%0 Conference Proceedings
%T RiskInDroid: Machine Learning-Based Risk Analysis on Android
%+ Università degli studi di Genova = University of Genoa (UniGe)
%+ Talos Security, s.r.l.s.
%A Merlo, Alessio
%A Georgiu, Gabriel, Claudiu
%Z Part 9: Digital Signature, Risk Management, and Code Reuse Attacks
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 32th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC)
%C Rome, Italy
%Y Sabrina De Capitani di Vimercati
%Y Fabio Martinelli
%I Springer International Publishing
%3 ICT Systems Security and Privacy Protection
%V AICT-502
%P 538-552
%8 2017-05-29
%D 2017
%R 10.1007/978-3-319-58469-0_36
%K Risk analysis
%K Android security
%K Static analysis
%K Machine learning
%Z Computer Science [cs]Conference papers
%X Risk analysis on Android is aimed at providing metrics to users for evaluating the trustworthiness of the apps they are going to install. Most of current proposals calculate a risk value according to the permissions required by the app through probabilistic functions that often provide unreliable risk values. To overcome such limitations, this paper presents RiskInDroid, a tool for risk analysis of Android apps based on machine learning techniques. Extensive empirical assessments carried out on more than 112 K apps and 6 K malware samples indicate that RiskInDroid outperforms probabilistic methods in terms of precision and reliability.
%G English
%Z TC 11
%2 https://inria.hal.science/hal-01648990/document
%2 https://inria.hal.science/hal-01648990/file/449885_1_En_36_Chapter.pdf
%L hal-01648990
%U https://inria.hal.science/hal-01648990
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC11
%~ IFIP-SEC
%~ IFIP-AICT-502