一种基于深度学习的强对抗性Android恶意代码检测方法

针对现有Android恶意代码检测方法容易被绕过的问题，提出了一种强对抗性的Android恶意代码检测方法.首先设计实现了动静态分析相结合的移动应用行为分析方法，该方法能够破除多种反分析技术的干扰，稳定可靠地提取移动应用的权限信息、防护信息和行为信息.然后，从上述信息中提取出能够抵御模拟攻击的能力特征和行为特征，并利用一个基于长短时记忆网络（Long Short-Term Memory，LSTM）的神经网络模型实现恶意代码检测.最后通过实验证明了本文所提出方法的可靠性和先进性.

A Robust Approach for Android Malware Detection Based on Deep Learning

Conventional Android malware detection method can easily be evaded.In this study,we propose a detection method of Android malicious code based on short-term memory network(LSTM),which makes malware more difficult to evade from detection.In this method,a program analysis framework that combines static and dynamic analysis is proposed at first to get the permission information,protection information and behavior information.Secondly,entrenched features such as ability features and behavior features are extracted from the information that provided by the program analysis framework.With the entrenched features,we design a malware detection method based on LSTM model to distinguish benign applications from the malicious ones.Experimental results demonstrate that our approach is more effective and robust in Android malware detection than the state-of-the-art methods.