2019
DOI: 10.1093/comjnl/bxz135
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Monarch-EWA: Monarch-Earthworm-Based Secure Routing Protocol in IoT

Abstract: Routing in the Internet of Things (IoT) renders the protection against various network attacks as any attacker intrudes the routing mechanism for establishing the destructive mechanisms against the network, which insists the essentiality of the security protocols in IoT. Thus, the paper proposes a secure protocol based on an optimization algorithm, Monarch-Earthworm Algorithm (Monarch-EWA), which is the modification of the Monarch Butterfly algorithm using the Earthworm Optimization Algorithm (EWA) in order to… Show more

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Cited by 25 publications
(6 citation statements)
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“…The various algorithms, such as Deer Hunting Optimization Algorithm (DHOA) + DeepCNN, 36,37 SSOA + DeepCNN, 36,38 SCA + DeepCNN, 34,36 and SFOA‐DeepCNN 33,36 are utilized for the comparative analysis.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…The various algorithms, such as Deer Hunting Optimization Algorithm (DHOA) + DeepCNN, 36,37 SSOA + DeepCNN, 36,38 SCA + DeepCNN, 34,36 and SFOA‐DeepCNN 33,36 are utilized for the comparative analysis.…”
Section: Resultsmentioning
confidence: 99%
“…The various algorithms, such as Deer Hunting Optimization Algorithm (DHOA) + DeepCNN, 36,37 SSOA + DeepCNN, 36,38 SCA + DeepCNN, 34,36 and SFOA-DeepCNN 33,36 are utilized for the comparative analysis. Hence, the improved percentage of SFCA + DeepCNN with the existing methods is 5.65%, 3.84%, 2.60%, and 1.17%.…”
Section: Algorithmic Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…Initially, an input text sentence is acquired from the dataset 22 , which is then fed to the first part, the ID-Net, for the multi-classification of infectious diseases. This ID-Net is the new DL architecture based on the integration of CNN 23,24 and Bi-LSTM 25 , where text input is given to the tokenization layer. The obtained tokens are then fed to CNN, where character-level features are acquired.…”
Section: Methodsmentioning
confidence: 99%
“…The architecture of Deep CNN [49,50] composes of three layers, namely, convolutional, pooling, and fully connected layers. The functioning of each layer is the extraction of features, subsampling, and classification.…”
Section: Architecture Of Deep Cnnmentioning
confidence: 99%