DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns
This addresses the challenge of autonomous cyber defense for enterprises, though it builds incrementally on prior work.
The paper tackles the problem of adaptive defense against multi-stage Advanced Persistent Threats (APTs) by proposing DeepStage, a deep reinforcement learning framework that achieves a stage-weighted F1-score of 0.89, outperforming a baseline by 21.9%.
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 fused into unified provenance graphs. Building on our prior work, StageFinder, a graph neural encoder and an LSTM-based stage estimator infer probabilistic attacker stages aligned with the MITRE ATT&CK framework. These stage beliefs, combined with graph embeddings, guide a hierarchical Proximal Policy Optimization (PPO) agent that selects defense actions across monitoring, access control, containment, and remediation. Evaluated in a realistic enterprise testbed using CALDERA-driven APT playbooks, DeepStage achieves a stage-weighted F1-score of 0.89, outperforming a risk-aware DRL baseline by 21.9%. The results demonstrate effective stage-aware and cost-efficient autonomous cyber defense.