Identifying Risky Apps on the Google Play Store: A Decision Tree Approach

Abstract

Abstract: With the rapid advancement of technology, fraudulent phone calls, such as spam and hazardous calls, have become a major issue for the telecom industry, resulting in annual financial losses of millions of dollars worldwide. Phishing and spamming via phone or mobile devices, as well as fraudulent phone calls, have become common dangers to individuals and organizations. Machine learning (ML) and artificial intelligence (AI) have evolved into excellent approaches for detecting and understanding fraudulent or damaging calls. This paper presents an overview of AI-based methods for detecting and evaluating spam or fraud, as well as an evaluation of their shortcomings and potential solutions. A new method for detecting fraudulent calls is proposed, which has shown extraordinary precision and accuracy. A dataset of actual fraudulent calls was used to evaluate the suggested approach. The results also showed that the technique was extremely accurate in detecting possibly fraudulent or spammy calls, as well as malicious calls. Countermeasures can be developed using the knowledge gathered from the research of fraud calls, which showed scammers' methods and approaches