Abstract
In this paper, a static analysis malware detection system based on machine learning techniques and making use of features like hardware components, requested permissions, application components, and filtered intents are extracted from various applications. Prominent features are selected as a part of dimensionality reduction using GSS coefficient and mutual information. Experiment has been evaluated on 3000 malware samples from Drebin dataset and on 1631 benign samples collected from Google Play Store. High ROC curve of 0.998 has been obtained for model developed using individual attributes with overall scanning time of 1.49 s. However, when the optimal features extracted from each category of attributes were aggregated a remarkable improvement in F-measure, i.e., 0.996 was noticed with a low FPR value of 0.003 concluding the fact that the approach can be used to support commercial AV.
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John, M.M., Vinod, P. (2018). Statistical Approach Using Meta Features for Android Malware Detection System. In: Bokhari, M., Agrawal, N., Saini, D. (eds) Cyber Security. Advances in Intelligent Systems and Computing, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-8536-9_27
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DOI: https://doi.org/10.1007/978-981-10-8536-9_27
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