Authors:
Gerardo Canfora
;
Francesco Mercaldo
and
Corrado Aaron Visaggio
Affiliation:
University of Sannio, Italy
Keyword(s):
Malware, Android, Security, Testing, Static Analysis.
Related
Ontology
Subjects/Areas/Topics:
Information and Systems Security
;
Security and Privacy in Mobile Systems
;
Software Security
Abstract:
Mobile malware has grown in scale and complexity, as a consequence of the unabated uptake of smartphones
worldwide. Malware writers have been developing detection evasion techniques which are rapidly making
anti-malware technologies uneffective. In particular, zero-days malware is able to easily pass signature based
detection, while dynamic analysis based techniques, which could be more accurate and robust, are too costly
or inappropriate to real contexts, especially for reasons related to usability. This paper discusses a technique
for discriminating Android malware from trusted applications that does not rely on signature, but on identifying
a vector of features obtained from the static analysis of the Android’s Dalvik code. Experimentation
accomplished on a sample of 11,200 applications revealed that the proposed technique produces high precision
(over 93%) in mobile malware detection, with an accuracy of 95%.