  Android is one of the leading operating systems for smart phones in terms of
market share and usage. Unfortunately, it is also an appealing target for
attackers to compromise its security through malicious applications. To tackle
this issue, domain experts and researchers are trying different techniques to
stop such attacks. All the attempts of securing Android platform are somewhat
successful. However, existing detection techniques have severe shortcomings,
including the cumbersome process of feature engineering. Designing
representative features require expert domain knowledge. There is a need for
minimizing human experts' intervention by circumventing handcrafted feature
engineering. Deep learning could be exploited by extracting deep features
automatically. Previous work has shown that operational codes (opcodes) of
executables provide key information to be used with deep learning models for
detection process of malicious applications. The only challenge is to feed
opcodes information to deep learning models. Existing techniques use one-hot
encoding to tackle the challenge. However, the one-hot encoding scheme has
severe limitations. In this paper, we introduce; (1) a novel technique for
opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we
have developed a dataset for end-to-end detection of android malware.
Introducing the end-to-end Android malware detection technique avoids
expert-intensive handcrafted features extraction, and ensures automation. Some
of the recent deep learning-based techniques showed significantly improved
results when tested with the proposed approach and achieved an average
detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979.
