DeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns
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 pipeline that provides structural, temporal, and policy-level explanations. Unlike post-hoc methods, explanation signals are incorporated directly into policy optimization through evidence alignment a...
| Authors | Trung V. Phan, Thomas Bauschert |
|---|---|
| Primary category | cs.CR |
| Published | 22 March 2026 |
| Last updated at source | 25 March 2026 |
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