Publicação: Malware Detection in Android-based Mobile Environments using Optimum-Path Forest
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
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
Idioma
Inglês
Como citar
2015 Ieee 14th International Conference On Machine Learning And Applications (icmla). Amsterdam: Elsevier Science Bv, p. 754-759, 2015.
