Abstract: In unsupervised graph anomaly detection, existing methods usually focus on detecting outliers by learning local context information of nodes, while often ignoring the importance of global ...
security-log-correlation-engine/ │ ├── logs/ │ ├── firewall_logs.json │ ├── auth_logs.json │ └── system_logs.json │ ├── rules ...
Abstract: We introduce a new attention mechanism, dubbed structural self-attention (StructSA), that leverages rich correlation patterns naturally emerging in key-query interactions of attention.
When cross-asset correlations break down, which assets decouple first and which follow — and can we identify systemic stress regimes in real time? An end-to-end data engineering project that ingests ...
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