International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 5, 8 Oct. 2025
Cover page and Table of Contents: PDF (size: 399KB)
An Intelligent Framework for Fraud User Identification Using Machine Learning Techniques
PDF (399KB), PP.31-41
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Author(s)
Index Terms
Support Vector Machine, Logistic Regression, Passive Aggressive, Decision Tree, Confusion Matrix
Abstract
With the rise of online platforms, concerns are increasing about the presence of fake user profiles, which can be exploited for malicious activities such as fraud, identity theft, and spreading misinformation. This study provides a detailed analysis of four machine learning algorithms to detect fake profiles: Support Vector Machine, Logistic Regression, Passive Aggressive, and Decision Tree. To train and evaluate these models, we first collect a broad dataset of both genuine and fake user profiles. Through feature engineering, relevant data such as text content, account creation details, and behavioral patterns are extracted from the profiles. Support Vector Machine is selected for its capacity to manage high-dimensional data and reduce the risk of overfitting, while Logistic Regression is valued for its interpretability and capability to model complex relationships. Passive Aggressive is included to test performance in real-time scenarios, where fake profile characteristics may evolve due to its adaptability to changing data streams. Decision Trees are employed for their ability to capture non-linear relationships and offer insights into the decision-making process. Metrics like recall, accuracy, and precision are used to evaluate the performance of each algorithm. This comparative analysis enhances our understanding of machine learning approaches for detecting fake profiles and offers practical insights for developers aiming to mitigate risks associated with online fraud. Among the algorithms, Decision Tree achieved the highest accuracy at 98.76%.
Cite This Paper
Vyankatesh Rampurkar, Thirupurasundari D. R., "An Intelligent Framework for Fraud User Identification Using Machine Learning Techniques", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.5, pp. 31-41, 2025. DOI:10.5815/ijieeb.2025.05.03
Reference
[1]B. Ersahin, O¨. Akta s, D. Kılınc¸, and C. Akyol. “Twitter fake account detection”. In: International Conference on Computer Science and Engineering (UBMK), 388–392.
[2]A. Homsi, J. Al Nemri, N. Naimat, H. A. Kareem, M. Al Fayoumi, and M. A. Snober. “Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques”. In: DATA. 2021, 88–95
[3]S. Gurajala, J. S. White, B. Hudson, and J. N. Matthews. “Fake Twitter accounts: profile characteristics obtained using an activity based pattern detection approach”. In: Proceedings of the 2015 international conference on social media and society. 2015, 1–7.
[4]G. Sansonetti, F. Gasparetti, G. D’aniello, and A. Micarelli, (2020) “Unreliable users detection in social media: Deep learning techniques for automatic detection” IEEE Access 8:368 213154–213167. DOI: 10.1109/ACCESS.2020.3040604 J
[5]F. Masood, G. Ammad, A. Almogren, A. Abbas, H. A. Khattak, I. Ud Din, M. Guizani, and M. Zuair, (2019) “Spammer Detection and Fake User Identification on Social Networks” IEEE Access 7: 68140–68152. DOI: 10.1109/ACCESS.2019.2918196.372
[6]J. Bordbar, M. Mohammadrezaie, S. Ardalan, and M. E. Shiri, (2022) “Detecting fake accounts through Generative Adversarial Network in online social media” arXiv preprint374 arXiv:2210.15657:375
[7]F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. “Detecting spammers on twitter”. In: Collaboration, electronic messaging, antiabuse and spam conference (CEAS). 6.377 2010. 2010, 12.378
[8]S. R. Sahoo and B. B. Gupta, (2020) “Fake profile detection in multimedia big data on online social networks” International Journal of Information and Computer Security 12(2 3):380 303–331
[9]L. P, S. V, V. Sasikala, J. Arunarasi, A. R. Rajini, and N. Nithiya. “Fake Profile Identification in Social Network using Machine Learning and NLP”. In: 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT). 2022, 1–4. DOI:384 10.1109/IC3IOT53935.2022.9767958.385
[10]N. S. Jogalekar, V. Attar, and G. K. Palshikar. “Rumor detection on so cial networks: a sociological approach”. In: 2020 IEEE international conference on big data (big data). IEEE.387 2020, 3877–3884.388 21
[11]Smadi, S., Aslam, N., Zhang, L.: Detection of online phishing email using dynamic evolving neural network based on reinforcement learning. Decision Support Systems 107, 88–102 (2018).
[12]Paithane, P.M., Kakarwal, S.: Automatic pancreas segmentation using a novel modified semantic deep learning bottom-up approach. International Journal of Intelligent Systems and Applications in Engineering 10(1), 98–104 (2022).
[13]Feng, F., Zhou, Q., Shen, Z., Yang, X., Han, L., Wang, J.: The application of a novel neural network in the detection of phishing websites. Journal of Ambient Intelligence and Humanized Computing, 1–15 (2018).
[14]Wagh, S.J., Paithane, P.M., Patil, S.: Applications of fuzzy logic in assessment of groundwater quality index from jafrabad taluka of marathawada region of maharashtra state: A gis based approach. In: International Conference on Hybrid Intelligent Systems, pp. 354–364 (2021). Springer.
[15]Pradip paithane, “Trust Aware Recommendation using Deep Matrix Factorization Model”, JETIA, vol. 10, no. 48, pp. 115-121, Aug.2024.
[16]Singh, Naman, et al. "Detection of fake profile in online social networks using machine learning." 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2018.
[17]Khaled, Sarah, Neamat El-Tazi, and Hoda MO Mokhtar. "Detecting fake accounts on social media." 2018 IEEE international conference on big data (big data). IEEE, 2018.
[18]Bhambulkar, Rutika, Sachin Choudhary, and Amit Pimpalkar. "Detecting fake profiles on social networks: a systematic investigation." 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2023.
[19]Tiwari, Vijay. "Analysis and detection of fake profile over social network." 2017 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2017.
[20]Romanov, Aleksei, Alexander Semenov, and Jari Veijalainen. "Revealing fake profiles in social networks by longitudinal data analysis." International Conference on Web Information Systems and Technologies. Vol. 2. SCITEPRESS, 2017.
[21]https://www.kaggle.com/datasets/whoseaspects/genuinefake-user-profile-dataset