A pragmatic android malware detection procedure

As a case study we designed and implemented an ensemble approach for automatic Android malware detection that meets the real-world requirements we identified. Atomic Naive Bayes classifiers used as inputs for the Support Vector Machine ensemble are based on different APK feature categories, providing fast speed and additional reliability against the attackers due to diversification. Our case study with several malware families showed that different families are detected by different atomic classifiers. To the best of our knowledge, our work contains the first publicly available results generated against evolving data streams of nearly 1 million samples with a model trained over a massive sample set of 120,000 samples.