Application of Symbolic Classifiers and Multi-Ensemble Threshold Techniques for Android Malware Dete...
Application of Symbolic Classifiers and Multi-Ensemble Threshold Techniques for Android Malware Detection
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Android malware detection using artificial intelligence today is a mandatory tool to prevent cyber attacks. To address this problem in this paper the proposed methodology consists of the application of genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect if the android is malware or not. To find the op...
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Full title
Application of Symbolic Classifiers and Multi-Ensemble Threshold Techniques for Android Malware Detection
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TN_cdi_doaj_primary_oai_doaj_org_article_9456b9387ae9403683d30f606916bc10
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9456b9387ae9403683d30f606916bc10
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ISSN
2504-2289
E-ISSN
2504-2289
DOI
10.3390/bdcc9020027
