<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>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.</p>
</abstract>

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.

Leveraging Machine Learning for Fraudulent Social Media Profile Detection

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Leveraging Machine Learning for Fraudulent Social Media Profile Detection"}

Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This...

Leveraging Machine Learning for Fraudulent Social Media Profile Detection

Pubblicato online: 23 mar 2024

Pagine: 118 - 136

Ricevuto: 29 mag 2023

Accettato: 08 gen 2024

DOI: https://doi.org/10.2478/cait-2024-0007

Parole chiave
Dummy Classifier, SVC Classifier, Random Forest classifier, Decision Tree Classifier, MLP (MultiLayer Perceptron) Classifier

© 2024 Soorya Ramdas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Leveraging Machine Learning for Fraudulent Social Media Profile Detection

Pubblicato online: 23 mar 2024

Pagine: 118 - 136

Ricevuto: 29 mag 2023

Accettato: 08 gen 2024

DOI: https://doi.org/10.2478/cait-2024-0007

Parole chiaveDummy Classifier, SVC Classifier, Random Forest classifier, Decision Tree Classifier, MLP (MultiLayer Perceptron) Classifier

© 2024 Soorya Ramdas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Dummy Classifier, SVC Classifier, Random Forest classifier, Decision Tree Classifier, MLP (MultiLayer Perceptron) Classifier