INTEL_REPORT
arXiv — Cryptography & Security (cs.CR) · published 5/19/2026, 4:00:00 AM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
Universal Graph Backdoor Defense: A Feature-based Homophily Perspective arXiv:2605.16815v1 Announce Type: new Abstract: Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) poses a significant barrier to broader adoption in high-stakes applications. Despite recent advances in graph backdoor defense (GBD), existing methods primarily focus on subgraph-based GBAs, relying on the assum…
https://arxiv.org/abs/2605.16815
sha256:ed0f8d23e82ced36fce933c03811aa00beb48a0b8774be07c8bdd832cf59e197
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Indicators
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Malware families
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Threat actors mentioned
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ATT&CK techniques
MITRE IDs referenced in text and present in local technique table.
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