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.
פורמט:	Статья
שפה:	English
יצא לאור:	Springer Science and Business Media Deutschland GmbH 2024
נושאים:	Decision Trees Unsupervised learning Deep learning K-Nearest Neighbour Reinforcement learning Semi-supervised learning Supervised learning
גישה מקוונת:	https://dspace.ncfu.ru/handle/123456789/29249
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!

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סיכום:	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.

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

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