Enhancing Smartphone Malware Detection Performance by Applying Machine Learning Hybrid Classifiers
作者： Abdelfattah Amamra , Chamseddine Talhi , Jean-Marc Robert , Martin Hamiche
DOI: 10.1007/978-3-642-35267-6_17
关键词: Classifier (UML) 、 Machine learning 、 Phone 、 Malware 、 Detection performance 、 False positive rate 、 Thread (computing) 、 Computer science 、 Artificial intelligence
摘要: Significant increase and serious thread of Smartphone malwares has imposed adopting accurate malware detection solutions. In this paper, we investigate the performance machine learning individual classifiers possibility enhancing by introducing hybrid using stacking method. For purpose on Smartphone, are evaluated tested 100 most download normal free applications 90 available malicious traces. Those have been installed executed a HTC Dream phone. The metrics used to measure classifier accuracy false positive rate.
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