SDN-based gateway architecture for electromagnetic nano-networks

Galal, A. A.; Hesselbach, Xavier; Tavernier, Wouter; Colle, Didier

doi:10.1016/j.comcom.2021.12.017

Cited by 10 publications

(11 citation statements)

References 30 publications

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“…to be discussed over here [45,46]. The core charisma lies in the core architecture of SDN as it compasses two processing layers and one interface layer.…”

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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“…to be discussed over here [45,46]. The core charisma lies in the core architecture of SDN as it compasses two processing layers and one interface layer.…”

Section: Dnn Mirari Datasetmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ topology-unawareness, limited computational power, high sensitivity for emergency information, redundant data, limited storage and computational capabilities, besides data routing without storing routing tables in minimal transmission ranges [10]. In addition to the aforementioned constraints, interoperability and heterogeneity are the main features of IoNT, because exchanging different data formats between several communication protocols in diverse nano-network applications leads to a lack of cooperation and capability mismatch between devices, which affects the overall performance of the nano-network [11]. Hence, the micro/nano-gateway interacts between the nano-network domain and the traditional network domain by providing a protocol mapping mechanism that allows an intermediary data format conversion for various protocols of the nano-network domain to the traditional domain and vice versa.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the micro/nano-gateway interacts between the nano-network domain and the traditional network domain by providing a protocol mapping mechanism that allows an intermediary data format conversion for various protocols of the nano-network domain to the traditional domain and vice versa. Therefore, providing access to the nano-network from the Internet and facilitating the development of different applications [11]. The two-sided communication capability of these micro/nano-gateways causes some compatibility issues, because of different communication protocols and data formats in different domains, on top of the heterogeneity of nano-devices/sensors, i.e., each device can operate with a certain communication protocol using a specific data format within a particular application, which adds more challenges to the micro/nano-gateway functionality [12].…”

Section: Introductionmentioning

confidence: 99%

“…The two-sided communication capability of these micro/nano-gateways causes some compatibility issues, because of different communication protocols and data formats in different domains, on top of the heterogeneity of nano-devices/sensors, i.e., each device can operate with a certain communication protocol using a specific data format within a particular application, which adds more challenges to the micro/nano-gateway functionality [12]. To overcome these limitations and constraints, the micro/nano-gateway needs to allocate the main information from each packet received from nano-domain protocols and convert it to a suitable data format to enable linking the nano-network with the Internet by affording a protocol conversion mechanism to accommodate different data formats coming from various nano-sensors [11]. Moreover, a data characterization mechanism is needed to differentiate between different types of traffic, such as the high-priority and low-priority traffic coming to/from the nano-network, which results in the loss of high-priority or critical information during a high data traffic load.…”

Section: Introductionmentioning

confidence: 99%

“…However, heavy resources are required to process and analyze the data that can be done either offline or using edge computing, which also requires heavy transmission rates to provide a timely response. When it comes to the nanonetwork, in addition to its role of providing Internet access to different nano-devices, the micro/nano-gateway represents the edge of the nano-network [11]. Similar to the progression from cloud computing to cloud intelligence, a fast evolution on the edge of the nano-network from edge computing to edge intelligence or Edge AI that will provide adaptation for data-driven applications, enhance nano-network access performance and enable the deployment of quality of experience, security and privacy targets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Models for Traffic Classification in Electromagnetic Nano-Networks

Galal

Hesselbach

2022

IEEE Access

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The number of nano-sensors connected to wireless electromagnetic nano-network generates different traffic volumes that have increased dramatically, enabling various applications of the Internet of nano-things. Nano-network traffic classification is more challenging nowadays to analyze different types of flows and study the overall performance of a nano-network that connects to the Internet through micro/nanogateways. There are traditional techniques to classify traffic, such as port-based technique and load-based technique, however the most promising technique used recently is machine learning. As machine learning models have a great impact on traffic classification and network performance evaluation in general, it is difficult to declare which is the best or the most suitable model to address the analysis of large volumes of traffic collected in operational nano-networks. In this paper, we study the classification problem of nano-network traffic captured by micro/nano-gateway, and then five supervised machine learning algorithms are used to analyze and classify the nano-network traffic from traditional traffic. Experimental analysis of the proposed models is evaluated and compared to show the most adequate classifier for nano-network traffic that gives very good accuracy and performance score to other classifiers.

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Nano-sensors communications and networking for healthcare systems: Review and outlooks

Abedi,

Goh,

Alkhayyat

2024

Journal of Computational Science

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SDN-based gateway architecture for electromagnetic nano-networks

Cited by 10 publications

References 30 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

Machine Learning Models for Traffic Classification in Electromagnetic Nano-Networks

Nano-sensors communications and networking for healthcare systems: Review and outlooks

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