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

• URL: http://arxiv.org/abs/2603.16969v1
• Date: Tue, 17 Mar 2026 09:46:11 GMT
• Title: DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns
• Abstract summary: This paper presents DeepStage, a framework for adaptive, stage-aware defense against Advanced Threats (APTs)<n>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.<n>DeepStage achieves a stage-weighted F1-score of 0.89, outperforming a risk-aware DRL baseline by 21.9%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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