A three-tier SDN architecture for DenseNets

The demand for real-time data in a modern battlefield is increasing exponentially. As a result, current military network paradigms and architectures are struggling to cope with increasing capacity demands at the tactical edge. Unfortunately, rescaling existing network architectures will not be a permanent solution to the problem. Therefore, in this paper, we propose a 3- tiered Software Defined Networking (SDN) architecture consisting of heterogeneous Dense Networks (DenseNets), which is a highly condensed deployment of relatively small, lowpowered femtocells, at the tactical edge. The proposed 3-tiered SDN architecture abstracts the Land Tactical Network (LTN) as the Physical Layer, the Battlefield Tactical Network (BTN) as the Control Layer, and the Joint Task Force Headquarters (JTFHQ) as the Management Layer. The simulation and results confirm the architecture’s capability for providing interoperability between heterogeneous communications equipment and supporting high capacity demands within limited and unreliable networking infrastructure in battlefield environments.

The software-defined network (SDNs) architecture can easily be attacked by a malicious user in order to prevent an acceptable level of service. Therefore, SDN security is a hot research topic to improve the SDN architecture and to protect the service level of the SDN components. In this letter, the INSPECTOR is a hardware device added to the SDN architecture to protect a compromised controller from a packet injection attack by verifying the authentication of Packet-In Messages accessing network resources. With simulations, we show that the INSPECTOR device efficiently stops the attack and enhances the controller performance under malicious attack.

Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of upmu text {s} in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language.Graphic

Purpose The purpose of the study concludes detecting address resolution protocol (ARP) Spoofing attack in software-defined network (SDN) architecture meanwhile using different machine learning models to evaluate their effectiveness. Design/methodology/approach This research originates from building a SDN topology and researching into its changes under ARP Spoofing attack. Based on that, the authors propose four features which show obvious abnormalities in network under attack stage. The data collected from SDN controller is used to build a data set, which is then put into different machine learning models, which are: Artificial Neuron network (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN-LSTM and Gated Recurrent Unit (GRU). Findings After applying this proposal in simulation and experimental environments, they achieve impressive performance metrics. In simulation environment, the GRU model stands out with the highest accuracy of 98.94%. In real environments, the CNN-LSTM model leads with a recall of 98.38% and an F1-Score of 98.57%, while the LSTM model has the highest precision (98.8%). The GRU model also performs strongly in real scenarios with a high accuracy of 97.65%. ANN, despite its reliability, struggles with lower recall and F1-Score across both environments. Originality/value This analysis emphasizes the importance of the proposed features when applied to different models and their high potential to conduct in practical environment.

The proliferation of mobile demands and increasingly multifarious services pose challenges to both the radio access networks (RANs) and the core network (CN). Unfortunately, the traditional networks can hardly meet the increasing requirements. Software-defined network (SDN) and network virtualization (NV) are considered as innovative paradigms to stress these problems. By extending SDN to wireless networks, software-defined wireless network (SDWN) consisting of both RANs and CN can be obtained. In this paper, the RANs and CN of SDWN are virtualized to virtual access points (VAPs) and network slices respectively based on NV technology. Considering the system scenario that a SDWN consists of overlapping RANs, an optimal joint VAP and network slice selection scheme is proposed. To characterize the complicated transmission performance of VAPs and network slices, the concepts of utility function and network calculus are introduced, and the joint utility of VAPs and network slices is formulated based on which the optimal VAP and network slice combination corresponding to the maximal joint utility can be selected. The simulation results demonstrate the efficiency of the proposed scheme.

Software Defined Network (SDN) Technology made tremendous changes in networking field by controlling the hardware with remote software by decoupling data plane and control plane. The focus here is on the controller which is part of control plane. A controller is a central part of SDN; it is an intermediary between Data plane and Forwarding plane. It takes prime place in SDN architecture like Operating System in Computers. SDN Controller is a software which directs the packets using flow rules and also contains statistical information of network. The controller orchestrates the networking devices such as switches and routers by channelizing the packets. SDN controller has different flavor based on the commercial requirement achieved by including new pluggable module. This paper discusses some of the SDN Controller with their feature; the controllers are POX, NOX, Floodlight, ODL, Open Contrail. This paper also compares the characteristic of centralized and distributed controller by focusing on performance, bandwidth and round-trip time of Floodlight and Open Daylight (ODL) controller. The simulation is conducted using Mininet.

