Version 1
: Received: 5 January 2024 / Approved: 5 January 2024 / Online: 5 January 2024 (15:17:49 CET)
How to cite:
Ashraf, H.; Nawaz, M.; Jhanjhi, N. ML-Based Detection of Sybil Attack on MANETS. Preprints2024, 2024010508. https://doi.org/10.20944/preprints202401.0508.v1
Ashraf, H.; Nawaz, M.; Jhanjhi, N. ML-Based Detection of Sybil Attack on MANETS. Preprints 2024, 2024010508. https://doi.org/10.20944/preprints202401.0508.v1
Ashraf, H.; Nawaz, M.; Jhanjhi, N. ML-Based Detection of Sybil Attack on MANETS. Preprints2024, 2024010508. https://doi.org/10.20944/preprints202401.0508.v1
APA Style
Ashraf, H., Nawaz, M., & Jhanjhi, N. (2024). ML-Based Detection of Sybil Attack on MANETS. Preprints. https://doi.org/10.20944/preprints202401.0508.v1
Chicago/Turabian Style
Ashraf, H., Mamona Nawaz and NZ Jhanjhi. 2024 "ML-Based Detection of Sybil Attack on MANETS" Preprints. https://doi.org/10.20944/preprints202401.0508.v1
Abstract
Mobile Ad hoc Networks (MANETs) play an important role in the technological era by enabling wireless communication in dynamic and infrastructure-free contexts. Because of Manet’s unique qualities and decentralized nature, MANETs are subject to many attacks such as the Blackhole attack, grey hole attack, Byzantine attack, Wormhole attack, and Sybil attack. Sybil attacks pose a substantial risk to MANETs. A Sybil attack occurs when a malicious node impersonates many identities inside the network, resulting in the creation of multiple suspicious nodes that appear as independent entities. This study presents a thorough analysis of the literature on the detection of Sybil attacks on MANETs. To find gaps in the literature, current surveys are also looked at. Also taken into consideration are several contemporary systems built on distinct techniques. In terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end latency, some recent methods-based approaches are also evaluated severely. To address the stated state-of-the-art issues in Sybil attack detection, this paper suggests ML-based strategies as the most suitable answer. Machine learning has huge potential for effective wireless network management. According to the researcher, this is the first comprehensive assessment of ML-based techniques for Sybil attack detection in wireless networks. Finally, we considered the unsolved issues in Sybil assault detection in wireless networks.
Keywords
Sybil Attacks; MANETs; Detection Techniques
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter:
The commenter has declared there is no conflict of interests.
Commenter:
The commenter has declared there is no conflict of interests.
Commenter:
The commenter has declared there is no conflict of interests.