{
  "id": 6890,
  "url": "https://arxiv.org/abs/2603.21296v3",
  "title": "DeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns",
  "summary": "Advanced Persistent Threats (APTs) are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning (DRL) enables autonomous cyber defense, its decisions are often opaque and difficult to trust in operational environments. This paper presents DeepXplain, an explainable DRL framework for stage-aware APT defense. Building on our prior DeepStage model, DeepXplain integrates provenance-based graph learning, temporal stage estimation, and a unified XAI pip",
  "authors": "Trung V. Phan, Thomas Bauschert",
  "category": "research",
  "topics": "military-security,transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-22T15:36:51.000Z",
  "fetched_at": "2026-07-14T16:32:50.144Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6890",
  "original_url": "https://arxiv.org/abs/2603.21296v3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}