An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data...

Area of Science:

• Computer Science
• Cybersecurity
• Biomedical Engineering

Background:

• Brain-machine interfacing (BMI) links humans and devices via brain signals, often using mobile applications.
• The Android platform's openness facilitates rapid app development but also increases Android malware threats.
• Existing Android malware detection methods struggle with diverse app categories and complex malware behaviors, impacting BMI data security.

Purpose of the Study:

• To propose a comprehensive approach for Android malware detection by analyzing a broad spectrum of app behavioral characteristics.
• To improve the efficiency and accuracy of detecting Android malware, thereby enhancing the security of brain-machine interfacing data transmission.

Main Methods:

• Automatic extraction of static and dynamic behavioral characteristics from Android applications.
• Systematic comparison of the efficiency of various behavioral characteristics for app analysis and malware detection.
• Experimental evaluation of chosen behavioral characteristics combined with machine learning algorithms for Android malware detection.

Main Results:

• The study successfully extracted diverse static and dynamic behavioral characteristics from Android apps.
• Experiments demonstrated the comparative efficiency of different behavioral characteristics in various aspects of analysis.
• The combined approach of broad behavioral characteristics and machine learning algorithms showed promising Android malware detection performance.

Conclusions:

• A broad analysis of app behavior, encompassing both static and dynamic characteristics, is crucial for effective Android malware detection.
• The proposed method offers a more efficient and robust solution for safeguarding brain-machine interfacing systems against Android malware threats.
• Further research can explore optimizing feature selection and machine learning models for enhanced security in BMI applications.