Android inter-app communication (IAC) allows apps to request functionalities from other apps, which has been extensively used to provide a better user experience. However, IAC has also become an enticing target by attackers to launch malicious activities. Dynamic class loading (DCL) and reflection are effective features to enhance the functionality of the apps. In this paper, we expose a new attack that leverages these features in conjunction with inter-app communication to conceal malicious attacks with the ability to bypass existing security mechanisms. To counteract such attack, we present DINA, a novel hybrid analysis approach for identifying malicious IAC behaviors concealed within dynamically loaded code through reflective/DCL calls. DINA appends reflection and DCL invocations to control-flow graphs and continuously performs incremental dynamic analysis to detect the misuse of reflection and DCL that obfuscates malicious Intent communications. DINA utilizes string analysis and inter-procedural analysis to resolve hidden IAC and achieves superior detection performance. Our extensive evaluation on 49,000 real-world apps corroborates the prevalent usage of reflection and DCL, and reveals previously unknown and potentially harmful, hidden IAC behaviors in real-world apps.

Java reflection and dynamic class loading (DCL) are effective features for enhancing the functionalities of Android apps. However, these features can be abused by sophisticated malware to bypass detection schemes. Advanced malware can utilize reflection and DCL in conjunction with Android Inter-App Communication (IAC) to launch collusion attacks using two or more apps. Such dynamically revealed malicious behaviors enable a new type of stealthy, collusive attacks, bypassing all existing detection mechanisms. In this paper, we present DINA, a novel hybrid analysis approach for identifying malicious IAC behaviors concealed within dynamically loaded code through reflective/DCL calls. DINA continuously appends reflection and DCL invocations to control-flow graphs; it then performs incremental dynamic analysis on such augmented graphs to detect the misuse of reflection and DCL that may lead to malicious, yet concealed, IAC activities. Our extensive evaluation on 3,000 real-world Android apps and 14,000 malicious apps corroborates the prevalent usage of reflection and DCL, and reveals previously unknown and potentially harmful, hidden IAC behaviors in real-world apps.

When multiple apps on an Android platform interact, faults and security vulnerabilities can occur. Software engineers need to be able to analyze interacting apps to detect such problems. Current approaches for performing such analyses, however, do not scale to the numbers of apps that may need to be considered, and thus, are impractical for application to real-world scenarios. In this paper, we introduce JITANA, a program analysis framework designed to analyze multiple Android apps simultaneously. By using a classloader-based approach instead of a compiler-based approach such as SOOT, JITANA is able to simultaneously analyze large numbers of interacting apps, perform on-demand analysis of large libraries, and effectively analyze dynamically generated code. Empirical studies of JITANA show that it is substantially more efficient than a state-of-the-art approach, and that it can effectively and efficiently analyze complex apps including Facebook, Pokemon Go, and Pandora that the state-of-the-art approach cannot handle.