Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are used in various healthcare and military surveillance applications. As more sensitive data is transmitted across the network, achieving security becomes critical. Ensuring security is also challenging because most sensors are deployed in remote areas, making them v...

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Главные авторы:	Lapina, M. A., Лапина, М. А.
Формат:	Статья
Язык:	English
Опубликовано:	Springer Science and Business Media Deutschland GmbH 2024
Темы:	Decision Trees Unsupervised learning Deep learning K-Nearest Neighbour Reinforcement learning Semi-supervised learning Supervised learning
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/29249
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spelling	ir-123456789-292492024-11-27T11:45:36Z Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks Lapina, M. A. Лапина, М. А. Decision Trees Unsupervised learning Deep learning K-Nearest Neighbour Reinforcement learning Semi-supervised learning Supervised learning Wireless Sensor Networks (WSNs) are used in various healthcare and military surveillance applications. As more sensitive data is transmitted across the network, achieving security becomes critical. Ensuring security is also challenging because most sensors are deployed in remote areas, making them vulnerable to many security attacks. Sybil attacks are one of the most destructive attacks. Security against Sybil attackers can be attained by implementing effective detection techniques to distinguish attackers from genuine nodes. This paper reviews existing machine learning-based approaches for detecting Sybil attacks, and their performance is compared based on different parameters. 2024-11-27T11:45:00Z 2024-11-27T11:45:00Z 2024 Статья Anita E.A.M., Jenefa J., Vinodha D., Lapina M. Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks // Lecture Notes in Networks and Systems. - 2024. - 863 LNNS. - pp. 67 - 75. - DOI: 10.1007/978-3-031-72171-7_7 https://dspace.ncfu.ru/handle/123456789/29249 en Lecture Notes in Networks and Systems application/pdf Springer Science and Business Media Deutschland GmbH
institution	СКФУ
collection	Репозиторий
language	English
topic	Decision Trees Unsupervised learning Deep learning K-Nearest Neighbour Reinforcement learning Semi-supervised learning Supervised learning
spellingShingle	Decision Trees Unsupervised learning Deep learning K-Nearest Neighbour Reinforcement learning Semi-supervised learning Supervised learning Lapina, M. A. Лапина, М. А. Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
description	Wireless Sensor Networks (WSNs) are used in various healthcare and military surveillance applications. As more sensitive data is transmitted across the network, achieving security becomes critical. Ensuring security is also challenging because most sensors are deployed in remote areas, making them vulnerable to many security attacks. Sybil attacks are one of the most destructive attacks. Security against Sybil attackers can be attained by implementing effective detection techniques to distinguish attackers from genuine nodes. This paper reviews existing machine learning-based approaches for detecting Sybil attacks, and their performance is compared based on different parameters.
format	Статья
author	Lapina, M. A. Лапина, М. А.
author_facet	Lapina, M. A. Лапина, М. А.
author_sort	Lapina, M. A.
title	Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
title_short	Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
title_full	Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
title_fullStr	Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
title_full_unstemmed	Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
title_sort	advancements in sybil attack detection: a comprehensive survey of machine learning-based approaches in wireless sensor networks
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2024
url	https://dspace.ncfu.ru/handle/123456789/29249
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Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks

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