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

Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware