基于深度学习的Android恶意软件检测:成果与挑战

Android Malware Detection Based on Deep Learning:Achievements and Challenges

随着Android应用的广泛使用,Android恶意软件数量迅速增长,对用户的财产、隐私等造成的安全威胁越来越严重。近年来基于深度学习的Android恶意软件检测成为了当前安全领域的研究热点。该文分别从数据采集、应用特征、网络结构、效果检测4个方面,对该研究方向已有的学术成果进行了分析与总结,讨论了它们的局限性与所面临的挑战,并就该方向未来的研究重点进行了展望。

With the prosperous of Android applications,Android malware has been scattered everywhere,which raises the serious security risk to users.On the other hand,the rapid developing of deep learning fires the combat between the two sides of malware detection.Inducing deep learning technologies into Android malware detection becomes the hottest topic of society.This paper summarizes the existing achievements of malware detection from four aspects:Data collection,feature construction,network structure and detection performance.Finally,the current limitations and facing challenges followed by the future researches are discussed.