabricated and measured. The proposed triple-mode circular SIW filter has the merits of the high-quality factor, high selectivity and controllable bandwidth as well as FTZs.
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