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Leveraging Machine Learning for Fraudulent Social Media Profile Detection

Cybernetics and information technologies : CIT, 2024-03, Vol.24 (1), p.118-136 [Peer Reviewed Journal]

EISSN: 1314-4081 ;DOI: 10.2478/cait-2024-0007

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  • Title:
    Leveraging Machine Learning for Fraudulent Social Media Profile Detection
  • Author: Ramdas, Soorya ; Agnes, Neenu N. T.
  • Subjects: Decision Tree Classifier ; Dummy Classifier ; MLP (MultiLayer Perceptron) Classifier ; Random Forest classifier ; SVC Classifier
  • Is Part Of: Cybernetics and information technologies : CIT, 2024-03, Vol.24 (1), p.118-136
  • Description: Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, Naïve Bayes classifier, and Logistic Regression. For a comprehensive evaluation of the performance and accuracy of different models in detecting fake social media profiles, it is essential to consider confusion matrices, sampling techniques, and various metric calculations. Additionally, incorporating extended computations such as root mean squared error, mean absolute error, mean squared error and cross-validation accuracy can further enhance the overall performance of the models.
  • Publisher: Sciendo
  • Language: English
  • Identifier: EISSN: 1314-4081
    DOI: 10.2478/cait-2024-0007
  • Source: Walter De Gruyter: Open Access Journals
    DOAJ Directory of Open Access Journals
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