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Machine learning based hybrid behavior model for Android malware analysis
Date Issued
2014
Date
2014
Author(s)
Chuang, Hsin-Yu
Abstract
Malware analysis on the Android platform has been an important issue as the platform is prevalent. We proposed a detection approach based on a static analysis and machine learning techniques to obtain a considerably accurate Android malware classifier. By conducting SVM classifications on two different feature sets, malicious-preferred features and normal-preferred features, we built a hybrid-model classifier to improve the detection accuracy. With the consideration of normal behavior features, the ability of detecting unknown malwares can be improved. Our experiment shows that the accuracy is as high as 96.69% in predicting unknown applications. Further, the proposed approach can be applied to make confident decisions on labeling unknown applications. In our experiments, the proposed hybrid model classifier can label 79.4% applications without false positive and false negative occurred in the labeling process.
Subjects
惡意軟體
靜態分析
分類
Type
thesis
File(s)
No Thumbnail Available
Name
ntu-103-R01921019-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):2d5ac06ad2bd664f3b7437471ea0caa6