Predicting the impact of android malicious samples via machine learning

Qiu, Junyang, Luo, Wei, Pan, Lei, Tai, Yonghang, Zhang, Jun and Xiang, Yang 2019, Predicting the impact of android malicious samples via machine learning, IEEE Access, pp. 1-14, doi: 10.1109/access.2019.2914311.

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Title Predicting the impact of android malicious samples via machine learning
Author(s) Qiu, JunyangORCID iD for Qiu, Junyang orcid.org/0000-0002-4711-7543
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Pan, LeiORCID iD for Pan, Lei orcid.org/0000-0002-4691-8330
Tai, Yonghang
Zhang, JunORCID iD for Zhang, Jun orcid.org/0000-0002-2189-7801
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Journal name IEEE Access
Start page 1
End page 14
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2019
ISSN 2169-3536
Keyword(s) Malware
deep neural network
high impact malicious samples
low impact malicious samples
static analysis
Summary Recently Android malicious samples threaten billions of the mobile end users’ security or privacy. The community researchers have designed many methods to automatically and accurately identify Android malware samples. However, the rapid increase of Android malicious samples outpowers the capabilities of traditional Android malware detectors and classifiers with respect to the cyber security risk management needs. It is important to identify the small proportion of Android malicious samples that may produce high cyber-security or privacy impact. In this paper, we propose a light-weight solution to automatically identify the Android malicious samples with high security and privacy impact. We manuallycheck a number of Android malware families and corresponding security incidents, and define two impact metrics for Android malicious samples. Our investigation results in a new Android malware dataset with impact ground truth (low impact or high impact). This new dataset is employed to empirically investigate the intrinsic characteristics of low impact as well as high impact malicious samples. To characterize and captureAndroid malicious samples’ pattern, the reverse engineering is performed to extract semantic features to represent malicious samples. The leveraged features are parsed from both the AndroidManifest.xml files aswell as the disassembled binary classes.dex codes. Then the extracted features are embedded into numerical vectors. Furthermore, we train highly accurate Support Vector Machine and Deep Neural Network classifiers to categorize the candidate Android malicious samples into low impact or high impact. The empirical results validate the effectiveness of our designed light-weight solution. This method can be further utilized foridentifying those high impact Android malicious samples in the wild.
Notes Early Access Article
Language eng
DOI 10.1109/access.2019.2914311
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121260

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Created: Fri, 03 May 2019, 10:30:12 EST

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