The flexibility and programmability offered by Software Defined Networking (SDN) emerge as an approach to resolve some of the main challenges that the Internet of Things (IoT) deployments expose. In the present article, we explore the application of SDN components onto IoT deployments, together with the decoupling of typical gateway functions in two realms; the physical one and the virtual at the edge. This disengagement is a novel approach necessary since the computation resources on the physical part of the infrastructure are limited. Thus, the more resource-consuming processes are transferred to the edge of the network, in the virtual counterpart of the gateway. Additionally, within this infrastructure, different network applications can be running at the top of the SDN controller, in the control plane, for example, QoS policies application, management of network parameters, etc. Providing a holistic view of the IoT deployment. Between its advantages, this work focus on the ease of creation of an IoT infrastructure with the gateway and SDN architecture, and the behaviour of the IoT system. For validation, a small setup scenario and a use case is created where heterogeneous IoT sensors communicate with the physical part of the gateway, implemented with a Raspberry Pis and this, with its virtual part at the edge of the network, so finally sent the data to an IoT platform (FIWARE-ORION) at the core network managed by a virtual Switch (OpenVSwitch) and a RYU-based SDN controller.

The main problems of current cellular networks illustrating high delay and traffic congestion relate to their network inflexibility and incorporated control and data planes. However, the prerequisites of 5G cellular systems are low delay, high throughput, low congestion at the core cellular networks, and seamless mobility and fast handover. To alleviate aforementioned problems we propose a 3-Tier Software-Defined Networking (SDN) architecture combined with a novel OpenFlow (OF) switch, which includes a User Rate-Perceived (URP) protocol and a Macrocell/Femtocell Information Base (MFIB). Our simulations prove that proposed mechanisms bring numerous benefits such as low delay, high throughput, low cost, and seamless mobility and fast handover to 5G cellular networks and high Dense Networks, which are called DenseNets.

Software Defined Networking (SDN) is a dynamic architecture that employs a variety of applications for making networks more adaptable and centrally controlled. It is easy to attack the entire network in SDN because the control plane and data plane are separated. DDoS attack is major danger to SDN service providers because it can shut down the entire network and stop services to all customers at any time. One of the key flaws of most SDN architectures is lack of susceptibility to DDoS attacks with its types like TCP flooding, UDP flooding, SYN flooding, ICMP flooding and DHCP flooding for detecting those kinds of attacks. The machine learning algorithms are widely used in recent years to identify DDoS attacks. This research utilizes Decision Tree Classifier for detection and classification of DDoS attacks on SDN. The Forward Feature Selection technique is also used in the research to select the best features from the dataset and from that dataset the data are employed to train and test the model by Decision Tree Classifier Algorithm. The decision Tree Classifier technique is a supervised method used to forecast desired values of observations using rudimentary machine learning decision rules derived from training data. Based on the accuracy of decision tree techniques, in future, a hybrid learning model will be designed for detecting the Distributed Denial of Services in an SDN environment with high accuracy and a low false negative rate.

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach.

