A Robust Machine Learning Framework for Fraudulent Mobile App Detection
Hassan Zaki, Muhammad Saad, Muhammad Rehan Rasheed · VFAST Transactions on Software Engineering · 2024
The rapid development of mobile applications has led to a significant rise in the number of fraudulent applications. The biggest risk now is financial loss and possible security compromise. Thus, the "Fraud App Detection" framework goal is to develop a reliable system that can recognize and categorize fraudulent apps utilizing cutting-edge machinelearning and artificial intelligence approaches. The process of identifying fraudulent patterns involves gathering data, preprocessing applications, extracting features, and training several machine learning models. The model’s performance will be assessed based on evaluation criteria like recall, accuracy, and F1-score. To improve detection efficiencyand accuracy, this uses cutting-edge techniques such as neural networks, decision trees, and ensemble approaches. These results can be used in enhancing mobile app security protocols, thus safeguarding consumers from the probable threats of fraudulent applications.