  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.
