The convergence of unmanned aerial systems, industrial Internet of Things (IIoT) devices, edge computing nodes, and cyber-physical infrastructure has created heterogeneous communication environments in which conventional intrusion detection approaches face substantial challenges. Differences in traffic characteristics, computational capabilities, communication protocols, and attack manifestations make multi-class cyberattack detection particularly difficult. This paper presents a conceptual explainable deep ensemble learning framework for detecting and interpreting cyberattacks across heterogeneous drone–industrial IoT networks. The proposed methodology integrates complementary deep learning architectures through an ensemble decision mechanism and incorporates explainability to associate predictions with influential network characteristics. The theoretical design is informed by the provided literature on deep convolutional, recurrent, multimodal, and ensemble-oriented learning, while the principal cybersecurity positioning is grounded in the supplied study on explainable ensemble intrusion detection for heterogeneous drone and industrial networks (Islam et al., 2026). The framework incorporates heterogeneous data normalization, feature representation, parallel deep learners, confidence-aware ensemble fusion, multi-class prediction, and post-hoc explanation. Rather than treating detection accuracy as the sole objective, the proposed approach considers interpretability, cross-domain robustness, computational efficiency, and operational trustworthiness. Analytical findings indicate that ensemble architectures can theoretically mitigate weaknesses associated with individual deep learners, while explainability can improve the usefulness of predictions for security analysts. However, the framework also introduces computational and interpretability trade-offs that require empirical validation. The paper establishes a research-oriented architecture for explainable multi-class intrusion detection in drone–IIoT environments and identifies future requirements for benchmark construction, cross-domain validation, real-time deployment, and explanation fidelity.