| Summary: | 碩士 === 國立雲林科技大學 === 資訊管理系碩士班 === 99 === Mobile device are getting increasingly popular, it has become a trend in
communication industry, and thus several malwares appeared targeting smartphone.
At present, the countermeasures to malware on smartphone are limited to
signature-based solutions which efficiently detect known malware, but they have
serious drawback that cannot detect malware variants and usually need a large
database. In order to solve above problems, we propose a malware detection
mechanism which uses Document object model to analyze application‟s behavior on
mobile device to improve the problem of traditional detection system. In the
experimental stage, we used 100 benign and 47 malwares for evaluation and apply
nine data mining algorithms to training classifier, using our proposed feature extract approach. The experimental result shows that our proposed detection mechanism not only detects malware proactive and high accuracy but also the performance of classifiers that using our extracted feature is better than permission-based.
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