Supervised Learning Based Detection of Malware on Android
F. Tchakounté; F. Hayata University of Ngaoundéré, Ngaoundéré, Cameroon
Abstract
This chapter aims to present a new approach for detecting Android malware by relying permissions and supervised learning techniques. For that, we present security and its flaws in the Android system. Then we present concepts around machine learning and how they can be used for malware detection in general. We discuss works using permissions as key feature for the characterization of applications to detect malicious behavior.
We present a detection system combining the proportion of requested permissions and risks induced on resources. This system requires the user to specify resources to protect ...
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