arXiv:2609.25256v1 Announce Type: new Abstract: Graph-based Android malware classifiers can lose accuracy under malware-type or family shifts. We test whether mesoscopic organization in function-call graphs provides shift-stable information beyond local degree profiles (LDP), global statistics, lightweight metadata, and size-matched random partitions.
Partition-Matched Evaluation of Community Features under Distribution Shift in Android Malware Function-Call Graphs
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