DaDiDroid: an obfuscation resilient tool for detecting Android malware via weighted directed call graph modelling

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 16th International Joint Conference on e-Business and Telecommunications
Subtitle of host publicationVolume 2: SECRYPT
EditorsMohammad S. Obaidat, Pierangela Samarati
Place of PublicationSetúbal
PublisherSciTePress
Pages211-219
Number of pages9
Volume2
ISBN (Electronic)9789897583780
DOIs
Publication statusPublished - 2019
Event16th International Joint Conference on e-Business and Telecommunications, ICETE 2019 - Prague, Czech Republic
Duration: 26 Jul 201928 Jul 2019

Conference

Conference16th International Joint Conference on e-Business and Telecommunications, ICETE 2019
CountryCzech Republic
CityPrague
Period26/07/1928/07/19

Keywords

  • Android
  • Machine Learning
  • Malware
  • Mobile Apps
  • Obfuscation

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