Impact of Dataset Representation on Smartphone Malware Detection Performance
作者： Abdelfattah Amamra , Chamseddine Talhi , Jean-Marc Robert
DOI: 10.1007/978-3-642-38323-6_12
关键词: Machine learning 、 Android (operating system) 、 Malware 、 Intrusion detection system 、 System call 、 Data mining 、 Classifier (UML) 、 Computation complexity 、 Computer science 、 Potential impact 、 Detection performance 、 Artificial intelligence
摘要: Improving Smartphone anomaly-based malware detection techniques is widely studied in recent years. Previous studies explore three factors: dataset size, type and normal profile model. These factors improve the performance, but increase computation complexity required memory space. In this paper we a new factor: representation. Dataset representation format adopted to organize represent data. To investigate impact of factor, examine four machine learning classifiers with different representations. Those representations are: successive system calls, bag calls patterns frequency calls. The used collection call traces executing Android 2.2. We analyse performance each classifier deduce influence on accuracy false positive rates. results show that has potential low computational cost.
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