ABSTRACT The rapid growth of Internet of things (IoT) deployments has intensified the need for effective and reliable intrusion detection systems capable of operating under heterogeneous and resource‐constrained environments. In response, deep learning (DL)‐based intrusion detection systems have been increasingly explored across IoT application domains. This study presents a PRISMA guided systematic review with descriptive meta‐analysis of recent DL‐based intrusion detection research in IoT environments, focusing on architectural trends, evaluation practices, deployment considerations and trust‐related properties. A total of 42 primary empirical studies published between 2021 and early 2026 were identified through systematic searches across major scholarly databases and analysed using standardized inclusion criteria and data extraction protocols. Instead of statistically aggregating heterogeneous results, the review employs descriptive meta‐analytic synthesis to examine patterns in model architectures, learning paradigms, deployment orientations and evaluation metrics. The analysis reveals a gradual shift from centralised DL models towards hybrid architectures, federated learning frameworks and resource‐aware designs suitable for edge or fog environments. Federated and privacy‐preserving learning approaches demonstrate promising detection performance while maintaining data locality, whereas explainable AI techniques are incorporated in only a limited subset of current IoT intrusion detection systems. The results also highlight a persistent gap between benchmark‐driven evaluation practices and the operational requirements of IoT deployments. Future studies should therefore adopt evaluation practices that jointly consider detection effectiveness, computational cost, privacy preservation, interpretability and deployment feasibility.
Kumar et al. (Thu,) studied this question.