Toward integrating software defined networks with the Internet of Things: a review

Ja’afreh, Mohammed Al; Adhami, Hikmat; Alchalabi, Alaa Eddin; Hoda, Mohamed; Saddik, Abdulmotaleb El

doi:10.1007/s10586-021-03402-4

Cited by 19 publications

(10 citation statements)

References 37 publications

(27 reference statements)

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“…Network devices such as switches and routers only become transmit devices, and all configuration, control decisions and management are completed by scalable centralized controllers, thus realizing dynamic, flexible and fine-grained management of devices and networks; achieving network openness, control flexibility, and high efficiency in operation and maintenance, and accelerating the innovation of network applications and protocols. At the same time, reducing the costs of network construction and operation and maintenance [7,8]. The universal architecture of software-defined IoV is shown in Figure 4.…”

Section: Iovmentioning

confidence: 99%

A universal architecture for software-defined Internet of Vessels

Wang,

Liu,

Zhang

et al. 2023

Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023)

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By summarizing and analyzing the main problems in the current development process of the Internet of Vessel (IoV), a universal architecture for software-defined Internet of Vessels is proposed and discussed in this paper. This architecture can meet the demands for heterogeneous interconnection, resource management, reliability and security, thus it should have a good application prospect and can satisfy multiple requirements of IoV users. Finally, taking marine rescue as an example, the advantages and prospects of software-defined IoV applications are clarified.

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Section: Iovmentioning

confidence: 99%

A universal architecture for software-defined Internet of Vessels

Wang,

Liu,

Zhang

et al. 2023

Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023)

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“…SDN has been proposed, mostly, as a solution to resolve traditional network issues and network management optimization for IoT networks [ 27 ]. Similarly, Docker is used all along with Fog and Edge nodes thanks to its advantages in terms of portability of services and applications.…”

Section: Background and Related Workmentioning

confidence: 99%

Empowering SDN-Docker Based Architecture for Internet of Things Heterogeneity

2022

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Internet of Things (IoT), an emerging technology, connects billions of smart devices to the Internet through heterogeneous communication technologies enabling novel services and applications. This complex and variable environment encounters several challenges such as managing and controlling the network and IoT applications, programming IoT devices, responding to some specific Quality of Service requirements of certain applications. The current IoT network based on the complex, static, and closed traditional architecture represents a serious concern, especially with the amounts of data generated from the huge and heterogeneous IoT devices. In this paper, we present a new model for IoT architecture that brings together the benefits of Software Defined Networking (SDN) architecture and containerization (i.e., Docker) to address most IoT challenges and abstract the underlying communication and hardware subsystem. The SDN concept decoupled the control plane from data plane which implies a centralized global network view and provides flexibility in network configuration and resource management. Moreover, containerization technique through the docker in the device brings the portability and scalability of the applications. First, we propose, an SDN-based architecture for IoT. Then, with lightweight virtualization of IoT device supplied by Docker, we empower the functionalities and effectiveness of the SDN based architecture solution to challenge the IoT heterogeneity. Furthermore, we use the smart supermarket as a use case to validate our SDN-Docker based architecture in a real IoT scenario. The experimental results validate the architecture implementation.

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“…6 Software-Defined Internet of Things (SDIoT) is an extension of SDN that integrates IoT devices with the SDN architecture, enabling network administrators to manage and control IoT networks more efficiently and securely. 4,7,8 As described in Figure 1, The architecture of SDIoT combines the IoT framework with the SDN layers, which consist of three principal planes: the Data Plane, the Control Plane, and the Application Plane. This specific infrastructure aims to improve IoT devices' and applications' performance and security.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, SDN provides a programmable and open architecture that enables the implementation of various security mechanisms, such as traffic monitoring, access control, and intrusion detection 6 . Software‐Defined Internet of Things (SDIoT) is an extension of SDN that integrates IoT devices with the SDN architecture, enabling network administrators to manage and control IoT networks more efficiently and securely 4,7,8 …”

Section: Introductionmentioning

confidence: 99%

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

Setitra

Fan

Bensalem

2023

Trans Emerging Tel Tech

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The growing popularity of Software Defined Networks (SDN) and the Internet of Things (IoT) has led to the emergence of Software Defined Internet of Things (SDIoT) based on centralized network management by the Control Plane, which can handle the dynamic nature of IoT devices and the high volume of network traffic. However, due to their specific design, SDIoTs are the ideal target for Distributed Denial of Service (DDoS) attacks, becoming one of the most destructive threats. Machine learning (ML) techniques are best suited to solve this problem due to the recent growth and sophistication of DDoS attacks. In this study, we propose an enhanced deep learning approach based on combining AutoEncoder (AE) and Extreme Gradient Boosting (XGBoost). First, we applied the SHapley Additive exPlanations (SHAP) feature selection method to select the appropriate features subset according to their correlation results. Next, the AE is trained on the previous subset to learn a compact representation of the input features. The latent representation generated by the AE is then used as input for the XGBoost model, which is trained to predict the target variable and classify the traffic as usual or attack. In parallel, Grid Search Cross Validation (GSCV) is used to find the optimal hyperparameters for the AE‐XGBoost. The experimental results using two publicly available realistic SDN‐Iot datasets demonstrate that the proposed approach enables precise identification of DDoS attacks in SDIoT networks, achieving a 99.9920% accuracy, an F1 score of 0.999917, and a low false positive rate. Furthermore, the proposed model's performance exceeds that of the models used for comparison.

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Toward integrating software defined networks with the Internet of Things: a review

Cited by 19 publications

References 37 publications

A universal architecture for software-defined Internet of Vessels

A universal architecture for software-defined Internet of Vessels

Empowering SDN-Docker Based Architecture for Internet of Things Heterogeneity

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

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