Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms....

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Bibliographic Details
Published in:Algorithms 2024-10, Vol.17 (10), p.442
Main Authors: Cárdenas-Haro, José Antonio, Salem, Mohamed, Aldaco-Gastélum, Abraham N., López-Avitia, Roberto, Dawson, Maurice
Format: Article
Language:English
Subjects:
Algorithms
Cellular telephones
Communication
Customer feedback
Cybersecurity
Data mining
Data security
Datasets
Internet
Literature reviews
Machine learning
Methods
Online social networks
Product reviews
random forest
Research methodology
Safety and security measures
Sensors
Social networks
support vector machine
Support vector machines
Sybil attacks
User behavior
Citations: Items that this one cites
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Summary:This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities.
ISSN:1999-4893
1999-4893
DOI:10.3390/a17100442