MEGDroid: A model-driven event generation framework for dynamic android malware analysis

Context: The tremendous growth of Android malware in recent years is a strong motivation for the vast endeavor in detection and analysis of malware apps. A prominent approach for this purpose is dynamic analysis in which providing complex interactions with the samples under analysis is a need. Event generation tools are almost used to provide such interactions, but they have deficiencies for effective malware analysis. For example, anti-static and anti-dynamic analysis techniques employed by the malware prevent event generators to extract sufficient information for generating appropriate events. As a result, they fail to trigger malicious payloads or obtain high code coverage in most cases. Objective: In this paper, we aim to present a new framework to improve the event generation process for dynamic analysis of Android malware. Method: We propose MEGDroid, a Model Driven Engineering (MDE) framework in which malware-related information is automatically extracted and represented as a domain-specific model. This model, then is used to generate appropriate events for malware analysis using model-to-model and model-to-code transformations. The proposed model-driven artifacts also provide required facilities to put the human in the loop for properly taking his/her knowledge into account. Results: The proposed framework has been realized as an Eclipse plugin and we performed extensive practical analysis on a set of malware samples selected from the AMD dataset. The experimental results showed that MEGDroid considerably increases the number of triggered malicious payloads as well as the execution code coverage compared with Monkey and DroidBot, as two state of the art general-purpose and malware specific event generators respectively. Conclusion: The proposed MDE approach, enhances the event generation process through both automatic event generation and analyzer user involvement who can efficiently direct the process to increase the effectiveness of the generated events considering small amount of information that is extractable from the malware code.