Data-Prediction Model Based on Stepwise Data Regression Method in Wireless Sensor Network

Jain, Khushboo; Singh, Akansha

doi:10.1007/s11277-022-10034-3

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…The stepwise selection method is used to construct a linear model of the training and test set by using stepwise regression to add or remove predictors to or from the constant models, while requiring knowledge of the system response to the variable. While it is true that larger models have less error in determining the output variable, on the other hand, their prediction ability is reduced [13][14][15].…”

Section: Reduction Of Input Parameters Using the Stepwise Selection M...mentioning

confidence: 99%

Detection of Malicious Network Activity by Artificial Neural Network

Turčaník

Barath

2023

AiMT

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This paper presents a deep learning approach to detect malicious communication in a computer network. The intercepted communication is transformed into behavioral feature vectors that are reduced (using principal component analysis and stepwise selection methods) and normalized to create training and test sets. A feed-forward artificial neural network is then used as a classifier to determine the type of malicious communication. Three training algorithms were used to train the neural network: the Levenberg-Marquardt algorithm, Bayesian regularization, and the scaled conjugate gradient backpropagation algorithm. The proposed artificial neural network topology after reducing the size of the training and test sets achieves a correct classification probability of 81.5 % for each type of malicious communication and of 99.6 % (and better) for normal communication.

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Section: Reduction Of Input Parameters Using the Stepwise Selection M...mentioning

confidence: 99%

Detection of Malicious Network Activity by Artificial Neural Network

Turčaník

Barath

2023

AiMT

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An Energy Saving Strategy of WSNs Based on Data Sensing and Similarity

Zhang

Zhou

Wang

2023

Wireless Pers Commun

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A novel method for the dynamics of worms in wireless sensor networks with fuzzy partition

Alsaadi,

Dayan,

Ahmed

et al. 2023

AIP Advances

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Wireless sensor networks (WSNs) have gained much interest due to their enormous potential in civil and military applications. The power and radio communication capabilities of the sensor nodes are limited. Because sensor nodes have limited resources, they have weak defense capabilities and are attractive targets for software attacks. Worm-based cyberattacks are among the most significant threats to computers and WSNs’ security and integrity. In this article, a five-compartmental WSN epidemic model is considered. We conducted an investigation into equilibrium analysis and the reproductive number, followed by the development of a nonstandard finite difference numerical scheme for our model. The outcomes of our numerical simulations are then presented. This method yields reliable predictions, which can be valuable for regulators when making decisions related to designing and implementing control strategies. Furthermore, some interesting properties of the developed scheme are investigated, such as positivity, convergence, and consistency. The developed scheme preserves the essential characteristics of disease epidemic models like positivity, convergence, and consistency.

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Data-Prediction Model Based on Stepwise Data Regression Method in Wireless Sensor Network

Cited by 5 publications

References 35 publications

Detection of Malicious Network Activity by Artificial Neural Network

Detection of Malicious Network Activity by Artificial Neural Network

An Energy Saving Strategy of WSNs Based on Data Sensing and Similarity

A novel method for the dynamics of worms in wireless sensor networks with fuzzy partition

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