To handle relentlessly emerging Android malware, deep learning has been widely adopted in the research community. Prior work proposed deep learning-based approaches that use different features of malware, and reported a high accuracy in malware detection, i.e., classifying malware from benign applications. However, familial analysis of real-world Android malware has not been extensively studied yet. Familial analysis refers to the process of classifying a given malware into a family (or a set of families), which can greatly accelerate malware analysis as the analysis gives their fine-grained behavioral characteristics. In this work, we shed light on deep learning-based familial analysis by studying different features of Android malware and how effectively they can represent their (malicious) behaviors. We focus on string features of Android malware, namely the Abstract Syntax Trees (AST) of all functions extracted from each malware, which faithfully represent all string features of Android malware. We thoroughly study how different string features, such as how security-sensitive APIs are used in malware, affect the performance of a neural network. A convolutional neural network was trained and tested in various configurations on 28,179 real-world malware dataset appeared in the wild from 2018 to 2020, where each malware has one or more labels assigned based on their behaviors. Our evaluation reveals how different features contribute to the performance of familial analysis. Notably, with all features combined, we were able to produce up to an accuracy of 98% and a micro F1-score of 0.82, a result on par with the state-of-the-art.

Sensitive information leakages from applications are a critical issue in the Android ecosystem. Despite the advance of techniques to secure applications such as packing and obfuscation, a lot of applications are still under the threat of repackaging attacks that inject malicious code and redistribute applications. Also, as we are becoming more dependent on mobile technologies, more sensitive information is used on our mobile devices. Hence, it is of great importance to reduce the risk of such sensitive information leaks. In this paper, we first present a threat model that attempts to leak users' sensitive information by using the repackaging attack, named ReMaCi attack. By analyzing the top 8,546 applications downloaded from Google Play Store, we show that 50% of them are really vulnerable to the ReMaCi attack. We, thus, propose a novel, automated static anti-analysis tool, called AmpDroid, for preventing sensitive information leaks. AmpDroid identifies sensitive dataflows and isolates the code that handles the sensitive data from an application. To demonstrate the effectiveness of AmpDroid, we perform the security and performance evaluation of AmpDroid, comparing it with other obfuscation tools.