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The astonishing spread of Android OS, not only in smartphones and tablets butalso in IoT devices, makes this operating system a very tempting target formalware threats. Indeed, the latter are expanding at a similar rate. In thisrespect, malware fingerprints, whether based on cryptographic or fuzzy-hashing,are the first defense line against such attacks. Fuzzy-hashing fingerprints aresuitable for capturing malware static features. Moreover, they are moreresilient to small changes in the actual static content of malware files. Onthe other hand, dynamic analysis is another technique for malware detectionthat uses emulation environments to extract behavioral features of Androidmalware. However, to the best of our knowledge, there is no such fingerprintingtechnique that leverages dynamic analysis and would act as the first defenseagainst Android malware attacks. In this paper, we address the followingquestion: could we generate effective fingerprints for Android malware throughdynamic analysis? To this end, we propose DySign, a novel technique forfingerprinting Android malware's dynamic behaviors. This is achieved throughthe generation of a digest from the dynamic analysis of a malware sample onexisting known malware. It is important to mention that: (i) DySignfingerprints are approximated of the observed behaviors during dynamic analysisso as to achieve resiliency to small changes in the behaviors of future malwarevariants; (ii) Fingerprint computation is agnostic to the analyzed malwaresample or family. DySign leverages state-of-the-art Natural Language Processing(NLP) techniques to generate the aforementioned fingerprints, which are thenleveraged to build an enhanced Android malware detection system with familyattribution.

DySign: Dynamic Fingerprinting for the Automatic Detection of Android Malware