IEICE TRANSACTIONS on Information
Android Malware Detection Based on Functional Classification
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Wenhao FAN
Beijing University of Posts and Telecommunications
Dong LIU
Beijing University of Posts and Telecommunications
Fan WU
Beijing University of Posts and Telecommunications
Bihua TANG
Beijing University of Posts and Telecommunications
Yuan'an LIU
Beijing University of Posts and Telecommunications
Keyword
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Wenhao FAN, Dong LIU, Fan WU, Bihua TANG, Yuan'an LIU, "Android Malware Detection Based on Functional Classification" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 3, pp. 656-666, March 2022, doi: 10.1587/transinf.2021EDP7133.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7133/_p
Copy
@ARTICLE{e105-d_3_656,
author={Wenhao FAN, Dong LIU, Fan WU, Bihua TANG, Yuan'an LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Android Malware Detection Based on Functional Classification},
year={2022},
volume={E105-D},
number={3},
pages={656-666},
abstract={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.},
keywords={},
doi={10.1587/transinf.2021EDP7133},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Android Malware Detection Based on Functional Classification
T2 - IEICE TRANSACTIONS on Information
SP - 656
EP - 666
AU - Wenhao FAN
AU - Dong LIU
AU - Fan WU
AU - Bihua TANG
AU - Yuan'an LIU
PY - 2022
DO - 10.1587/transinf.2021EDP7133
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2022
AB - 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.
ER -
English
