DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns
📰 ArXiv cs.AI
Learn how DeepStage uses deep reinforcement learning to defend against multi-stage APT campaigns by adapting to evolving threats
Action Steps
- Formulate the enterprise environment as a partially observable Markov decision process (POMDP)
- Fuse host provenance and network telemetry into unified provenance graphs
- Employ a graph neural network encoder to analyze the provenance graphs
- Train a deep reinforcement learning model to learn adaptive defense policies
- Test and evaluate the effectiveness of the learned defense policies against multi-stage APT campaigns
Who Needs to Know This
Security teams and researchers can benefit from DeepStage's autonomous defense policies to enhance their threat detection and response capabilities
Key Insight
💡 DeepStage's use of graph neural networks and deep reinforcement learning enables autonomous and stage-aware defense against evolving threats
Share This
🚀 Introducing DeepStage: a DRL framework for adaptive defense against APT campaigns #cybersecurity #AI
Key Takeaways
Learn how DeepStage uses deep reinforcement learning to defend against multi-stage APT campaigns by adapting to evolving threats
Full Article
Title: DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns
Abstract:
arXiv:2603.16969v2 Announce Type: replace-cross Abstract: This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environment is formulated as a partially observable Markov decision process (POMDP), in which host provenance and network telemetry are fused into unified provenance graphs. Building on our prior work (StageFinder), DeepStage employs a graph neural network encoder and
Abstract:
arXiv:2603.16969v2 Announce Type: replace-cross Abstract: This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive and stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environment is formulated as a partially observable Markov decision process (POMDP), in which host provenance and network telemetry are fused into unified provenance graphs. Building on our prior work (StageFinder), DeepStage employs a graph neural network encoder and
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