Malware Detection in Android-based Mobile Environments using Optimum-Path Forest
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Data
2015-01-01
Autores
Costa, Kelton A. P. da [UNESP]
Silva, Luis A. da [UNESP]
Martins, Guilherme B. [UNESP]
Rosa, Gustavo H. [UNESP]
Pereira, Clayton R. [UNESP]
Papa, Joao P. [UNESP]
IEEE
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Editor
Elsevier B.V.
Resumo
Nowadays, people use smartphones and tablets with the very same purposes as desktop computers: web browsing, social networking and home-banking, just to name a few. However, we are often facing the problem of keeping our information protected and trustworthy. As a result of their popularity and functionality, mobile devices are a growing target for malicious activities. In such context, mobile malwares have gained significant ground since the emergence and growth of smartphones and handheld devices, becoming a real threat. In this paper, we introduced a recently developed pattern recognition technique called Optimum-Path Forest in the context of malware detection, as well we present DroidWare, a new public dataset to foster the research on mobile malware detection. In addition, we also proposed to use Restricted Boltzmann Machines for unsupervised feature learning in the context of malware identification.
Descrição
Palavras-chave
Optimum-Path Forest, Restricted Boltzmann Machines, Malware Detection
Como citar
2015 Ieee 14th International Conference On Machine Learning And Applications (icmla). Amsterdam: Elsevier Science Bv, p. 754-759, 2015.