Abstract
As the leading mobile phone operating system, Android is an attractive target for malicious applications trying to exploit the system’s security vulnerabilities. Although several approaches have been proposed in the research literature for the detection of Android malwares, many of them suffer from issues such as small training datasets, there are few features (most studies are limited to permissions) that ultimately affect their performance. In order to address these issues, we propose an approach combining advanced machine learning techniques and Android vulnerabilities taken from the AndroVul dataset, which contains a novel combination of features for three different vulnerability levels, including dangerous permissions, code smells, and AndroBugs vulnerabilities. Our approach relies on that dataset to train Deep Learning (DL) and Support Vector Machine (SVM) models for the detection of Android malware. Our results show that both models are capable of detecting malware encoded in Android APK files with about 99% accuracy, which is better than the current state-of-the-art approaches.
Highlights
The adoption of mobile applications in a wide range of domains has made many activities, from banking to education or gaming, simpler, faster, or more convenient
We provide comparison of the performance of our Deep Learning (DL) and Support Vector Machine (SVM) classifiers, with respect to state-of-the-art approaches and even some commercial anti-viruses and results show that our classifiers are the most effective in identifying malicious applications
We investigated two of the most powerful families of machine learning techniques: Deep Learning (DL) and Support Vector Machines (SVM)
Summary
The adoption of mobile applications in a wide range of domains has made many activities, from banking to education or gaming, simpler, faster, or more convenient. Users are generally uneducated about the risks of the permissions they can be asked to grant They may grant permissions allowing malicious apps to exploit security breaches [2] and to monitor a mobile device without the user’s consent [3]. This approach necessitates the declaration by app developers of which sensitive resources will be utilised by their applications. According to Android, there are several categories of permissions, among which are “dangerous” ones, which are deemed more critical and privacy sensitive because they grant access to system features such as cameras and internet access as well as personal contact information and SMS messages, among other things [10]
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