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Analysis and Evaluation of SafeDroid v2.0, a Framework for Detecting Malicious Android Applications
DTU Compute, Technical University of Denmark, Lyngby, Denmark.
Örebro University, School of Science and Technology. DTU Compute, Technical University of Denmark, Lyngby, Denmark. (Centre for Applied Autonomous Sensor Systems)ORCID iD: 0000-0001-9575-2990
Dipartimento Informatica, Sapienza Università di Roma, Rome, Italy.
2018 (English)In: Security and Communication Networks, ISSN 1939-0114, E-ISSN 1939-0122, article id UNSP 4672072Article in journal (Refereed) Published
Abstract [en]

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 analysing 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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018. article id UNSP 4672072
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-69259DOI: 10.1155/2018/4672072ISI: 000444877600001Scopus ID: 2-s2.0-85053706618OAI: oai:DiVA.org:oru-69259DiVA, id: diva2:1253200
Available from: 2018-10-04 Created: 2018-10-04 Last updated: 2018-10-04Bibliographically approved

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Dragoni, Nicola

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