For instance, ensemble methods such as AdaBoost [10] suffer from limited discrimination across classes, whereas deep learning methods--including LSTM [11] and CNN-LSTM [7]--offer high accuracy but require extensive computational resources, making them impractical for edge IIoT devices. Additionally, previous works, such as that of [PERSON] et al. [12], demonstrated strong recall via autoencoder-XGBoost integration but lacked precision due to imbalance and noise sensitivity. These findings directly motivate our design of ROSE-BOX: a lightweight and efficient multi-class intrusion detection framework specifically designed for resource-constrained IIoT environments. We propose an efficient hybrid feature selector (XGB-RF), SMOTE-based data balancing, and Bayesian-optimized XGBoost to overcome these specific deficiencies. The low computational demand of ROSE-BOX makes it ideally suited for deployment across a wide array of devices, from industrial controllers to edge computing nodes and embedded systems. ROSE-BOX is designed to scale across diverse IIoT platforms by leveraging three principles: (1) early feature reduction via XGBoost-assisted Random Forest to minimize input size, (2) data balancing with SMOTE on a compact subset to prevent unnecessary memory usage and improve detection performance, and (3) the parameters of ROSE-BOX are tuned via offline Bayesian optimization during training to ensure low-latency inference. These steps collectively enable scalable deployment across edge, embedded, and industrial systems. Our findings establish ROSE-BOX as a scalable, lightweight, and real-time IDS solution, bridging the gap between detection performance and practical deployment in resource-limited IIoT applications. The contributions of our work are listed as follows:  
* We propose a hybrid feature selection framework that synergistically combines Random Forest and XGBoost algorithms to identify and rank the most salient features from high-dimensional IIoT datasets. By eliminating redundant and non-informative features, this approach significantly reduces computational overhead and enhances detection accuracy, making it highly suitable for deployment in resource-constrained IIoT environments. * We incorporate the Synthetic Minority Oversampling Technique (SMOTE) [13] to address the class imbalance inherent in intrusion detection datasets and Bayesian optimization (BO) [9] to fine-tune the hyperparameters of XGBoost, further optimizing detection performance while minimizing computational demands. * The experimental results indicate that the proposed ROSE-BOX framework [14] consistently achieves detection accuracies exceeding 99.85%, markedly surpassing traditional intrusion detection systems. Moreover, ROSE-BOX substantially reduces latency and computational overhead, rendering it highly suitable for real-time deployment in resource-constrained Industrial Internet of Things (IIoT) environments. The novelty of the ROSE-BOX framework lies in its synergistic integration of XGBoost-assisted Random Forest for efficient and relevant feature extraction, SMOTE for handling class imbalance, and Bayesian optimization for real-time parameter tuning, all tailored for resource-constrained IIoT and smart home environments. This integrated approach allows ROSE-BOX not only to detect intrusions with extremely high accuracy but also to minimize computational and energy overhead--a critical feature for smart home and smart grid systems. Moreover, by facilitating local detection without reliance on cloud-based computation [15], ROSE-BOX inherently enhances privacy protection and reduces latency--two central concerns in edge-based smart home security, agricultural robot networks, and cyber ranges [16; 17; 18]. The rest of this paper is designed as follows. Section 2 introduces the related studies on intrusion detection in IIoT. Section 3 proposes the detection model ROSE-BOX. Section 4 introduces the experimental results and analyzes the detection performance of the proposed model.