Android Malware Detection Based on Functional Classification

Wenhao FAN
Dong LIU
Fan WU
Bihua TANG
Yuan'an LIU

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.3    pp.656-666
Publication Date: 2022/03/01
Publicized: 2021/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2021EDP7133
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Android,  malware detection,  functional classification,  mobile security,  HITS algorithm,  

Full Text: PDF(2.2MB)>>
Buy this Article

Android operating system occupies a high share in the mobile terminal market. It promotes the rapid development of Android applications (apps). However, the emergence of Android malware greatly endangers the security of Android smartphone users. Existing research works have proposed a lot of methods for Android malware detection, but they did not make the utilization of apps' functional category information so that the strong similarity between benign apps in the same functional category is ignored. In this paper, we propose an Android malware detection scheme based on the functional classification. The benign apps in the same functional category are more similar to each other, so we can use less features to detect malware and improve the detection accuracy in the same functional category. The aim of our scheme is to provide an automatic application functional classification method with high accuracy. We design an Android application functional classification method inspired by the hyperlink induced topic search (HITS) algorithm. Using the results of automatic classification, we further design a malware detection method based on app similarity in the same functional category. We use benign apps from the Google Play Store and use malware apps from the Drebin malware set to evaluate our scheme. The experimental results show that our method can effectively improve the accuracy of malware detection.

open access publishing via