Android Permission's Analysis Using Ontologies and Machine Learning

Data
21/10/20152015-10-20 22:00:00 2015-10-20 22:00:00 Android Permission's Analysis Using Ontologies and Machine Learning Success on analysis and construction of any system depends on a good model which allows to represent components, properties, relationships and consequently features and behaviors. As ontologies are the way to represent data with their associated meaning (semantics) and inference logic, it is a good foundation for creating system models which are computable. They can be stored and processed using ontology technologies as defined on Web Semantic arena. In the other hand, a system description easily grows in complexity up to levels where it is difficult to establish cause-effect inference rules due to the complicated chain of conditions that are normally found in actual models. Then machine learning can help to identify what are the elements, properties and relations which are important on the cause-effect complex relationship. It is not the final solution as it depends on a large number of samples and the quality of information captured, but it can help on the process of investigation and model refinement. Particularly on the malware attacks, permissions have an important role as they are part of the resources' protection mechanism . This presentation shows a method to represent Android packages and permissions using OWL (Ontology Web Language) and a way to analyze the model using Random Forest as a base algorithm of a machine learning approach to identify permissions which can be related to malwares. Auditório do IC 2 - Sala 85 Auditório do IC 2 - Sala 85 Auditório do IC 2 - Sala 85 America/Sao_Paulo public
Horário
14:00 h
Local
Auditório do IC 2 - Sala 85
Palestrante
Luiz Claudio Navarro (lcnavarro@lasca.ic.unicamp.br)
Descrição

Success on analysis and construction of any system depends on a good model which allows to represent components, properties, relationships and consequently features and behaviors. As ontologies are the way to represent data with their associated meaning (semantics) and inference logic, it is a good foundation for creating system models which are computable. They can be stored and processed using ontology technologies as defined on Web Semantic arena. In the other hand, a system description easily grows in complexity up to levels where it is difficult to establish cause-effect inference rules due to the complicated chain of conditions that are normally found in actual models. Then machine learning can help to identify what are the elements, properties and relations which are important on the cause-effect complex relationship. It is not the final solution as it depends on a large number of samples and the quality of information captured, but it can help on the process of investigation and model refinement. Particularly on the malware attacks, permissions have an important role as they are part of the resources' protection mechanism . This presentation shows a method to represent Android packages and permissions using OWL (Ontology Web Language) and a way to analyze the model using Random Forest as a base algorithm of a machine learning approach to identify permissions which can be related to malwares.

Informações Adicionais

Responsável: Marcelo I.P. Salas
Email: marcelopalma@ic.unicamp.br
Fone: (19) 98809-6410
LASCA, Sala 84
Instituto de Computação, UNICAMP