Authors:
Muhammad Ikram
1
;
Pierrick Beaume
2
and
Mohamed Ali Kaafar
3
Affiliations:
1
Macquarie University, Australia, University of Michigan and U.S.A.
;
2
Data61 CSIRO and Australia
;
3
Macquarie University, Australia, Data61 CSIRO and Australia
Keyword(s):
Malware, Obfuscation, Machine Learning, Android, Mobile Apps.
Related
Ontology
Subjects/Areas/Topics:
Information and Systems Security
;
Security and Privacy in Mobile Systems
;
Software Security
Abstract:
With the number of new mobile malware instances increasing by over 50% annually since 2012 (McAfee, 2017), malware embedding in mobile apps is arguably one of the most serious security issues mobile platforms are exposed to. While obfuscation techniques are successfully used to protect the intellectual property of apps’ developers, they are unfortunately also often used by cybercriminals to hide malicious content inside mobile apps and to deceive malware detection tools. As a consequence, most of mobile malware detection approaches fail in differentiating between benign and obfuscated malicious apps. We examine the graph features of mobile apps code by building weighted directed graphs of the API calls, and verify that malicious apps often share structural similarities that can be used to differentiate them from benign apps, even under a heavily “polluted” training set where a large majority of the apps are obfuscated. We present DaDiDroid an Android malware app detection tool that l
everages features of the weighted directed graphs of API calls to detect the presence of malware code in (obfuscated) Android apps. We show that DaDiDroid significantly outperforms MaMaDroid (Mariconti et al., 2017), a recently proposed malware detection tool that has been proven very efficient in detecting malware in a clean non-obfuscated environment. We evaluate DaDiDroid’s accuracy and robustness against several evasion techniques using various datasets for a total of 43,262 benign and 20,431 malware apps. We show that DaDiDroid correctly labels up to 96% of Android malware samples, while achieving an 91% accuracy with an exclusive use of a training set of obfuscated apps.
(More)