论文标题
MALCEIVER：具有分层和多模式特征的感知器，用于Android恶意软件检测
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection
论文作者
论文摘要
我们提出了使用多模式特征的Android恶意软件检测的分层感知器模型Malceiver。主要输入是opcode序列和给定的Android APK文件的请求权限。为了达到恶意软件分类决策，模型将从OpCode序列提取的层次功能与请求的权限结合在一起。该模型的体系结构基于感知者/感知者，该构建允许有效地处理很长的OpCode序列。我们提出的模型可以轻松扩展到使用多模式功能。我们通过实验表明，该模型的表现优于基于OpCode序列的恶意软件检测的常规CNN体系结构。然后，我们证明，使用其他方式可以提高性能。我们提出的架构为在恶意软件研究中使用变压器式网络开辟了新的途径。
We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features. The primary inputs are the opcode sequence and the requested permissions of a given Android APK file. To reach a malware classification decision the model combines hierarchical features extracted from the opcode sequence together with the requested permissions. The model's architecture is based on the Perceiver/PerceiverIO which allows for very long opcode sequences to be processed efficiently. Our proposed model can be easily extended to use multi-modal features. We show experimentally that this model outperforms a conventional CNN architecture for opcode sequence based malware detection. We then show that using additional modalities improves performance. Our proposed architecture opens new avenues for the use of Transformer-style networks in malware research.
