{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:41:19Z","timestamp":1767984079257,"version":"3.49.0"},"reference-count":17,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vietnam J. Comp. Sci."],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:p> Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection. <\/jats:p>","DOI":"10.1142\/s2196888820500086","type":"journal-article","created":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T07:45:01Z","timestamp":1580802301000},"page":"145-159","source":"Crossref","is-referenced-by-count":20,"title":["Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection"],"prefix":"10.1142","volume":"07","author":[{"given":"Md. Shohel","family":"Rana","sequence":"first","affiliation":[{"name":"School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, 118 College Drive, #5106 Hattiesburg, MS 39406, USA"}]},{"given":"Andrew H.","family":"Sung","sequence":"additional","affiliation":[{"name":"School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, 118 College Drive, #5106 Hattiesburg, MS 39406, USA"}]}],"member":"219","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"S2196888820500086BIB001","volume-title":"AndroidTM Hacker\u2019s Handbook","author":"Drake J. 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