Abstract:
Android malware has become the topmost threat for the ubiquitous and useful Android
ecosystem. Multiple solutions leveraging big data and machine-learning capabilities to detect
Android malware are being constantly developed. Too often, these solutions are either limited to
research output or remain isolated and incapable of reaching end users or malware researchers.
An earlier work named PACE (Platform for Android Malware Classification and Performance
Evaluation), was introduced as a unified solution to offer open and easy implementation access
to several machine-learning-based Android malware detection techniques, that makes most of the
research reproducible in this domain. The benefits of PACE are offered through three interfaces:
Representational State Transfer (REST) Application Programming Interface (API), Web Interface,
and Android Debug Bridge (ADB) interface. These multiple interfaces enable users with different
expertise such as IT administrators, security practitioners, malware researchers, etc. to use their
offered services. In this paper, we propose PACER (Platform for Android Malware Classification,
Performance Evaluation, and Threat Reporting), which extends PACE by adding threat intelligence
and reporting functionality for the end-user device through the ADB interface. A prototype of the
proposed platform is introduced, and our vision is that it will help malware analysts and end users
to tackle challenges and reduce the amount of manual work