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Malware Detection with Confidence Guarantees on Android Devices

Malware Detection with Confidence Guarantees on Android Devices

Authors :
Nestoras Georgiou
Andreas Konstantinidis
Harris Papadopoulos
Source :
IFIP Advances in Information and Communication Technology ISBN: 9783319449432, AIAI
Publication Year :
2016
Publisher :
Springer International Publishing, 2016.

Abstract

The evolution of ubiquitous smartphone devices has given rise to great opportunities with respect to the development of applications and services, many of which rely on sensitive user information. This explosion on the demand of smartphone applications has made them attractive to cybercriminals that develop mobile malware to gain access to sensitive data stored on smartphone devices. Traditional mobile malware detection approaches that can be roughly classified to signature-based and heuristic-based have essential drawbacks. The former rely on existing malware signatures and therefore cannot detect zero-day malware and the latter are prone to false positive detections. In this paper, we propose a heuristic-based approach that quantifies the uncertainty involved in each malware detection. In particular, our approach is based on a novel machine learning framework, called Conformal Prediction, for providing valid measures of confidence for each individual prediction, combined with a Multilayer Perceptron. Our experimental results on a real Android device demonstrate the empirical validity and both the informational and computational efficiency of our approach.

Details

ISBN :
978-3-319-44943-2
ISBNs :
9783319449432
Database :
OpenAIRE
Journal :
IFIP Advances in Information and Communication Technology ISBN: 9783319449432, AIAI
Accession number :
edsair.doi...........3425ad3e325de31e27f8417ea0795be6
Full Text :
https://doi.org/10.1007/978-3-319-44944-9_35