# Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms
## 5 Conclusions and Future Work  
Today's world has been likened to a global village due to the existence of the global Internet network. Humans are making new tools every day to communicate with each other faster. One of these tools is social networks, which are becoming more widespread day by day. Despite the many advantages of these networks, they also have risks for users, and one of these risks is the falsification of their names in social networks, which can cause irreparable financial and credit losses to the people in the network.  
In this paper, the methods of detecting fake accounts in social networks have been investigated. By examining these methods, it can be seen that machine learning methods have been very effective in detecting fake accounts. For this purpose, in this study, we used machine learning to detect fake accounts. To implement and extract the results, a database of real and fake Instagram accounts available to the public on the Kaggle site has been used. The effective features in detecting fake accounts were selected using Particle Swarm Optimization (PSO), Binary Grey Wolf Optimization (BGWO), and a hybrid algorithm with PSO and BGWO (BGWOPSO). They were evaluated with four classifiers, namely KNN, SVM, ANN, and LR. Finally, the hybrid optimization BGWOPSO achieved the highest accuracy of 99.1% and an AUC of 1.0. The proposed method is a high-precision method that was implemented in the MATLAB simulator.  
This research focuses on providing a high-accuracy fake account detection system, and some suggestions that can be made to complete and improve it in the future are mentioned below. As a combination method, the combination of the genetic algorithm and Ant Colony algorithm can be used for detecting fake accounts. As it was said, in the proposed method, high accuracy is preferred. In future work, the time criterion for detecting can be used to improve the identification system, and other evolutionary algorithms can also be used to test the models.  
Conceptualization, P.A. and K.P.; methodology, P.A. and K.P.; software, P.A.; validation, P.A. and K.P.; formal analysis, P.A.; investigation, P.A.; resources, P.A.; data curation, P.A.; writing--original draft preparation, P.A.; writing--review and editing, K.P.; visualization, P.A.; supervision, K.P.; project administration, K.P.; funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available in kaggle.com (accessed on 19 September 2024).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**The State-of-the-Art Literature** & **Method** & **Accuracy** & **AUC** \\ \hline [PERSON], [PERSON], and [PERSON] & Random Forest & 91.76\% & \\ \hline [PERSON] et al. [4] & Random Forest & 92.5\% & \\ \hline [PERSON] and [PERSON] [31] & Neural Network with genetic algorithm as feature selection & 96\% & \\ \hline [PERSON], [PERSON], and [PERSON] [6] & CNN & 91.5\% & \\ \hline [PERSON] et al. [7] & Random Forest & 96.94\% & \\ \hline [PERSON] [8] & Bagged Decision Tree & 98.45\% & \\ \hline [PERSON] and [PERSON] [32] & Decision Tree & 96\% & \\ \hline BGWOPSO & Artificial Neural Network & 99.1\% & 1.0 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of BGWOPSO with the state-of-the-art literature.