This is the public webpage of the paper published in Security and Communication Networks, Volume 2018, Article ID 4672072 https://doi.org/10.1155/2018/4672072
Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analyzing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead.
The paper can be cited as
Marios Argyriou, Nicola Dragoni, and Angelo Spognardi, “Analysis and Evaluation of SafeDroid v2.0, a Framework for Detecting Malicious Android Applications,” Security and Communication Networks, vol. 2018, Article ID 4672072, 15 pages, 2018. https://doi.org/10.1155/2018/4672072The first paper mentioning the original approach and with preliminary results is
Rohit Goyal, Angelo Spognardi, Nicola Dragoni, and Marios Argyriou, “SafeDroid: A distributed malware detection service for android,” in Proceedings of the 9th IEEE International Conference on Service-Oriented Computing and Applications, SOCA'16, pp. 59–66, Macau, China, November 2016. https://doi.org/10.1109/SOCA.2016.14 The dataset to reproduce the experiments of the paper is publicly available for research purposes and can be download at the following link: sciencedata mirror
The official repository of the framework is on github at the following link: https://github.com/Dubniak/SafeDroid-v2.0
For any inquiry or question, please refer to spognardi@di.uniroma1.it