Abstract
Currently, in the smartphone market, Android is the platform with the highest share. Due to this popularity and also to its open source nature, Android-based smartphones are now an ideal target for attackers. Since the number of malware designed for Android devices is increasing fast, Android users are looking for security solutions aimed at preventing malicious actions from damaging their smartphones.
In this paper, we describe MADAM, a Multi-level Anomaly Detector for Android Malware. MADAM concurrently monitors Android at the kernel-level and user-level to detect real malware infections using machine learning techniques to distinguish between standard behaviors and malicious ones. The first prototype of MADAM is able to detect several real malware found in the wild. The device usability is not affected by MADAM due to the low number of false positives generated after the learning phase.
The research leading to these results has received funding from the EU FP7 under grant n. 256980 (NESSoS) and under grant n- 257930 (Aniketos).
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Dini, G., Martinelli, F., Saracino, A., Sgandurra, D. (2012). MADAM: A Multi-level Anomaly Detector for Android Malware. In: Kotenko, I., Skormin, V. (eds) Computer Network Security. MMM-ACNS 2012. Lecture Notes in Computer Science, vol 7531. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33704-8_21
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DOI: https://doi.org/10.1007/978-3-642-33704-8_21
Publisher Name: Springer, Berlin, Heidelberg
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