Risk Analysis of Android Application

Abstract

The report introduces AndroidRisk, which is a tool that employs machine learning techniques to analyze Android apps and provide users with more reliable metrics to evaluate their trustworthiness. This is in contrast to current probabilistic methods, which can be unreliable. The tool was evaluated on more than 112K apps and 6K malware samples, and it was found to outperform probabilistic methods in terms of precision and reliability. AndroidRisk works by analyzing the app's features such as its permissions and then using a machine learning algorithm to classify the app as either benign or malicious. The algorithm is trained on a dataset of known benign and malicious apps, and it can detect previously unseen malware by recognizing patterns in the app's features. The results of the empirical assessments demonstrate that AndroidRisk is more precise and reliable than probabilistic methods in detecting malware. The tool's ability to accurately detect malware makes it a valuable addition to the existing suite of security tools available to Android users. In summary, AndroidRisk is a promising tool for risk analysis of Android apps that utilizes machine learning techniques to provide more reliable metrics to users. Its effectiveness in detecting malware suggests that it could play a significant role in enhancing the security of Android devices.