The convergence of unmanned aerial vehicles (UAVs), industrial Internet of Things (IIoT) devices, wireless sensor networks, and networked control infrastructures has created highly heterogeneous communication environments in which anomalous and malicious activities may propagate across multiple layers. Conventional intrusion detection approaches that rely on a single classifier frequently face difficulties in distinguishing multiple attack classes under heterogeneous traffic, particularly when communication characteristics, payload structures, and network relationships vary considerably. This paper proposes a conceptual hybrid explainable artificial intelligence (XAI) framework for multi-class anomaly and intrusion detection in UAV and IIoT networks. The framework combines heterogeneous traffic preprocessing, feature-level representation, hybrid ensemble classification, anomaly scoring, and post-hoc explanation into a unified detection pipeline. Its theoretical foundation draws upon the security properties of network coding, secure wireless communication, image-quality assessment, and representation-oriented information processing discussed in the supplied literature. In particular, secure network coding studies establish the importance of protecting information flows and communication structures, while research on opportunistic relaying and physical-layer secrecy highlights the security implications of wireless communication conditions (Cai and Yeung, 2002; Khan and Chatzigeorgiou, 2017; Tajbakhsh et al., 2018). The proposed architecture is designed to distinguish multiple attack categories while simultaneously providing human-interpretable explanations concerning influential features, confidence, and predicted attack class. The resulting framework offers a theoretically grounded direction for security monitoring in heterogeneous UAV-IIoT environments, although empirical validation using representative multi-class datasets remains necessary before operational deployment.