# PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting +
## 1 Introduction
for Android security threats and proposed solutions to minimize human roles by analyzing the granted security at runtime and revoking them as required; many of these misconfigurations are also caused by the physical appearance of the smartphone [5]. There exists a large piece of prior work related to malware detection, and authors have proposed solutions specifically for detecting malware in Android. [PERSON] et al. [6] and many recent studies [7; 8; 9; 10; 11] have presented an in-depth analysis of Android malware detection techniques. For example, [PERSON] et al. [9] reviewed the code analysis and ML-based Android malware detection techniques and listed future challenges in Android malware analysis and detection research. The following research works have been done in the direction of providing online services and platforms to perform analysis and classification of Android malware, quite similar to what we proposed in this article. In [12], the author developed a web-based Android malware detection and classification application following client-server architecture which helps users to submit and analyze apps based on static analysis. In Table 1, a summary of the key features of various similar platforms is presented. After a thorough study of these platforms, we observe that \"most of these platforms are focused only on malware static or/and dynamic feature analysis and many of them are either not functional or not updated according to a recent version of Android OS\". To the best of our knowledge, no online or offline tool exists that offers ML-based Android malware detection and classification services. However, we came across a few works where implementation code and dataset are publicly available [1; 13; 14]. These isolated studies motivated us to propose a platform which can reproduce distributed Android malware research on one platform and make it available to users holding different responsibilities and domain expertise. Our earlier proposed work [15] i.e., a Platform for Android malware Classification and performance Evaluation (PACE) is built upon state-of-art in Android malware detection, in particular, ML-based and non-signature-based techniques. The reproducible research is one of the main motivations of our work using research contributions from many authors and building an end-user product which serves the need for multiple stakeholders in the security community. The core idea was presented in [15] and this proposed work is a substantial improvement over _PACE_. In this proposed work (Platform for Android Malware Classification, Performance Evaluation and Threat Reporting (PACER)), we have extended the functionality of _PACE_ by enhancing threat intelligence functionality and providing a proper reporting module for the end-user device. The new threat intelligence module uses _PACE_ for malware scanning and also scans the device against various published Common Vulnerabilities and Exposures (CVEs) for Android platform and apps and uses app metadata to find other threat actors like _outdated apps_, apps with _negative reviews_, etc. The reporting module aims to simplify the threat and vulnerability reporting by providing an in-depth report (PDF format) on malicious apps, vulnerable apps (app versions with known CVEs), outdated apps, etc. for any Android smartphone. The major contributions of _PACER_ are:  
1. Reproducible and transparent research;
2. Improved Android malware detection;
3. Multi-application integration;
4. Triaging and fast incident response;
5. Android threat intelligence;
6. Simple, descriptive and readable in-device threat report. The performance gain and differentiated points regarding the proposed work _PACER_ lies in its capabilities to provide an easy research platform for Android malware detection, generating threat intelligence and effective reporting.