Deep Recurrent Hidden Markov Learning Framework for Multi-Stage Advanced Persistent Threat Prediction

2026-01-11

arXiv.org

AI Summary

This work addresses the challenge of stage-aware prediction for advanced persistent threats (APTs), which are multi-stage, stealthy, and difficult for existing intrusion detection systems to detect—particularly under sparse or incomplete observations where robust reasoning is lacking. The authors propose E-HiDNet, a novel framework that uniquely integrates deep semantic feature learning with probabilistic state-space modeling. It jointly employs CNNs and RNNs to extract spatiotemporal features from alert sequences and leverages a hidden Markov model to characterize latent APT attack stages and their stochastic transitions. An enhanced Viterbi algorithm enables uncertainty-aware inference even with incomplete observations. Evaluated on the S-DAPT-2026 dataset, the method achieves 98.8%–100% stage prediction accuracy when at least four observations are available, significantly outperforming conventional HMMs and maintaining high robustness under reduced training data.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Advanced Persistent Threats (APTs) represent hidden, multi\-stage cyberattacks whose long term persistence and adaptive behavior challenge conventional intrusion detection systems (IDS). Although recent advances in machine learning and probabilistic modeling have improved APT detection performance, most existing approaches remain reactive and alert\-centric, providing limited capability for stage-aware prediction and principled inference under uncertainty, particularly when observations are sparse or incomplete. This paper proposes E\-HiDNet, a unified hybrid deep probabilistic learning framework that integrates convolutional and recurrent neural networks with a Hidden Markov Model (HMM) to allow accurate prediction of the progression of the APT campaign. The deep learning component extracts hierarchical spatio\-temporal representations from correlated alert sequences, while the HMM models latent attack stages and their stochastic transitions, allowing principled inference under uncertainty and partial observability. A modified Viterbi algorithm is introduced to handle incomplete observations, ensuring robust decoding under uncertainty. The framework is evaluated using a synthetically generated yet structurally realistic APT dataset (S\-DAPT\-2026). Simulation results show that E\-HiDNet achieves up to 98.8\-100\% accuracy in stage prediction and significantly outperforms standalone HMMs when four or more observations are available, even under reduced training data scenarios. These findings highlight that combining deep semantic feature learning with probabilistic state\-space modeling enhances predictive APT stage performance and situational awareness for proactive APT defense.

Problem

• Advanced Persistent Threats
• multi-stage prediction
• uncertainty inference
• partial observability
• intrusion detection

Innovation

• Deep Probabilistic Learning
• Hidden Markov Model
• Recurrent Neural Network
• APT Stage Prediction
• Uncertainty-aware Inference