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By endowing network with the ability of reliable scheduling of security resources, it can realize the dynamic deployment of security resources as well as the efficient configuration of protection resources, and further can quickly respond to network security threats and emergencies timely. Furthermore, due to the network is increasingly complex, it is of great importance to make it reliable by invoking appropriate security resources. However, the security mechanism and response speed of traditional network based on security domain division, network boundary protection are hard to meet the requirements of the virtual network. In this paper, we propose the SDN-empowered reliable and dynamic scheduling scheme for security resources. In the scheme, we use network security resource virtualization technology to virtualize and modularize network security software and hardware resources; at the SDN control layer, we propose meta-security function combination technology to provide on-demand customization for various security applications’ security service; by using role-based conflict detection and conflict resolution technology, we can detect and handle security rule conflicts. The experiments show that the scheme is reliable and efficient with low latency and great throughput.

Software Defined Networking (SDN) is a new promising network architecture, with the property of decoupling the data plane from the control plane and centralizing the network topology logically, making the network more agile than traditional networks. However, with the continuous expansion of network scales, the single-controller SDN architecture is unable to meet the performance requirements of the network. As a result, the logically centralized and physically separated SDN multi-controller architecture comes into being, and thereupon the Controller Placement Problem (CPP) is proposed. In order to minimize the propagation latency in Wide Area Network (WAN), we propose Greedy Optimized K-means Algorithm (GOKA) which combines K-means with greedy algorithm. The main thought is to divide the network into multiple clusters, merge them greedily and iteratively until the given number of controllers is satisfied, and place a controller in each cluster through the K-means algorithm. With the purpose of proving the effectiveness of GOKA, we conduct experiments to compare with Pareto Simulated Annealing (PSA), Adaptive Bacterial Foraging Optimization (ABFO), K-means and K-means$++$ on 6 real topologies from the Internet Topology Zoo and Internet2 OS3E. The results demonstrate that GOKA has a better and more stable solution than other four heuristic algorithms, and can decrease the propagation latency by up to $83.3\%$, $70.7\%$, $88.6\%$ and $64.5\%$ in contrast to PSA, ABFO, K-means and K-means$++$, respectively. Moreover, the error rate between GOKA and the best solution is always less than $10\%$, which promises the precision of our proposed algorithm.

Mobile traffic is currently the most important traffic on the Internet, both domestically and internationally. This is predominantly attributable to widespread cloud computing-based services on mobile networks. Fifth generation (5G) mobile networks are expected to comfortably accommodate this type of traffic by supporting 1,000 times faster speeds than conventional 4G. However, commercial implementation of 5G mobile is targeted for the year 2020. Therefore, interim accommodation approaches are needed. In essence, 5G mobile networks are an evolution of 4G in which a licensed spectrum that generates revenue from user data transfer is being considered. Consequently, more cost-effective methods that utilize the unlicensed spectrum are also desired. Meanwhile, concomitant with pursuit of miniaturization and reduction in weight, the number and types of network interfaces in recent mobile devices are increasing. We propose a method for carrier aggregation of multiple heterogeneous wireless links based on software-defined networking (SDN) that controls traffic and manages the wireless interfaces of mobile devices effectively when multiple radio access technologies are available. Evaluations conducted of the proposed method on an experimental testbed developed using the OpenStack cloud computing platform with aggregated multiple radio access technology environments indicate that it is feasible and provides higher data rate.

Network Application Classification (NAC) is a vital technology for intrusion detection, Quality-of-Service (QoS)-aware traffic engineering, traffic analysis, and network anomalies. Researchers have focused on designing algorithms using deep learning models based on statistical information to address the challenges of traditional payload and port-based traffic classification techniques. Internet of Things (IoT) and Software Defined Network (SDN) are two popular technologies nowadays and aims to connect devices over the internet and intelligently control networks from a centralized space. IoT aims to connect billions of devices; therefore, classification is essential for efficient processing. SDN is a new networking paradigm, which separates data plane measurement from the control plane. The emergence of deep learning algorithms with SDN provides a scalable traffic classification architecture. Due to the inadequate results of payload and port-based approaches, a statistical technique to classify network traffic into different classes using a Convolution Neural Network (CNN) and a Recurrent Neural Network (RNN) is presented in this paper. This paper provides a classification method for software defined IoT networks. The results show that, contrary to other traffic classification methods, the proposed approach offered a better accuracy rate of over 99 %, which is promising.

