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The acceptance and widespread use of the Android operating system drew theattention of both legitimate developers and malware authors, which resulted ina significant number of benign and malicious applications available on variousonline markets. Since the signature-based methods fall short for detectingmalicious software effectively considering the vast number of applications,machine learning techniques in this field have also become widespread. In thiscontext, stating the acquired accuracy values in the contingency tables inmalware detection studies has become a popular and efficient method and enabledresearchers to evaluate their methodologies comparatively. In this study, wewanted to investigate and emphasize the factors that may affect the accuracyvalues of the models managed by researchers, particularly the disassemblymethod and the input data characteristics. Firstly, we developed a model thattackles the malware detection problem from a Natural Language Processing (NLP)perspective using Long Short-Term Memory (LSTM). Then, we experimented withdifferent base units (instruction, basic block, method, and class) andrepresentations of source code obtained from three commonly used disassemblingtools (JEB, IDA, and Apktool) and examined the results. Our findings exhibitthat the disassembly method and different input representations affect themodel results. More specifically, the datasets collected by the Apktoolachieved better results compared to the other two disassemblers.

Malicious code detection in android: the role of sequence characteristics and disassembling methods