Version 1
: Received: 3 August 2024 / Approved: 7 August 2024 / Online: 8 August 2024 (11:44:28 CEST)
How to cite:
Cárdenas-Haro, J. A.; Salem, M.; Aldaco-Gastélum, A. N.; López-Avitia, R.; Dawson, M. Enhancing Security in Social Networks through ML: Detecting and Mitigating Sybil Attacks. Preprints2024, 2024080542. https://doi.org/10.20944/preprints202408.0542.v1
Cárdenas-Haro, J. A.; Salem, M.; Aldaco-Gastélum, A. N.; López-Avitia, R.; Dawson, M. Enhancing Security in Social Networks through ML: Detecting and Mitigating Sybil Attacks. Preprints 2024, 2024080542. https://doi.org/10.20944/preprints202408.0542.v1
Cárdenas-Haro, J. A.; Salem, M.; Aldaco-Gastélum, A. N.; López-Avitia, R.; Dawson, M. Enhancing Security in Social Networks through ML: Detecting and Mitigating Sybil Attacks. Preprints2024, 2024080542. https://doi.org/10.20944/preprints202408.0542.v1
APA Style
Cárdenas-Haro, J. A., Salem, M., Aldaco-Gastélum, A. N., López-Avitia, R., & Dawson, M. (2024). Enhancing Security in Social Networks through ML: Detecting and Mitigating Sybil Attacks. Preprints. https://doi.org/10.20944/preprints202408.0542.v1
Chicago/Turabian Style
Cárdenas-Haro, J. A., Roberto López-Avitia and Maurice Dawson. 2024 "Enhancing Security in Social Networks through ML: Detecting and Mitigating Sybil Attacks" Preprints. https://doi.org/10.20944/preprints202408.0542.v1
Abstract
This study contributes to the Sybil nodes detecting algorithm in Online Social Networks (OSNs). As major communication platforms, online social networks get significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. Additional exploration of distinct reputation systems and their practical application led to the study’s discovery of machine learning algorithms, i.e., the KNN, Support Vector Machines, and Random Forest algorithms. The study details the data cleansing process for the employed dataset in its process for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to explaining and analysis of conducting experiments and comparing their results. Such experiments demonstrate the algorithm’s ability to detect Sybil nodes in OSNs (100% accuracy in SVM, 99.6% in Random Forest, and 97% in KNN algorithms); thus concluding by proposing future research opportunities.
Keywords
machine learning; sybil attacks; online social networks; cybersecurity; random forest; support vector machine; KNN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
