DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling

Muhammad Ikram, Pierrick Beaume, Mohamed Kaafar

2019

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 leverages 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.

Paper Citation

in Harvard Style

Ikram M., Beaume P. and Kaafar M. (2019). DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling.In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 2: SECRYPT, ISBN 978-989-758-378-0, pages 211-219. DOI: 10.5220/0007834602110219

in Bibtex Style

@conference{secrypt19,
author={Muhammad Ikram and Pierrick Beaume and Mohamed Kaafar},
title={DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling},
booktitle={Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 2: SECRYPT,},
year={2019},
pages={211-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007834602110219},
isbn={978-989-758-378-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 2: SECRYPT,
TI - DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling
SN - 978-989-758-378-0
AU - Ikram M.
AU - Beaume P.
AU - Kaafar M.
PY - 2019
SP - 211
EP - 219
DO - 10.5220/0007834602110219