Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for the next-generation Internet. The deployment and applications of IoT range from smart cities to urban computing and from ubiquitous healthcare to tactile Internet. For this reason, the physical infrastructure of heterogeneous network systems has become more complicated and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks and Network Function Virtualization has been extensively researched for IoT in the recent past. In this article, we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software-defined networks designed for IoT, function virtualization for IoT networks, and software-defined IoT networks. These categories are further divided into works that present architectural, security, and management solutions. Besides, the article highlights several short-term and long-term research challenges and open issues related to the adoption of software-defined Internet of Things.

A Survey of Network Virtualization Techniques for Internet of Things Using SDN and NFV

The distributed denial-of-service (DDoS) attack is a security challenge for the software-defined network (SDN) The different limitations of the existing DDoS detection methods include the dependency on the network topology, not being able to detect all DDoS attacks, applying outdated and invalid datasets and the need for powerful and costly hardware infrastructure Applying static thresholds and their dependency on old data in previous periods reduces their flexibility for new attacks and increases the attack detection time A new method detects DDoS attacks in SDN This method consists of the three collector, entropy-based and classification sections The experimental results obtained by applying the UNB-ISCX, CTU-13 and ISOT datasets indicate that this method outperforms its counterparts in terms of accuracy in detecting DDoS attacks in SDN

The DDoS attacks detection through machine learning and statistical methods in SDN

Software defined networks is the future of Computer networks which claims that traditional networks are getting replaced by SDN. Considering the number of nodes everyday connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution as SDN to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers. Out of the existing multiple controllers providing the SDN functionalities to the network, one of the basic controllers is Ryu Controller. This paper is a contribution towards performance evaluation of scalability of the Ryu Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Ryu Controller and iPerf. Ryu Controller is tested in the simulation environment by observing throughput of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes until it supported tested on high end devices.

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Ryu controller's scalability experiment on software defined networks

The laboratory plays an important role in teaching engineering skills. An Electrical Engineering department at an Australian University implemented a reform to monitor and improve student satisfaction with the teaching laboratories. A Laboratory Manager was employed to oversee the quality of 27 courses containing instructional laboratories. Student satisfaction surveys were carried out on all relevant laboratories every year, and the data were used for continuous improvement. This paper will investigate the reforms that were implemented and outline a number of the improvements made. It also examines the program's overall impact on: 1) overall satisfaction; 2) laboratory notes; 3) learning experiences; 4) computer facilities; 5) engineering equipment; and 6) condition of the laboratory. Student satisfaction with the laboratories increased by 32% between 2007 and 2013. The results show that the laboratory notes (activity and clarity) and the quality of the equipment used are among the most influential factors on student satisfaction. In particular, it is important to have notes or resources that explain in some detail how to use and troubleshoot equipment and software used in the laboratory.

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Decoding Student Satisfaction: How to Manage and Improve the Laboratory Experience

The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score.

ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid.

Software Defined Network is the booming area of research in the domain of networking With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers Out of existing multiple controllers providing the SDN functionalities to the network, one of the stellar controllers is Floodlight Controller This paper is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Floodlight Controller and iPerf Floodlight Controller is tested in the simulation environment by observing throughput and latency parameters of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes

Experimenting with scalability of floodlight controller in software defined networks

Software-defined networks initiate the era of a programmable network by segregating the control plane and data plane. It eliminates the bottleneck of programming each device manually and brace the concept of logical centralization. The primary element of the control plane is the controller where the intelligence of the entire network is implemented. There are a variety of controllers available in the market such as RYU, ODL, FloodLight, Beacon, IRIS, ONOS, OpenMUL, Mastero, POX, NOX, Libfliud, and many more. All the controllers behave differently depending on the number of nodes, topology structure, configuration environment, type of traffic flow, etc. To explore the capabilities of the controller and to measure the performance capabilities, proper comparative performance evaluation is highly demanded. So, this paper evaluates the seven widely used controllers in the market against several performance parameters such as TCP-Throughput, UDP-throughput, jitter, latency, and the number of hosts. To carry out this evaluation, research tools such as Mininet, iperf, Gnuplot have been used. At last, in-depth discussion is carried out and results are represented in the form of graphs for better analysis and understanding. Obtained results give in-depth information about the controllers performance that which controllers perform best based on the number of switches and hosts.

A Comparative Performance Analysis of Stellar SDN Controllers using Emulators

Software Defined Network is the thriving area of research in the realm of networking With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the competence and applicability to hold up the fault tolerance of the solution offered in the form of SDN Controllers Out of the accessible multiple controllers with enabled the SDN functionalities to the network infrastructure, one of the best choice in controllers is Floodlight Controller This research article is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing dual scenarios implemented, experimented and analyzed on the emulation tool of OFNet Floodlight Controller is tested in the emulation environment by observing eight different parameters of the controller and checked its performance in scalable networking conditions over linear topology by gradually increasing the number of nodes

Scalability of software defined network on floodlight controller using OFNet

In traditional network, a developer cannot change the configuration of a router with software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to make the routers/switches programmable, a new architecture of network has to be developed and this gave rise to Software defined networks. It is the new architecture for Computer Networks in which, the old traditional architecture is slowly depreciated. It is very difficult to adapt new technology especially to decide upon which controller has to be considered and what may be its scalability to compete the dynamic circumstances of networks. Many researches are working on possible solutions and look upon SDN to overcome the traditional network limitations. There are many SDN controllers existing amongst them, some are OpenDaylight, Floodlight, Onos, Ryu, Beacon etc. From the existing multiple controllers serving the SDN services to the network, Onos is one of the Controller. ONOS can be deployed on Docker container and it is accessed using its IP as a host. In this paper, authors are contributing for the evaluation of the Performance to check the Scalability of ONOS controller by taking many scenarios which are experimented on the simulation tool of Mininet, Onos Controller, Docker and iPerf. ONOS Controller‟s simulated environments are observed for its throughput evaluated in dynamic conditions of a network over Mesh topology by gradually increasing the number of hosts until its supported by the system with optimum resource utilization.

Bhargavi Goswami

Papers

Ryu controller's scalability experiment on software defined networks

Experimenting with scalability of floodlight controller in software defined networks

A Comparative Performance Analysis of Stellar SDN Controllers using Emulators

Scalability of software defined network on floodlight controller using OFNet

Experimenting with ONOS Scalability on Software Defined Network