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The phenomenal growth in use of android devices in the recent years has also been accompanied by the rise of android malware. This reality warrants development of tools and techniques to analyze android apps in large scale for security vetting. Most of the state-of-the-art vetting tools are either based on static analysis or on dynamic analysis. Static analysis has limited success if the malware app utilizes sophisticated evading tricks. Dynamic analysis on the other hand may not find all the code execution paths, which let some malware apps remain undetected. Moreover, the existing static and dynamic analysis vetting techniques require extensive human interaction. To ad- dress the above issues, we design a deep learning based hybrid analysis technique, which combines the complementary strengths of each analysis paradigm to attain better accuracy. Moreover, automated feature engineering capability of the deep learning framework addresses the human interaction issue. In particular, using lightweight static and dynamic analysis procedure, we obtain multiple artifacts, and with these artifacts we train the deep learner to create independent models, and then combine them to build a hybrid classifier to obtain the final vetting decision (malicious apps vs. benign apps). The experiments show that our best deep learning model with hybrid analysis achieves an area under the precision-recall curve (AUC) of 0.9998. In this paper, we also present a comparative study of performance measures of the various variants of the deep learning framework. Additional experiments indicate that our vetting system is fairly robust against imbalanced data and is scalable.