The Internet has reached a breaking point, enabling ubiquitous accessibility to all digital demands. Though people have accelerated their use of overall digital demands due to the convenience of the Internet, where nearly everything is connected and accessible, it is becoming increasingly difficult to manage and modify networks. It is always a challenge to meet the emerging needs of network industry. SDN framework was proposed to overcome traditional network architecture (TNA) restrictions in order to cope with contemporary network management in terms of flexibility, agility, responding to faults, and dynamic changes. SDN assists network operators by uncoupling the control plane from its underlying ternary content-addressable memory (TCAM) hardware often called as forwarding plane by allowing the networks to programme as per the anticipated network functionality. Now, switching from traditional to software-defined networking styles, it is worth to check the robustness of the open flow (south bound interface)-based controller platform and the capability of its fault tolerance attribute to approach the successful SDN implementation. The performance of SDN open flow controllers plays a vital role in SDN implementation, which is meant for the separation between control and data plane to leverage the life of network industry. This paper comprehensively surveys the contrast between traditional network architecture (TNA) and software-defined network architecture. This research work discusses about the best feasible SDN implementation, as well as the selection of a useful controller platform for overcoming traditional network constraints and the performance assessment criteria for SDN open flow controllers. Further, the survey is continued with possible literature or work done by various researchers on performance analysis and evaluation of open flow-based SDN controllers as well as in a group of two or more controllers with respect to multifarious network condition, network criteria/parameter/metric, scaling of network load resources with most important varying network topology design. Ultimately, it strives to point ongoing research omission with respect to controller’s performance bottleneck benchmark suggesting optimized solution when it is well experimented.KeywordsTraditional network architecture (TNA)Software-defined networking (SDN)SDN open flow controllersOpen flow protocolPerformance evaluation/analysis

Software Defined Networking (SDN) has come up as a promising technology for making the communication networks more flexible and dynamically (re)configurable. With the advent of fifth generation (5G) of mobile networks, the aforementioned flexibility became more urgent and critical than ever. SDN offers the decoupling of the network's control plane from the data plane. As a result, the infrastructure layer can be abstracted from the application and network services and therefore the network control can be done completely through a centralized component/orchestrator. In this work, a detailed practical deployment of an SDN controller for a converged FiberWireless (Fi-Wi) 5G network is presented, followed by the SDN architecture and functional components. Lastly, an experimental trial using an SDN controller directly connected to Fi-Wi lab equipment is described, in which the physical layer parameters are managed and monitored successfully, proving the applicability of SDN in a Fi-Wi network.

The software defined networks (SDN) is a new network paradigm that supports logical central control. For the failure recovery in SDN, most existing research mainly addressed single failure scenario or single flow rerouting. However, considering multi-failure scenario and multi-flows rerouting is closer to the real SDN environment. Since SDN architecture has a communication bottleneck between the controller and switches, flow rule plays an importance role in their communication. Thus, in this paper, we concentrate on minimizing flow rules for rerouting multi-flows when network occurs multiple link failures, named RMFWMF. We first propose a model to metric the communication overhead between controller and switch during flow rerouting. Next, we formulate the problem as a 0-1 nonlinear programming model, show it can be transformed into a 0-1 linear programming and solve the model using decomposition based on Lagrange relaxation. At last, we implement an experiment in a test network. The result shows that our solution can avoid link congestion in the post-recovery network and can provide the minimum flow rules compared with the pervious algorithm.

Software defined network (SDN) is an emerging network architecture where control plane is decoupled from forwarding and is directly programmable. SDN has been studied by academic for several years and some prototype have been developed and tested by industry. Here we firstly analyze SDN's architecture in an operator's view. Then we discuss SDN applications for telecom networks and its influence for the future.

The software-defined networking (SDN) paradigm proposes the decoupling of control and data planes and a centralized software-oriented management approach based on a central controller, easing the development of new applications and services. These design principles pave the way for a more flexible, fast, and dynamic software-controlled network. However, in terms of security, the elements that comprise the SDN architecture present several vulnerabilities, which could be exploited by attackers to carry out malicious actions and thus affect the network and its services. Although for several years, some studies have already focused on identifying the weaknesses of the SDN layer structure, the nature of the attacks, and possible solutions for this paradigm, the literature contains few contributions that review and discuss this topic in an integral fashion. This paper provides a comprehensive, updated, and detailed review of the main security issues and mitigating measures for all layers and interfaces of the SDN architecture, classifying the contributions according to the STRIDE threat modeling methodology categories. Finally, this manuscript identifies, discusses, and synthesizes open challenges and future research directions in this area.