HYBRIDROID: Using Hierarchical Machine Learning Algorithms to Spot Malicious Applications on Linux-Powered Smart phones: Deep Survey

Since the previous two decades, mobile phones have played a significant role in our lives. Android is the most widely used operating system for mobile devices. The rapidly evolving environment surrounding Android has attracted hackers who want to produce malware. Applications from the Android Market and other websites can be downloaded and installed on Android devices. This gives hackers the chance to repackage harmful code and insert it into legal programmers. Numerous malware detection methods have been proposed, and several frameworks for their identification that make use of both static and dynamic investigative methods have been developed. Different machine learning techniques are used to classify apps based on behavior in a new fusion approach. This offers excellent efficiency and accuracy in malware detection by combining static and dynamic analysis. The results of the experiments demonstrated high precision and excellent memory and power efficiency.