Malware Detection with Confidence Guarantees on Android Devices
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 ha
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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 signaturebased 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. Keywords: Malware detection · Android · Security · Inductive Conformal Prediction · Confidence measures · Multilayer Perceptron
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Introduction
The widespread deployment of smartphone devices has brought a revolution in mobile applications and services that span from simple messaging and calling applications to more sensitive financial transactions and internet banking services. As a result, a great deal of sensitive information, such as access passwords and credit card numbers, are stored on smartphone devices, which has made them a very attractive target for cybercriminals. More specifically, a significant increase of malware attacks was observed in the past few years, aiming at stealing private information and sending it to unauthorized third-parties. Mobile malware are malicious software used to gather information and/or gain access to mobile computer devices such as smartphones or tablets. In particular, they are packaged and redistributed with third-party applications to inject c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved L. Iliadis and I. Maglogiannis (Eds.): AIAI 2016, IFIP AICT 475, pp. 407–418, 2016. DOI: 10.1007/978-3-319-44944-9 35
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malicious content into a smartphone and therefore expose the device’s security. While the first one appeared in 2004 targeting the Nokia Symbian OS [1], in the fourth quarter of 2015 G DATA security experts reported discovering 8,240 new malware applications on average per day and a total of 2.3 million new malware samples in 2015, in just the Android OS [2]. When malware compromises a smartphone, it can illegally watch and impersonate its user, participate in dangerous botnet activities without the user’s consent and capture user’s personal data. Mobile malware detection techniques can be c
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