Analysis of dynamic topology wireless sensor networks for the internet of things (<scp>IoT</scp>)

R, Praveen Kumar; Raj, Jennifer S.; Smys, S.

doi:10.1002/dac.4978

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…The second kind, network-based IDS, analyzes all network traffic and identifies known and unknown threats (HIDS) using an anomaly-based and signature-based hybrid approach. Signaturebased technique uses more resources and doesn't identify attacks, only database records [12]. Anomaly-based NIDS is better at monitoring…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Methods of IoT Security and Future Application

ALI

NAEEM

Anam

et al. 2022

PPASA

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One of the technologies that are now expanding rapidly is called the Internet of Things (IoT). It is a technology that enables billions of smart devices or things, collectively referred to as “Things,” to collect a variety of data about themselves and the environment in which they are located using a variety of sensors. They can then share data with parties who have been permitted to do so for a variety of objectives, such as the management and monitoring of industrial services or the expansion of company services or operations. However, there are presently more security risks associated with the Internet of Things than ever. The field of machine learning (ML) has recently experienced significant advancement in technology, which has resulted in the opening of various new lines of inquiry that may be used to address existing and upcoming issues related to the Internet of Things. Nevertheless, machine learning is a robust technology that can recognize suspicious dangers and activities in smart devices and grids. The authors of this paper conducted an extensive literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. After that, they compared several different ML algorithms regarding the detection of attacks and anomalies. Additionally, many machines learning-based Internet of Things protection systems have been presented.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Methods of IoT Security and Future Application

ALI

NAEEM

Anam

et al. 2022

PPASA

View full text Add to dashboard Cite

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Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Praveenkumar¹,

Kirthika²,

Arumugam³

et al. 2023

IJEER

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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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Analysis of dynamic topology wireless sensor networks for the internet of things (IoT)

Cited by 2 publications

References 26 publications

Machine Learning Methods of IoT Security and Future Application

Machine Learning Methods of IoT Security and Future Application

Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

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