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

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.
Format: Статья
Sprog:English
Udgivet: Springer Science and Business Media Deutschland GmbH 2024
Fag:
Decision Trees
Unsupervised learning
Deep learning
K-Nearest Neighbour
Reinforcement learning
Semi-supervised learning
Supervised learning
Online adgang:https://dspace.ncfu.ru/handle/123456789/29249
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Summary: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.

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