Open AccessAn Android Malware Detection System Based on Feature Fusion
Author(s) -
Li Jian,
Wang Zheng,
Wang Tao,
Tang Jinghao,
Yang Yuguang,
Zhou Yihua
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.09.008
Subject(s) - malware , android malware , computer science , android (operating system) , feature selection , artificial intelligence , random forest , data mining , curse of dimensionality , pattern recognition (psychology) , machine learning , computer security , operating system
In order to improve the detection efficiency of Android malicious application, an Android malware detection system based on feature fusion is proposed on three levels. Feature fusion especially emphasizes on ten categories, which combines static and dynamic features and includes 377 features for classification. In order to improve the accuracy of malware detection, attribute subset selection and principle component analysis are used to reduce the dimensionality of fusion features. Random forest is used for classification. In the experiment, the dataset includes 43,822 benign applications and 8,454 malicious applications. The method can achieve 99.4% detection accuracy and 0.6% false positive rate. The experimental results show that the detection method can improve the malware detection efficiency in Android platform.