Architecture and Deployment Models-SDN Protocols, APIs, and Layers, Applications and Implementations

Rudra, Bhawana; Thanmayee, S

doi:10.1007/978-3-030-89328-6_4

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…The core charisma lies in the core architecture of SDN as it compasses two processing layers and one interface layer. The interface layer is only responsible to implement, and reflecting the decisions made by the processing layers [47]. However, processing layers included the control plane and data plane, that actively participate in the decision making as well as facilitate other integrated technologies.…”

Section: Dnn Mirari Datasetmentioning

confidence: 99%

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

et al. 2022

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Internet of Things (IoT) is an instantly exacerbated communication technology that is manifesting miraculous effectuation to revolutionize conventional means of network communication. The applications of IoT are compendiously encompassing our prevalent lifestyle and the integration of IoT with other technologies makes this application spectrum even more latitudinous. However, this admissibility also introduces IoT with a pervasive array of imperative security hazards that demands noteworthy solutions to be swamped. In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. Our proposed model (DNNLSTM) is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats in IoT communications. The proposed model is trained on CICIDS 2018 dataset, and its performance is evaluated on several decisive parameters i.e Accuracy, Precision, Recall, and F1-Score. Furthermore, the designed framework is analytically compared with relevant classifiers, i.e., DNNGRU, and BLSTM for appropriate validation. An exhaustive performance comparison is also conducted between the proposed system and a few preeminent solutions from the literature. The proposed design has circumvented the existing literature with unprecedented performance repercussions such as 99.55% accuracy, 99.36% precision, 99.44% recall, and 99.42% F1-score.

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Section: Dnn Mirari Datasetmentioning

confidence: 99%

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

et al. 2022

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“…1, one or more SDN controllers can control and manage resource management mechanisms in the whole network from RANs to CN. In SDN, the controller manages packet forwarding through programmable interfaces [22], [23], [24], [25]. Floodlight, OpenDayLight (ODL), Onix, and NOX are the most well-known SDN controllers.…”

Section: A Software-defined Networking (Sdn)mentioning

confidence: 99%

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

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One of the key foundations of 5 th Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.

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Difficulties arising due to software definednetworks against DDoS attacks

Jaber¹,

Al-Hawawreh²

2023

Advances in Intelligent Applications and Innovative Approach

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Architecture and Deployment Models-SDN Protocols, APIs, and Layers, Applications and Implementations

Cited by 3 publications

References 31 publications

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

Difficulties arising due to software definednetworks against DDoS attacks

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