Journal Press India^®

Author Details ( * ) denotes Corresponding author

This study employs Random Forest (RF) and Deep Convolutional Neural Networks (DCNN) to predict stalking behavior on X and detect phony profiles. The source of the dataset was Kaggle. The model was developed and evaluated using the Object Oriented Analysis and Design (OOAD) methodology. Utilizing the Python computer language, the RF&DCNN algorithms were implemented. Real-time detection and prediction are provided by the algorithms, which process the input data iteratively and update the model parameters in response to fresh observations. Statuses_count, followers_count, friends_count, favorites_count, and listed_count are among the input parameters provided into the model. By including these parameters in the model, profiles can be predicted effectively and with accuracy. Based on the research, an accuracy level of 93.89% with an error rate of 6.104 was achieved. With an accuracy rate of 86.57% and an error rate of 13.43%, the proposed model outperformed the current one in terms of effectiveness. The outcomes show how well the RF and DCNN based prediction model works to identify fake profiles and predict stalking. By putting out a novel method for identifying phony profiles and forecasting stalking utilizing RF and DCNN, this study advances the field of anomaly detection operations.

Keywords

Fake profile, X, Stalking, Machine Learning, RF classifier, DCNN classifier

1. Asante, A. & Feng, X. (2021). Content-based technical solution for cyberstalking detection. 3^rd International Conference on Computer Communication and the Internet (ICCCI).
2. Balakrishnan, V., Khan, S. & Arabnia, H.R. (2020). Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Computer & Security, 90(3), 101710.
3. Beatriche, G. (2018). Detection of fake profiles in online social networks. Proceedings of the International Conference on Advances in Computing and Communication Engineering (ICACCE), Paris, France.
4. Bhosale, R. & Mane, V. (2024). Enhancing user trust: A novel hybrid model to detect fake profiles in online social networks. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 542.
5. Chakraborty, P., Shazan, M., Nahid, M., Ahmed, M. & Talukder, P. (2022). Fake profile detection using machine learning techniques. Journal of Computer and Communications, 10(10), 74-87.
6. Eastee, V.D.W. & Jan, E. (2018). Using machine learning to detect fake identities: Bots vs Humans. Retrieved from https://www.researchgate.net/publication/322650456_Using_Machine_Learning_to_Detect_Fake_Identities_Bots_vs_Humans
7. Egele, M., Stringhini, G., Stringhini, G. & Vigna, G. (2015). Towards detecting compromised accounts on social networks. IEEE Transactions on Dependable and Secure Computing 14(99). Retrieved from https://www.researchgate.net/publication/281768604_Towards_Detecting_Compromised_Accounts_on_Social_Networks
8. El-Azab, A., Idrees, A.M., Mahmoud, M. & Hefny, D.H. (2016). Fake account detection in Twitter based on minimum weighted feature set. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 10(1), 13–18.
9. Fatih, C. A. & Esat, M.K. (2019). Identification of spurious and automated records that lead to a phony Instagram joint effort. Social Network Analysis and Mining, 4(1). Retrieved from https://doi.org/10.1007/s13278-014-0194-4
10. Gayatri, N., Vaibhav, D., Kajal, D., Shraddha, G., Kulkarni, P.R. (2020). Detection of fake twitter accounts with machine learning algorithms. Retrieved from https://ijirt.org/master/publishedpaper/IJIRT150525_PAPER.pdf
11. Harish, K., Naveen, R., Kumar, J., & Briso, B. (2023). Fake profile detection using machine learning. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), 10(2), 719-725. Retrieved from https://doi.org/10.32628/ IJSRSET2310264.
12. Huang, C., Fang, Y., Yang, S., & Zhao, B. (2021). Cyberbullying detection in social networks using Bi-gru with self-attention mechanism. Information, 12(4), 171.
13. Mohammed, A. (2020). Early detection of similar fake accounts on twitter using the random forest algorithm. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(12), 611-620. Retrieved from http://iaeme.com/Home/issue/ IJARET?Volume=11&Issue=12
14. Prathyusha, T., Sai-Kumar, T.N., Vishnu, E.P., & Vijaykanth, T.R. (2021). Fake account detection using machine learning. International Journal of Creative Research Thoughts (IJCRT), 9(6), e804-e807. Retrieved from https://ijcrt.org/papers/IJCRT2106559.pdf
15. Ratkiewicz, J., Conover, M. D., Meiss, M., Gonc, B., Flammini, A., & Menczer, F. (2010). Detecting and tracking political abuse in social media. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14127
16. Reza, R. R., & Soheila, K. (2020). Detecting fake accounts on Twitter social network using multi-objective hybrid feature selection approach. Webology, 17(1), 1-18.
17. Saberi, A., Vahidi, M., & Bidgoli, B.M. (2007). Learn to detect phishing scams using learning and ensemble methods. IEEE, 311–314. Retreived from https://www.researchgate.net/publication/4310062_Learn_to_Detect_Phishing_Scams_Using_Learning_and_Ensemble_Methods
18. Saeid, S. (2020). An efficient method for detection of fake accounts on the instagram platform. Revue d’Intelligence Artificielle, 34(4), 429-436.
19. Secchiero, M. (2012). FakeBook: Detecting fake profiles in on-line social networks. IEEE. Retrieved from DOI: 10.1109/ASONAM.2012.185
20. Yazan, J. S. (2015). Thwarting fake OSN accounts by predicting their victims. Retrieved from https://dl.acm.org/doi/10.1145/2808769.2808772