Software-defined networking (SDN) is a networking paradigm of subsequent generation where various network components are used by a centralized controller that allows reliability in network system configuration, execution of policy decisions, and management via a primary programmable network infrastructure unit. SDN is known to deny DDoS attacks despite the default security protocols. State-of-the-art researches have shown that SDN intrusion is possible in diverse layers of its generalized architecture. Addressing this problem, this work presents an optimized intrusion detection system for SDN to mitigate the effect of DDoS attacks. This article’s main contribution comprises the development of a voting strategy-based ensemble classifier, which is established based on bio-inspired particle swarm optimization and salp swarm optimization in the context of optimized classification of DDoS attack-prone traffic SDN. Experimental analysis of the proposed SDN-IDS depicts that the proposed strategy outperforms existing classifiers in terms of accuracy.

Software Defined Networking (SDN) is a promising paradigm in the field of network technology. This paradigm suggests the separation between the control plane and the data plane which brings flexibility, efficiency and programmability to network resources.

SDN deployment in large scale networks raises many issues which can be overcame using a collaborative multi-controller approaches. Such approaches can resolve problems of routing optimization and network scalability. In large scale networks, such as SD-WAN, routing optimization consists of achieving a trade-off between per-flow QoS, the load balancing in each domain as well as the resource utilization in inter-domain links. Multi-Agent Reinforcement Learning paradigm(MARL) is one of the most popular solutions that can be used to optimize routing strategies in SD-WAN.

This paper proposes an efficient approach based on MARL which is able to ensure a load balancing among each network as well as optimized resource utilization of inter-domain links. This approach profits from our previous work, denoted SPFLR, and tries to balance the load of the whole network using Deep Q-Networks (DQN) algorithms. Simulation results show that the proposed solution performs better than parallel solutions such as BGP-based routing and random routing.

Software-defined network (SDN) is a new network structure, which has the characteristics of centralized management and programmable, and is widely used in the field of Internet of things. Distributed denial of service (DDoS) attack is one of the most threatening attacks in SDN network. How to effectively detect DDoS attacks has become a research hotspot in the field of SDN security management. Aiming at the above problems, this paper proposes a DDoS attack detection method based on Deep belief network (DBN) in SDN network architecture. By extracting the characteristics of OpenFlow switch flow table entries, DBN algorithm is trained to detect whether there are DDoS attacks. The experimental results show that the method is better than the other algorithms in accuracy, precision and recall.

The control plane plays an essential role in the implementation of Software Defined Network (SDN) architecture. Basically, the control plane is an isolated process and operates on control layer. The control layer encompasses controllers which provide a global view of the entire SDN. The Controller selection is more crucial for the network administrator to meet the specific use case. This research work mainly focuses on obtaining a better SDN controller. Initially, the SDN controllers are selected using integrated Analytic Hierarchy Process and Technique for Order Preference Similarity to Ideal Solution (AHP and TOPSIS) method. It facilitates to select minimal number of controllers based on their features in the SDN application. Finally, the performance evaluation is carried out using the CBENCH tool considering the best four ranked controllers obtained from the previous step. In addition, it is validated with the real-time internet topology such as Abilene and ERNET considering the delay factor. The result shows that the “Floodlight” controller responds better for latency and throughput. The selection of an optimum controller-Floodlight, using the real-world Internet topologies, outperforms in obtaining the path with a 28.57% decrease in delay in Abilene and 16.94% in ERNET. The proposed work can be applied in high traffic SDN applications.

Wireless Sensor Network (WSN) is the interconnection between things or objects embedded with hardware and software. In WSN, small end devices (like sensors) and high end devices (like servers) are connected to the Internet. For WSN enabled in Software-Defined Network (SDN), the routers are controlled using a controller server node. It is a dynamic network due to the presence of mobile nodes and energy constrained nodes. The routing is the process of detecting route from source to target. In dynamic networks like WSN, routing is a challengeable task. This paper is to provide a routing solution for backboneless SDN-enabled WSN. The proposed work enhances routing Quality of Service (QoS) in WSN. The paths are dynamically reallocated to reduce the packet loss.

