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Evidence record 7124 · automatically gathered

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

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 an LSTM-based stage estimator to infer probabilistic attac

Record details

Published: 17 March 2026
Source: arXiv
Category: Research
Topics: Military & security · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (17 March 2026), “DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns,” evidence record 7124, https://ethics.ai/record/7124 (originally published by arXiv).

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