Analysis, Detection, and Classification of Android Malware using System Calls
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With the increasing popularity of Android in the last decade, Android is popular among users as well as attackers. The vast number of android users grabs the attention of attackers on android. Due to the continuous evolution of the variety and attacking techniques of android malware, our detection methods should need an update too. Most of the researcher's works are based on static features, and very few focus on dynamic features. In this paper, we are filling the literature gap by detecting android malware using System calls. We are running the malicious app in a monitored and controlled environment using an emulator to detect malware. Malicious behavior is activated with some simulated events during its runtime to activate its hostile behavior. Logs collected during the app's runtime are analyzed and fed to different machine learning models for Detection and Family classification of Malware. The result indicates that K-Nearest Neighbor and the Decision Tree gave the highest accuracy in malware detection and Family Classification respectively.
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