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
Sistem pengendalian Android menduduki bahagian yang tinggi dalam pasaran terminal mudah alih. Ia menggalakkan perkembangan pesat aplikasi (aplikasi) Android. Walau bagaimanapun, kemunculan perisian hasad Android amat membahayakan keselamatan pengguna telefon pintar Android. Kerja penyelidikan sedia ada telah mencadangkan banyak kaedah untuk pengesanan perisian hasad Android, tetapi mereka tidak menjadikan penggunaan maklumat kategori fungsi apl supaya persamaan yang kukuh antara apl jinak dalam kategori fungsi yang sama diabaikan. Dalam makalah ini, kami mencadangkan skim pengesanan perisian hasad Android berdasarkan klasifikasi berfungsi. Apl jinak dalam kategori fungsi yang sama lebih serupa antara satu sama lain, jadi kami boleh menggunakan kurang ciri untuk mengesan perisian hasad dan meningkatkan ketepatan pengesanan dalam kategori fungsi yang sama. Matlamat skim kami adalah untuk menyediakan kaedah pengelasan fungsi aplikasi automatik dengan ketepatan yang tinggi. Kami mereka bentuk kaedah pengelasan berfungsi aplikasi Android yang diilhamkan oleh algoritma carian topik teraruh hiperpautan (HITS). Menggunakan hasil pengelasan automatik, kami selanjutnya mereka bentuk kaedah pengesanan perisian hasad berdasarkan persamaan apl dalam kategori fungsi yang sama. Kami menggunakan apl jinak daripada Gedung Google Play dan menggunakan apl perisian hasad daripada set perisian hasad Drebin untuk menilai skim kami. Keputusan percubaan menunjukkan bahawa kaedah kami boleh meningkatkan ketepatan pengesanan perisian hasad dengan berkesan.
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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Salinan
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
Salinan
@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},}
Salinan
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 -