MSNdroid:The Android Malware Detector Based On Multi-class Features and Deep Belief Network
Android operating system has become a very popular mobile op-erating platform.However,the popularity and openness of the Android has also made it a major target for malicious application developers.In recent years,many researchers have conducted re-search on Android malware detection,but almost all static analysis techniques focus on the analysis of Manifest.xml and java-layer code.This results many malware producers hide malicious code in the native-layer to evade existing detection techniques.There-fore,in this paper we innovatively add the feature of native-layer to the features data.We extract the corresponding native_apis fea-ture,combined with permissions feature and system_apis feature to form complete features.Using the Deep Belief Networks(DBN)algorithm,we achieve classification accuracy of 98.71%and false negative rate of 0.7%.To the best of our knowledge,this is the first study and MSNdroid is the first tool to apply deep learning to na-tive code features for Android malware detection.
Malware detection static analysis native code DBN
Xiaoxia Qin Fangping Zeng Yu Zhang
University of Science and Technology of China Shushan Qu,Hefei Shi,China
国际会议
2019国图灵大会(ACM Turing Celebration conference-China 2019 )
成都
英文
583-587
2019-05-17（万方平台首次上网日期，不代表论文的发表时间）