@Inbook{Georgiou2016,
author="Georgiou, Nestoras
and Konstantinidis, Andreas
and Papadopoulos, Harris",
editor="Iliadis, Lazaros
and Maglogiannis, Ilias",
title="Malware Detection with Confidence Guarantees on Android Devices",
bookTitle="Artificial Intelligence Applications and Innovations: 12th IFIP WG 12.5 International Conference and Workshops, AIAI 2016, Thessaloniki, Greece, September 16-18, 2016, Proceedings",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="407--418",
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.",
isbn="978-3-319-44944-9",
doi="10.1007/978-3-319-44944-9_35",
url="http://dx.doi.org/10.1007/978-3-319-44944-9_35"
}