DeepStage learns stage-aware autonomous defense policies for APTs by combining graph neural network embeddings with LSTM-based stage inference in a POMDP and training a hierarchical PPO agent, reporting 0.887 F1-score and 84.7% mitigation success in a CALDERA testbed.
A survey of intrusion detection systems leveraging host data
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StageFinder fuses graph neural networks on provenance data with LSTMs to estimate APT attack stages aligned with MITRE ATT&CK, achieving 0.96 macro F1 on DARPA datasets.
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DeepStage: Learning Autonomous Defense Policies Against Multi-Stage APT Campaigns
DeepStage learns stage-aware autonomous defense policies for APTs by combining graph neural network embeddings with LSTM-based stage inference in a POMDP and training a hierarchical PPO agent, reporting 0.887 F1-score and 84.7% mitigation success in a CALDERA testbed.
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Learning the APT Kill Chain: Temporal Reasoning over Provenance Data for Attack Stage Estimation
StageFinder fuses graph neural networks on provenance data with LSTMs to estimate APT attack stages aligned with MITRE ATT&CK, achieving 0.96 macro F1 on DARPA datasets.