Intrusion Detection (ID) in the Internet of Secure Things (IoST) has become increasingly critical due to the rising frequency and sophistication of cyber-attacks, which can lead to severe consequences such as data breaches, financial losses, and service disruptions. These risks are further intensified in computationally limited environments, where limited computational capacity and rapidly evolving threats make accurate and efficient detection challenging. In this study, a data-efficient ID framework tailored for resource-constrained environments is proposed by leveraging active learning and meta-heuristic optimization techniques. The proposed framework systematically addresses three critical limitations commonly observed in traditional models: data imbalance, inefficient hyperparameter tuning, and dependency on large labeled datasets. Initially, to mitigate class imbalance, adaptive synthetic sampling generates synthetic instances for minority classes, thereby enhancing learning in complex regions of the feature space. Next, for hyperparameter optimization, the Sandpiper Optimization (SO) algorithm fine-tunes the regularization parameter of Logistic Regression (LR), yielding significant improvements in model generalization. Finally, the challenge of limited labeled data is addressed through two active learning strategies: Active Learning Uncertainty-based (ALU) and Active Learning Entropy-based (ALE). These strategies selectively query the most informative samples from the unlabeled pool, ensuring maximum learning with minimal annotation effort. The performance of the proposed models is evaluated on two benchmark datasets: the wireless sensor networks and network intrusion detection datasets. Simulation results demonstrate that proposed models outperform base model LR. LRALE achieves improvements of 10.48% and 3.16% in accuracy, 19.48% and 3.16% in recall, and 7.23% and 1.04% in F1-score on WSN-DS and CIC-IDS-DS datasets, respectively. LRALU shows improvements of 18.18% and 2.11% in accuracy, 18.18% and 2.11% in recall, and 14.63% and 2.08% in Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). Similarly, LRSO achieves improvements of 9.09% and 2.11% in accuracy, 9.09% and 1.05% in recall, and 9.76% and 3.12% in ROC-AUC on WSN-DS and CIC-IDS-DS datasets, respectively. To ensure model generalization and stability across different data partitions, a rigorous 10-fold cross-validation is conducted. Model interpretability is further enhanced using eXplainable artificial intelligence techniques, including Local interpretable model-agnostic explanations and Shapley additive explanations, to elucidate feature contributions and improve transparency. Additionally, statistical significance testing through paired t-tests confirms the robustness and reliability of the proposed models. Overall, this framework introduces a comprehensive, annotation-efficient, and transparent ID solution that significantly advances the domain, making it well-suited for practical deployment in IoSTs environments.