%0 Conference Proceedings
%T IntelliAV: Toward the Feasibility of Building Intelligent Anti-malware on Android Devices
%+ Università degli Studi di Cagliari = University of Cagliari (UniCa)
%A Ahmadi, Mansour
%A Sotgiu, Angelo
%A Giacinto, Giorgio
%Z Part 3: MAKE Privacy
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Reggio, Italy
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-10410
%P 137-154
%8 2017-08-29
%D 2017
%R 10.1007/978-3-319-66808-6_10
%K Android
%K Malware detection
%K Machine learning
%K On-device
%K TensorFlow
%K Mobile security
%K Classification
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Android is targeted the most by malware coders as the number of Android users is increasing. Although there are many Android anti-malware solutions available in the market, almost all of them are based on malware signatures, and more advanced solutions based on machine learning techniques are not deemed to be practical for the limited computational resources of mobile devices. In this paper we aim to show not only that the computational resources of consumer mobile devices allow deploying an efficient anti-malware solution based on machine learning techniques, but also that such a tool provides an effective defense against novel malware, for which signatures are not yet available. To this end, we first propose the extraction of a set of lightweight yet effective features from Android applications. Then, we embed these features in a vector space, and use a pre-trained machine learning model on the device for detecting malicious applications. We show that without resorting to any signatures, and relying only on a training phase involving a reasonable set of samples, the proposed system outperforms many commercial anti-malware products, as well as providing slightly better performances than the most effective commercial products.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-01677144/document
%2 https://inria.hal.science/hal-01677144/file/456304_1_En_10_Chapter.pdf
%L hal-01677144
%U https://inria.hal.science/hal-01677144
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-LNCS-10410
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9