Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
- Title
- Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
- Creator
- Anita E.A.M.; Jenefa J.; Vinodha D.; Lapina M.
- 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. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-863 LNNS, pp. 67-75.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Decision Trees; Deep learning; K-Nearest Neighbour; Reinforcement learning; Semi-supervised learning; Supervised learning; Unsupervised learning
- Coverage
- Anita E.A.M., Department of Computer Science and Engineering, Christ University, Bangalore, India; Jenefa J., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vinodha D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Lapina M., Department of Information Security of Automated Systems, North-Caucasus Federal University, Stavropol, Russian Federation
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303172170-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
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,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19115.