# Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms
###### Abstract  
In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWPSO), improved the performance in accurately detecting fake accounts on Instagram. fake accounts; Binary Grey Wolf Optimization; Particle Swarm Optimization; Support Vector Machine; Logistic Regression; K-Nearest Neighbor; Artificial Neural Network +
Footnote †: journal: _Algorithms_  
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Researchers have been at the forefront in proposing different techniques to detect fake accounts on social networks, thereby increasing interest in this area of research. These techniques include the use of feature selection and machine learning algorithms. Fake account creators are able to circumvent the defensive techniques on platforms to mislead unsuspecting users. There is a need to constantly monitor and devise stronger techniques to detect fake accounts as the attackers become more skilled at deception. The main concerns in the diagnosis process are the accuracy and response time of feature analysis. Accounts are generally identified by the user's picture, a username, and a brief biography of the user on Instagram. Instagram does not provide any method to validate the real identity of a user based on their profile. The lack of protective tools on these social media accounts has provided the ground for malicious actions by malicious people. Fake accounts on social media are generally identified based on the user profiles and by detecting unusual activity such as a sudden surge in the number of followers [2]. At first glance, it is difficult to prevent such scenarios, however, machine learning provides the techniques and the tools to detect fake accounts. Before machine learning can be applied to datasets, it is almost always necessary to preprocess the data into a form that can provide optimal results, and the most successful method is to select the right features in the dataset that are effective in predicting the target variable. Not all features are important in predicting the target variable, and some features may increase the computation speed in the prediction process and as a result, might be detrimental in accurately predicting the outcome. The raw data must be filtered by eliminating the features that are not helpful in predicting the target variable through selection of the right features [3; 4]. Selecting the right features is not only important but requires different techniques, some of which can be expensive computationally. One of the techniques to select the features is through different metaheuristics to find an optimal solution, here used for finding the right set of features. Some of the metaheuristic algorithms include Grey Wolf Optimization (GWO) [5; 6], genetic algorithms (GAs), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) [7; 8], and Differential Evolution (DE) [9]. We have applied and tested GWO and PSO algorithms to find the best features to be input into a machine learning model to obtain the most accurate prediction of fake accounts. The PSO algorithm is inspired by the behavior of particles in a system where they tend to accumulate in concentrated locations [10].