Droid Detector:Android Malware Characterization and Detection Using Deep Learning

查看全文 作　　者：Zhenlong [1]Yuan;Yongqiang [2]Lu;Yibo [3]Xue

机构地区：[1]Department of Automation and Research Institute of Information Technology (RIIT),Tsinghua University;[2]Department of Antivirus,Baidu Inc.;[3]Research Institute of Information Technology (RIIT) and Tsinghua National Lab for Information Science and Technology (TNList),Tsinghua University

出　　处：《Tsinghua Science and Technology》索引2016年第21卷第1期,共10页

摘　　要：Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine(Droid Detector) that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection. 关键词：机器学习检测引擎机器人探测器软件特性移动操作系统恶意软件应用程序

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Droid Detector:Android Malware Characterization and Detection Using Deep Learning

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