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However, the existing identification methods either collect feature values from known APKs for inefficient comparative analysis, or use expensive program syntax or semantic analysis methods to extract features. Therefore, this paper proposes an APK static identification method that is different from traditional graph analysis. We match application programming interface (API) call graph to a complex network, and use a dual\u2010centrality analysis method to calculate the importance of sensitive nodes in the API call graph, while integrating the global and relative influence of sensitive nodes. Our key insight is that the dual\u2010centrality analysis method can more accurately characterize the graph semantic information of Android malicious APKs. We created and named a method\n                    <jats:italic>DCDroid<\/jats:italic>\n                    and evaluated it on a dataset of 4,428 benign samples and 4,626 malicious samples. The experimental results show that compared to the four advanced methods\n                    <jats:italic>Drebin<\/jats:italic>\n                    ,\n                    <jats:italic>MaMaDroid<\/jats:italic>\n                    ,\n                    <jats:italic>MalScan<\/jats:italic>\n                    , and\n                    <jats:italic>HomeDroid<\/jats:italic>\n                    ,\n                    <jats:italic>DCDroid<\/jats:italic>\n                    can identify Android malicious APKs with an accuracy of 97.5%, with an F1 value of 96.7% and is two times faster than\n                    <jats:italic>HomeDroid<\/jats:italic>\n                    , eight times faster than\n                    <jats:italic>Drebin<\/jats:italic>\n                    , and 17 times faster than\n                    <jats:italic>MaMaDroid<\/jats:italic>\n                    . We grabbed 10,000 APKs from the Google Play Market,\n                    <jats:italic>DCDroid<\/jats:italic>\n                    was able to find 68 malicious APKs, of which 67 were confirmed Android malicious APKs, with a good ability to identify market\u2010level malicious APKs.\n                  <\/jats:p>","DOI":"10.1049\/2024\/6652217","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T05:35:07Z","timestamp":1724045707000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DCDroid: An APK Static Identification Method Based on Na\u00efve Bayes Classifier and Dual\u2010Centrality Analysis"],"prefix":"10.1049","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7529-729X","authenticated-orcid":false,"given":"Lansheng","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"key":"e_1_2_11_1_2","unstructured":"StatCounter Google\u2019s android market share was about 72% Retrieved from https:\/\/gs.statcounter.com\/os-market-share\/mobile\/worldwide. 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