An Empirical Study of Efficient Malware Detection Analysis on Android Mobile Phones using Machine Learning

Nowadays, smartphones occupy a significant role in everyone’s life; in the present circumstances, android mobile applications as well as their security threats were improved. Therefore, an automatic model is essential to detect malware in smartphones to safe from harmful threats. In recent days researchers were concerned about machine learning-based detection models for detecting the malware which is present in smartphones. Large numbers of ML-based techniques were proposed for android malware detection. Lack of knowledge about the methods available for the detection of malware in the android initiates several challenges. Hence, in this survey, 27 works of literature based on machine learning grounded techniques connected with the malware analysis technique are reviewed. The study of various methods was simplified based on their performance measures as well as the achievement of each method. In addition, the limitations, as well as the improvements of each method, were represented in the study. Eventually, consider the potential future research as well as the challenges in terms of obtaining high accuracy for malware detection. The study methods were reviewed in research gaps and it was represented in the taxonomy diagram.