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.

翻译：高级持续性威胁（APT）表现为隐蔽的多阶段网络攻击，其长期持续性和自适应行为挑战了传统的入侵检测系统（IDS）。尽管机器学习和概率建模的最新进展提升了APT检测性能，但现有方法大多仍是被动的且以警报为中心，在不确定性条件下，特别是观测数据稀疏或不完整时，缺乏阶段感知预测和基于原理推理的能力。本文提出E-HiNet，一个统一的混合深度概率学习框架，它集成卷积神经网络、循环神经网络与隐马尔可夫模型（HMM），从而实现对APT攻击进程的精确预测。深度学习组件从关联的警报序列中提取层次化时空表示，而HMM则对潜攻击阶段及其随机转移进行建模，支持在不确定性和部分可观测性下进行基于原理的推理。本文引入改进的维特比算法处理不完整观测，确保在不确定性条件下的鲁棒解码。该框架使用一个通过合成生成但结构真实的APT数据集（S-DAPT-2026）进行评估。仿真结果表明，在四个或更多观测数据可用时，即使训练数据量减少，E-HiNet的阶段预测准确率仍可达98.8%-100%，且显著优于独立HMM。这些发现表明，将深度语义特征学习与概率状态空间建模相结合，能够提升APT阶段预测性能及情景感知能力，从而支持主动APT防御。