The digitization of government departments and the automation of administrative processes have resulted in a popularity of mobile applications that request crucial information from users. However, this surge has also given rise to a multitude of counterfeit applications introduced by malicious actors. These deceptive apps primarily target users’ personal and sensitive data, posing a direct threat to privacy and potentially leading to financial harm. This paper introduces an innovative approach for detecting these fraudulent applications amidst the genuine ones. The primary goal is to develop a model that can differentiate between genuine and counterfeit applications by analyzing the assembly-level code in Smali files. A series of experiments have been conducted to achieve the highest accuracy, resulting in a notable rate of 76.92%.