{
  "id": 12623,
  "url": "https://arxiv.org/abs/2607.18479v1",
  "title": "Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection",
  "summary": "Malicious anomalous activity detection is a fundamental challenge for cyber security systems. Both tensor decomposition under statistical framework with CANDECOMP-PARAFAC alternating Poisson regression (CP-APR) and normalizing flows have proven to be powerful unsupervised machine learning methods that model multi-dimensional data and capture complex and multi-faceted details of behavior profiles in cyber security applications. In this study, we propose Hybrid Latent-Structural Fusion (HLSF), a w",
  "authors": "Dorianis M. Perez, Maksim E. Eren, Bryan E. Kaiser",
  "category": "research",
  "topics": "military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-20T19:57:21.000Z",
  "fetched_at": "2026-07-22T05:10:49.469Z",
  "source_slug": "x-arxiv-red-teaming-query",
  "source_name": "arXiv red teaming query",
  "source_homepage": "https://arxiv.org/a/redteam",
  "ethics_ai_record_url": "https://ethics.ai/record/12623",
  "original_url": "https://arxiv.org/abs/2607.18479v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}