{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T09:05:03Z","timestamp":1768640703677,"version":"3.49.0"},"reference-count":47,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T00:00:00Z","timestamp":1624060800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["0066\/2019\/AFJ"],"award-info":[{"award-number":["0066\/2019\/AFJ"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["MF2009"],"award-info":[{"award-number":["MF2009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Trusted Computing on Cross-Area Data","award":["0066\/2019\/AFJ"],"award-info":[{"award-number":["0066\/2019\/AFJ"]}]},{"name":"Trusted Computing on Cross-Area Data","award":["MF2009"],"award-info":[{"award-number":["MF2009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,6,19]]},"abstract":"<jats:p>As the number of Android malware applications continues to grow at a high rate, detecting malware to protect the system security and user privacy is becoming increasingly urgent. Each malware application belongs to a specific family, and there is a gap in the number of malware families. The accuracy of detection can be improved if malware family information is well utilized and certain strategies are adopted to balance the variability among samples. In addition, the performance of a base classifier is limited. If an ensemble classifier or an ensemble method can be adopted, the detection effect can be further improved. Therefore, this paper proposes a novel malware family-based bagging algorithm for Android malware detection, called FB2Droid, to perform malware detection. First, five features are extracted from the Android application package. Then, the relief feature selection algorithm is used for feature selection. Next, we designed two different sampling strategies based on different families of malware to alleviate the sample imbalance in the dataset. Combined with the two sampling strategies, the traditional bagging algorithm is improved to integrate the classifier. In the experiment, several classifiers were used to evaluate the proposed scheme. The experimental results show that the proposed sampling strategy and the improved bagging algorithm can effectively improve the detection accuracy of these classifiers.<\/jats:p>","DOI":"10.1155\/2021\/6642252","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T20:50:15Z","timestamp":1624308615000},"page":"1-13","source":"Crossref","is-referenced-by-count":4,"title":["FB2Droid: A Novel Malware Family-Based Bagging Algorithm for Android Malware Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5457-2846","authenticated-orcid":true,"given":"Ke","family":"Shao","sequence":"first","affiliation":[{"name":"City University Macau, Institute of Data Science, Macau 999078, China"},{"name":"School of Computer Technology, Beijing Institute of Technology Zhuhai, Zhuhai 519088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6861-2124","authenticated-orcid":true,"given":"Zhiming","family":"Cai","sequence":"additional","affiliation":[{"name":"City University Macau, Institute of Data Science, Macau 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"Mobile operating system market share world-wide: global status","year":"2019"},{"key":"2","article-title":"McAfee mobile threat report Q1, 2020 McAfee mobile threat report mobile malware is playing hide and steal","author":"McAfee","year":"2020"},{"key":"3","article-title":"New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM","volume":"101","author":"J. 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