Android Malware Family Classification Based on Resource Consumption over Time

The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years. An important task of malware analysis is the classification of malware samples into known families. Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques. To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScribe. With respect to DroidScribe, our approach is easier to reproduce. Our methodology only employs publicly available tools, does not require any modification to the emulated environment or Android OS, and can collect data from physical devices. The latter is a key factor, since modern mobile malware can detect the emulated environment and hide their malicious behavior. Our approach relies on resource consumption metrics available from the proc file system. Features are extracted through detrended fluctuation analysis and correlation. Finally, a SVM is employed to classify malware into families. We provide an experimental evaluation on malware samples from the Drebin dataset, where we obtain a classification accuracy of 82%, proving that our methodology achieves an accuracy comparable to that of DroidScribe. Furthermore, we make the software we developed publicly available, to ease the reproducibility of our results.Comment: Extended Versio

Proposed Framework to Improving Performance of Familial Classification in Android Malware

Because of the recent developments in hardware and software technologies for mobile phones, people depend on their smartphones more than ever before. Today, people conduct a variety of business, health, and financial transactions on their mobile devices. This trend has caused an influx of mobile applications that require users' sensitive information. As these applications increase so too have the number of malicious applications increased, which may compromise users' sensitive information. Between all smartphone, Android receives major attention from security practitioners and researchers due to the large number of malicious applications. For the past twelve years, Android malicious applications have been clustered into groups for better identification. Characterizing the malware families can improve the detection process and understand the malware patterns. However, in the research community, detecting new malware families is a challenge. In this research, a framework is proposed to improve the performance of familial classification in Android malware. The framework is named a Reverse Engineering Framework (RevEng). Within RevEng, applications' permissions were selected and then fed into machine learning algorithms. Through our research, we created a reduced set of permissions using Extremely Randomized Trees algorithm that achieved high accuracy and a shorter execution time. Furthermore, we conducted two approaches based on the extracted information. The first approach used a binary value representation of the permissions. The second approach used the features' importance. We represented each selected permission in latter approach by its weight value instead of its binary value in the former approach. We conducted a comparison between the results of our two approaches and other relevant works. Our approaches achieved better results in both accuracy and time performance with a reduced number of permissions