{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:23:19Z","timestamp":1773840199029,"version":"3.50.1"},"reference-count":20,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"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":["61601113"],"award-info":[{"award-number":["61601113"]}],"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":["2018YFB2202200"],"award-info":[{"award-number":["2018YFB2202200"]}],"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":["2018YFB2100403"],"award-info":[{"award-number":["2018YFB2100403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["61601113"],"award-info":[{"award-number":["61601113"]}]},{"name":"National Key R&D Program of China","award":["2018YFB2202200"],"award-info":[{"award-number":["2018YFB2202200"]}]},{"name":"National Key R&D Program of China","award":["2018YFB2100403"],"award-info":[{"award-number":["2018YFB2100403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,7,28]]},"abstract":"<jats:p>Since a growing number of malicious applications attempt to steal users\u2019 private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.<\/jats:p>","DOI":"10.1155\/2021\/6689486","type":"journal-article","created":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T20:50:10Z","timestamp":1627591810000},"page":"1-12","source":"Crossref","is-referenced-by-count":25,"title":["Permission Sensitivity-Based Malicious Application Detection for Android"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-6126","authenticated-orcid":true,"given":"Yubo","family":"Song","sequence":"first","affiliation":[{"name":"Key Laboratory of Computer Networking Technology of Jiangsu Province, School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"Purple Mountain Laboratories, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijin","family":"Geng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer Networking Technology of Jiangsu Province, School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"Purple Mountain Laboratories, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"National Mobile Communications Research Laboratory, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shang","family":"Gao","sequence":"additional","affiliation":[{"name":"Computing Department, The Hong Kong Polytheistic University, Hung Hom, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer Networking Technology of Jiangsu Province, School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"Purple Mountain Laboratories, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.31209\/2019.100000079"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/wcsp.2017.8171036"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1145\/2046614.2046619"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/cyberc.2016.42"},{"key":"5","doi-asserted-by":"crossref","article-title":"Static based classification of malicious software using machine learning methods","author":"A. 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