Software defined network (SDN) controller selection in SDN is a key challenge to the network administrator. In SDN, control plane is an isolated process and operate on control layer. The controller provides a universal view of the entire network and support applications and services. The three focused parameters for controller selection are productivity, campus network and open source. In SDN, it is vital to have a good device for the efficient processing of all requests made by the switch and for good behavior of the network. For selecting best controller for the specified parameters, decision logic has to be developed that allow us to do comparison of the available controllers. Therefore, in this research we have suggested a methodology that uses analytic-hierarchy-process (AHP) to find a best controller. The approach has been studied and verified for a big organization network setup of Al-Majmaah University, Saudi Arabia. The approach is found to be more effective and increase the network performance significantly.

Software defined networking (SDN) is a promising technology that is expected to dominate the networking market, especially wired networking. This network architecture overcomes many of the shortcomings in traditional transmission control protocol (TCP) networking by decoupling data and control planes and centralising the logic/control in the network. A successful and popular implementation of SDN is the OpenFlow protocol, which has lately been implemented by the largest Internet and networking corporations. To date, however, there is little research related to wireless networks and the integration of the OpenFlow protocol. This paper presents the results of a comprehensive survey of trials to integrate SDN technology with wireless networks. The applications of such integration are highlighted, such as performance, load balancing, and networks in rural areas.

The working approach is changing with the digitalization and advancement of technical tools, along with increased security risks. One of the most common and biggest threats that have been observed is attacks on communicational applications and networks. This threat is caused by denial of service (DoS) or distributed denial of service (DDoS). The behavior of the attacks performed through DoS and DDoS could be understood with an analysis of each step and attack process. A piece of brief information related to the attack through DoS or DDoS helps in understanding its effects, such as the development of countermeasures for the migration. Different types of networks, such as software-defined networks (SDN), virtual networks, and traditional networks, are classified in the model of DoS or DDoS. This chapter describes an analytical view of the different types of DoS or DDoS for network architecture. Mathematical modeling of the attacks through DoS or DDoS on different models, such as epidemic, hierarchical, analytical, and traffic, is described in the chapter.

The issue of routing is vitally important in Wireless Mesh Networks. But currently, most routing protocols such as OLSR and AODV cannot make the most of multiple paths between the source site and destination site because of the complexity and the cost. Software Defined Networking(SDN) structure promises to obtain the network configuration effectively, and with a centralized controller, it can deploy fine-grained routing algorithms to make full use of the network resources, while ensuring that the control overhead is acceptable. In this paper, we propose a new approach of SDN-based routing algorithm, or SDNR. We introduce link saturation to SDN, with which the SDN controller can figure out the congested path and reroute the following traffic to another non-congested path, which is the real-time optimal one, to ensure the network throughput. We compare SDNR to classic routing protocols and demonstrate its superiority.

In this paper, a new communication model to describe the controller placement problem of software-defined networks is proposed. Previous solutions to the problem only focus on the switch to controller latency, without paying attention to the latencies between controllers. In this study, both of these latencies were comprehensively studied, based on which the theory was raised. The simulation results show that the algorithm which suited the new model has better delays in most topologies.

Forwarding lookup in the open flow switch can be done for each arriving packet by every switch in path. In view of the possible resource shortage in the existing SDN technology; this paper describes a fine-grained OpenFlow multiple-table pipeline architecture that efficiently stores the flow table in the TCAM memories. Simulations show that the strategy reduces the TCAM usage for the multipletable mapping scheme [1] by 14.3%, which is important for scalable designs of OpenFlow data planes.

Software Defined Network (SDN) decouples the control plane from the data plane, yielding a vast flexibility for networks. This paper focuses on the optimal placement for heterogeneous controllers. First, SDN networks are modeled using graph theory. Therefore, the controller placement problem is transformed into a specified graph partitioning problem. Then, an optimal controller placement scheme is proposed based on multi-level graph partitioning. Finally, Simulation experiments are conducted on real network topologies. The results indicate that our scheme can achieve nearly optimal controller load balance and is of application value to wide-area SDN deployments.