3 papers
cs.CR2026
DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defense
Trung V. Phan, Tri Gia Nguyen, Thomas Bauschert
Advanced Persistent Threats (APTs) are difficult to detect and interpret due to their multi-stage and stealthy nature. While recent autonomous defense systems leverage provenance g…
cs.CR2026
DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns
Trung V. Phan, Tri Gia Nguyen, Thomas Bauschert
This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environ…
cs.CR2026
DeepXplain: XAI-Guided Autonomous Defense Against Multi-Stage APT Campaigns
Trung V. Phan, Thomas Bauschert
Advanced Persistent Threats (APTs) are stealthy, multi-stage attacks that require adaptive and timely defense. While deep reinforcement learning (DRL) enables autonomous cyber defe…