GRASP detects anomalies in system provenance graphs via self-supervised executable prediction from two-hop neighborhoods, outperforming prior PIDS on DARPA datasets by identifying all documented attacks where behaviors are learnable plus additional unlabeled suspicious activity.
FUDD: Threat Hunting Framework Utilizing Graph-Based Anomaly Detection on Log Data
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An agentic LLM workflow for 6G intent orchestration grounds translations in TMF service catalogs, validates with SHACL, and decomposes via constraint satisfaction and set cover, reporting 97% structured success and 26-point hallucination reduction.
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GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification
GRASP detects anomalies in system provenance graphs via self-supervised executable prediction from two-hop neighborhoods, outperforming prior PIDS on DARPA datasets by identifying all documented attacks where behaviors are learnable plus additional unlabeled suspicious activity.
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Intent-Driven 6G Service Orchestration: Grounded Translation, Validation, and Decomposition
An agentic LLM workflow for 6G intent orchestration grounds translations in TMF service catalogs, validates with SHACL, and decomposes via constraint satisfaction and set cover, reporting 97% structured success and 26-point hallucination reduction.