Android Malware Detection Using Local Binary Pattern and Principal Component Analysis
作者机构：College of Computer Science and Electronic EngineeringHunan UniversityChangshaHunanChina Hunan Key Laboratory of Big Data Research and ApplicationChangshaHunanChina Hunan Institute of Metrology and TestChangshaChina
出 版 物：《国际计算机前沿大会会议论文集》 (International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）)
年 卷 期：2017年第1期
页 面：63-66页
主 题：Android malware detection Binary texture feature Local binary pattern Principal component analysis
摘 要：Nowadays,analysis methods based on big data have been widely used in malicious software *** Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers *** to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy *** this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware ***,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this *** on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.
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