Machine Learning based Robust Techniques to Detect DDoS Attacks in WSN

Wireless sensor networks (WSN) keep developing in recent days concerning the self-covered network, self-healing network, and association of system component circuit selections that enable the implementation process. Wireless sensor network lifetime stabilization is essential to providing a higher quality experience to consumers. The wireless sensor network is associated with classifiers that keep learning the data pattern and further modify the cluster selection to produce dynamic results. The presented system is focused on creating an efficient wireless sensor network in which cluster head selection is dynamically programmed to improve the life span of the devices. The energy utilized by each node is pre-programmed with random assignments. The values are configured by the machine learning techniques to improve the hop death. The models developed using the parameters help project energy consumption. The proposed system considers a support vector machine (SVM), and the Gaussian regression process (GRP) enabled the comparative study of lifespan analysis and support systems to make cluster selection efficient. The proposed model is used to test the current selection of cluster heads using a support rectangle machine and further modify the regression process using the Gaussian regression model. Evaluation of network lifetime and flexibility obtained in cluster selection.

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“…Various existing frameworks are considered here for the purpose of study of wireless sensor network and dynamic routing protocols [11]- [27].…”

Section: Y Zhou Et Al (2020)mentioning

confidence: 99%

Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Praveenkumar¹,

Kirthika²,

Arumugam³

et al. 2023

IJEER

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“…In addition, Chandan et al [9] used SVM to classify the soil type beneath the Indian ground surface. Clay, clayey peat, clayey sand, humus clay, peat, sandy clay, and silty sand were among the several kinds of soil.…”

Section: Literature Reviewmentioning

confidence: 99%

Sand fineness modulus prediction in construction sector using convolutional neural network

Fahad,

Nayem,

Hossain

et al. 2023

Asian J Civ Eng

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BackgroundThe construction industry relies heavily on the sand. In construction, the neness modulus of sand is an important parameter. It impacts the relative proportions in the mix, the workability, the economy, the porosity, and the strength of the concrete. This standard speci es that sand's neness modulus should not be less than 2.3 and not more than 3.1. Sand's neness modulus refers to the mean size of its particles. Methods/Analysis/Findings:There is a remarkable success in predicting various fruits images, grains, vegetables, and soils using convolutional neural networks. Convolutional neural networks (CNN) are introduced here as a deep learning-based approach to sand neness modulus value prediction. The CNN algorithm extracts automatic features, so this research was conducted on the latest CNN architecture, ResNet-50. Currently, the sand (FM) is calculated based on laboratory sieve analysis, which is an accurate but time-consuming process. For that, the instance method is necessary to determine the sand's neness modulus. We have proposed a novel image-based model to predict sand FM values. However, sand the neness modulus (FM) can be quickly determined using images, but with low accuracy. Applications/Improvements:In experiments using our proposed method, we achieved 94.6% accuracy. There is also evidence that the proposed image-based system performs better on each of the ve standard assessment metrics, including accuracy, precision, recall, speci city, and the F-score, when predicting Fineness Modulus (FM) values.

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Sand Fineness Modulus Prediction in Construction Sector Using Convolutional Neural Network

FAHAD,

NAYEM,

HOSSAIN

et al. 2023

Preprint

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Background The construction industry relies heavily on the sand. In construction, the fineness modulus of sand is an important parameter. It impacts the relative proportions in the mix, the workability, the economy, the porosity, and the strength of the concrete. This standard specifies that sand's fineness modulus should not be less than 2.3 and not more than 3.1. Sand's fineness modulus refers to the mean size of its particles. Methods/Analysis/Findings: There is a remarkable success in predicting various fruits images, grains, vegetables, and soils using convolutional neural networks. Convolutional neural networks (CNN) are introduced here as a deep learning-based approach to sand fineness modulus value prediction. The CNN algorithm extracts automatic features, so this research was conducted on the latest CNN architecture, ResNet-50. Currently, the sand (FM) is calculated based on laboratory sieve analysis, which is an accurate but time-consuming process. For that, the instance method is necessary to determine the sand's fineness modulus. We have proposed a novel image-based model to predict sand FM values. However, sand the fineness modulus (FM) can be quickly determined using images, but with low accuracy. Applications/Improvements: In experiments using our proposed method, we achieved 94.6% accuracy. There is also evidence that the proposed image-based system performs better on each of the five standard assessment metrics, including accuracy, precision, recall, specificity, and the F-score, when predicting Fineness Modulus (FM) values.

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Machine Learning based Robust Techniques to Detect DDoS Attacks in WSN

Cited by 3 publications

References 8 publications

Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Sand fineness modulus prediction in construction sector using convolutional neural network

Sand Fineness Modulus Prediction in Construction Sector Using Convolutional Neural Network

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