Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection
This addresses malware detection for Android security, but it is incremental as it adapts an existing Perceiver architecture to this domain.
The authors tackled Android malware detection by proposing Malceiver, a hierarchical Perceiver model that uses multi-modal features like opcode sequences and permissions, and showed it outperforms a conventional CNN and improves with additional modalities.
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.