Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber security research. Deep learning models have many advantages over traditional Machine Learning (ML) models, particularly when there is a large amount of data available. Android malware detection or classification qualifies as a big data problem because of the fast booming number of Android malware, the obfuscation of Android malware, and the potential protection of huge values of data assets stored on the Android devices. It seems a natural choice to apply DL on Android malware detection. However, there exist challenges for researchers and practitioners, such as choice of DL architecture, feature extraction and processing, performance evaluation, and even gathering adequate data of high quality. In this survey, we aim to address the challenges by systematically reviewing the latest progress in DL-based Android malware detection and classification. We organize the literature according to the DL architecture, including FCN, CNN, RNN, DBN, AE, and hybrid models. The goal is to reveal the research frontier, with the focus on representing code semantics for Android malware detection. We also discuss the challenges in this emerging field and provide our view of future research opportunities and directions.

中文翻译：

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使用深度神经模型进行 Android 恶意软件检测的调查

深度学习 (DL) 是一种颠覆性技术，它改变了网络安全研究的格局。与传统机器学习 (ML) 模型相比，深度学习模型具有许多优势，尤其是在有大量可用数据的情况下。Android 恶意软件检测或分类属于大数据问题，因为 Android 恶意软件的数量迅速增加、Android 恶意软件的混淆以及对存储在 Android 设备上的大量数据资产的潜在保护。将深度学习应用于 Android 恶意软件检测似乎是一个自然的选择。然而，研究人员和从业者面临挑战，例如深度学习架构的选择、特征提取和处理、性能评估，甚至收集足够的高质量数据。在本次调查中，我们旨在通过系统回顾基于 DL 的 Android 恶意软件检测和分类的最新进展来应对挑战。我们根据 DL 架构组织文献，包括 FCN、CNN、RNN、DBN、AE 和混合模型。目标是揭示研究前沿，重点是表示 Android 恶意软件检测的代码语义。我们还讨论了这一新兴领域的挑战，并提供了我们对未来研究机会和方向的看法。