The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
안드로이드 운영체제는 모바일 단말기 시장에서 높은 점유율을 차지하고 있다. 안드로이드 애플리케이션(앱)의 빠른 개발을 촉진합니다. 그러나 안드로이드 악성코드의 출현은 안드로이드 스마트폰 사용자의 보안을 크게 위협하고 있다. 기존 연구에서는 안드로이드 악성코드 탐지를 위한 다양한 방법을 제안해 왔지만, 동일한 기능 카테고리에 있는 양성 앱 간의 강한 유사성을 무시할 정도로 앱의 기능 카테고리 정보를 활용하지 못했습니다. 본 논문에서는 기능 분류를 기반으로 한 안드로이드 악성코드 탐지 기법을 제안한다. 동일한 기능 카테고리에 있는 양성 앱은 서로 더 유사하므로 더 적은 기능을 사용하여 맬웨어를 탐지하고 동일한 기능 카테고리에서 탐지 정확도를 높일 수 있습니다. 우리 계획의 목표는 높은 정확도의 자동 응용 기능 분류 방법을 제공하는 것입니다. 우리는 HITS(Hyperlink Induced Topic Search) 알고리즘에서 영감을 받아 Android 애플리케이션 기능 분류 방법을 설계합니다. 자동 분류 결과를 활용하여 동일한 기능 카테고리 내 앱 유사성을 기반으로 악성코드 탐지 방법을 추가로 설계합니다. 우리는 Google Play 스토어의 양성 앱을 사용하고 Drebin 악성코드 세트의 악성코드 앱을 사용하여 우리의 계획을 평가합니다. 실험 결과는 우리의 방법이 악성 코드 탐지의 정확성을 효과적으로 향상시킬 수 있음을 보여줍니다.
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
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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
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@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},}
부
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 -