DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defense

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 graphs and learning-based models for detection and mitigation, their outputs remain largely machine-oriented and difficu...

DeepStage: Learning Autonomous Defense Policies against Multi-Stage APT Campaigns

This paper presents DeepStage, a deep reinforcement learning DRL framework for adaptive, stage-aware defense against Advanced Persistent Threats APTs. The enterprise environment is modeled as a partially observable Markov decision process POMDP, where host provenance and network telemetry are fus...