Classification of Malicious Financial Applications Using Naive Bayes and K-Nearest Neighbor Algorithms Based on Android Permission

In this research paper, we have worked on some of the vulnerabilities of multiple Android apps and we have explored the vulnerability of financial apps and how these apps are acting as threats in our daily lives. Nowadays in this digital technology, mobile banking is a common way for people to make transactions, through which many people have to face financial loss. we have identified the vulnerability of Financial Apps by performing permission analysis to protect the privacy, security, and financial security of these customers. Our main contribution is to find out the vulnerabilities of Android apps by permission analysis to create high-quality datasets using static analysis and learning the dataset through the K-Nearest Neighbor algorithm and Naive Bayes algorithm to show whether it is malicious or not by using the ‘Permission Dataset’ and the ‘Appdroid’ dataset. To test the developed model, we have extracted 1438 combined attributes from these two datasets and took 12050 applications samples. According to the test findings, the Naive Bayes model can improve detection by roughly 82.07% For further testing it was 88.25% that the KNN model has the highest accuracy and efficiency. Our main objective is to identify multiple Financials app vulnerabilities and create a comprehensive dataset by permission analysis and by using a machine learning model, as a result, we have been able to determine the Financial Apps vulnerability and safeguard these financial security, privacy, and safety.