Machine learning for detecting fake accounts and genetic algorithm-based feature selection

People rely extensively on online social networks (OSNs) in Africa, which aroused cyber attackers' attention for various nefarious actions. This global trend has not spared African online communities, where the proliferation of OSNs has provided new opportunities and challenges. In Africa, as in many other regions, a burgeoning black-market industry has emerged, specializing in the creation and sale of fake accounts to serve various purposes, both malicious and deceptive. This paper aims to build a set of machine -learning models through feature selection algorithms to predict the fake account, increase performance, and reduce costs. The suggested approach is based on input data made up of features that describe the profiles being investigated. Our findings offer a thorough comparison of various algorithms. Furthermore, compared to machine learning without feature selection and Boruta, machine learning employing the suggested genetic algorithmbased feature selection offers a clear runtime advantage. The final prediction model achieves AUC values between 90% and 99.6%. The findings showed that the model based on the features chosen by the GA algorithm provides a reasonable prediction quality with a small number of input variables, less than 31% of the entire feature space, and therefore permits the accurate separation of fake from real users. Our results demonstrate exceptional predictive accuracy with a significant reduction in input variables using the genetic algorithm, reaffirming the effectiveness of our approach. Policy Significance Statement Machine -learning algorithms coupled with genetic algorithm -based feature selection offer a powerful approach for detecting fake accounts in Online Social Networks platforms. This research demonstrates that by leveraging advanced machine -learning techniques and employing genetic algorithms for feature selection, it is possible to achieve highly accurate and efficient identification of fake accounts. Furthermore, the integration of genetic algorithm -based feature selection optimizes the performance of the detection system by identifying the most informative features. This improves the efficiency and effectiveness of fake account detection and reduces computational complexity.